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General General

Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.

In Frontiers in endocrinology ; h5-index 55.0

Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3β, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC50 value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.

Pirzada Rameez Hassan, Ahmad Bilal, Qayyum Naila, Choi Sangdun

2023

GSK3, QSAR, coronaviruses, machine learning, molecular descriptors

General General

Deep Survival Analysis With Clinical Variables for COVID-19.

In IEEE journal of translational engineering in health and medicine

OBJECTIVE : Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients.

METHODS AND PROCEDURES : We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups.

RESULTS : Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19.

CONCLUSION : Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner.

CLINICAL IMPACT : The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

Chaddad Ahmad, Hassan Lama, Katib Yousef, Bouridane Ahmed

2023

CNN, COVID-19, clinical variables

Public Health Public Health

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19.

In Frontiers in public health

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

Basit Syed Abdullah, Qureshi Rizwan, Musleh Saleh, Guler Reto, Rahman M Sohel, Biswas Kabir H, Alam Tanvir

2023

CORD-19, COVID-19, SARS-CoV-2, deep learning, machine learning

Public Health Public Health

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.

In Open forum infectious diseases

BACKGROUND : The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza.

METHODS : Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network).

RESULTS : We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection.

CONCLUSIONS : This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Luciani Lauren L, Miller Leigh M, Zhai Bo, Clarke Karen, Hughes Kramer Kailey, Schratz Lucas J, Balasubramani G K, Dauer Klancie, Nowalk M Patricia, Zimmerman Richard K, Shoemaker Jason E, Alcorn John F

2023-Mar

SARS-CoV-2, cytokine, human, machine learning, pneumonia

General General

SARS-CoV-2 related adaptation mechanisms of rehabilitation clinics affecting patient-centred care: Qualitative study of online patient reports.

In JMIR rehabilitation and assistive technologies

BACKGROUND : The SARS-CoV-2 pandemic impacted the access to inpatient rehabilitation services. At the current state of research, it is unclear to what extent the adaptation of rehabilitation services to infection-protective standards affected patient-centred care in Germany.

OBJECTIVE : This study aimed to explore which aspects of patient-centred care were relevant for patients in inpatient rehabilitation clinics under early-phase pandemic conditions.

METHODS : A deductive-inductive framework analysis of online patient reports posted on a leading German hospital rating website was conducted (www.klinikbewertungen.de). The selected hospital rating website is a third party, patient-centred commercial platform which operates independently of governmental entities. Following a theoretical sampling approach, online reports of rehabilitation stays in two federal states of Germany (Brandenburg, Saarland) uploaded between March 2020 and September 2021 were included. Independently of medical specialty groups, all reports were included. Keywords addressing framework domains were analysed descriptively.

RESULTS : In total, 649 online reports reflecting inpatient rehabilitation services of 31 clinics (Brandenburg N = 23; Saarland N = 8) were analysed. Keywords addressing the care environment were most frequently reported (59.9%) followed by staff prerequisites (33.0%), patient-centred processes (4.5%) and expected outcomes (2.6%). Qualitative in depth-analysis revealed SARS-CoV-2 related reports to be associated with domains of patient-centred processes and staff prerequisites. Discontinuous communication of infection protection standards was perceived to threaten patient autonomy. This was amplified by a tangible gratification crisis of medical staff. Established and emotional supportive relationships to clinicians and peer-groups offered the potential to mitigate adverse effects of infection protection standards.

CONCLUSIONS : Patients predominantly reported feedback associated with the care environment. SARS-CoV-2 related reports were strongly affected by increased staff workloads as well as patient-centred processes addressing discontinuous communication and organizationally demanding implementation of infection protection standards which were perceived to threaten patient autonomy. Peer-relationships formed during inpatient rehabilitation had the potential to mitigate these mechanisms.

CLINICALTRIAL : Not applicable.

Kühn Lukas, Lindert Lara, Kuper Paulina, Choi Kyung-Eun Anna

2023-Mar-05

Public Health Public Health

Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning.

In JMIR formative research

BACKGROUND : Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multi-dimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome" which is associated with health anxiety, problematic internet use and obsessive-compulsive symptoms. Cyberchondria is not included in the ICD-11 or the DSM-5, and its defining features, etiological mechanisms and assessment continue to be debated.

OBJECTIVE : This study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time.

METHODS : Data collection started on May 4, 2020 and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the present study took place, French-speaking countries in Europe (France, Switzerland, Belgium and Luxembourg) all implemented lockdown or semi-lockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level and country of residence) and information on socioeconomic circumstances during the first lockdown (e.g., economic situation, housing and employment status), and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18 to 77 years (mean 33.29, SD 12.88 years), with females constituting the majority (416/725, 57.4%).

RESULTS : The results show that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). Also, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic.

CONCLUSIONS : These findings provide evidence about the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. Also, they are consistent with the theoretical model of cyberchondria during the COVID-19 pandemic proposed by Starcevic and his colleagues in 2020. In addition, the findings have implications for the conceptualization and future assessment of cyberchondria.

Infanti Alexandre, Starcevic Vladan, Schimmenti Adriano, Khazaal Yasser, Karila Laurent, Giardina Alessandro, Flayelle Maèva, Hedayatzadeh Razavi Seyedeh Boshra, Baggio Stéphanie, Vögele Claus, Billieux Joël

2023-Mar-09

General General

Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis.

In JMIR medical education

BACKGROUND : Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place.

OBJECTIVE : Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice.

METHODS : We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t0 (February 2020, before the COVID-19 pandemic) and 138 responses at t1 (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data.

RESULTS : Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry).

CONCLUSIONS : Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.

Wagner Gerit, Raymond Louis, Paré Guy

2023-Mar-22

artificial intelligence, attitudes and beliefs, behavioral intentions, fsQCA, fuzzy-set qualitative comparative analysis, knowledge and experience, medical education

General General

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

In Research square

Background Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1 st , 2020, and November 30 th , 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Zang Chengxi, Hou Yu, Schenck Edward, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Nordvig Anna, Shenkman Elizabeth, Rothman Russel, Block Jason, Lyman Kristin, Zhang Yiye, Varma Jay, Weiner Mark, Carton Thomas, Wang Fei, Kaushal Rainu, Consortium The Recover

2023-Mar-08

General General

A ventilation early warning system (VEWS) for diaphanous workspaces considering COVID-19 and future pandemics scenarios.

In Heliyon

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.

Costa Gonçal, Arroyo Oriol, Rueda Pablo, Briones Alan

2023-Mar

BIM, Building digital twin, COVID-19, Facilities management, IoT, Simulation, Smart building

General General

COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm.

In Results in engineering

Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.

Poola Rahul Gowtham, Pl Lahari, Y Siva Sankar

2023-Jun

Boundary tracing, Covid diagnosis, Deep transfer-learning, Medical imaging, Neural network models and classifiers

General General

Human Behavior in the Time of COVID-19: Learning from Big Data

ArXiv Preprint

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups - using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, Jiebo Luo

2023-03-23

Radiology Radiology

Impact of inactivated COVID-19 vaccines on lung injury in B.1.617.2 (Delta) variant-infected patients.

In Annals of clinical microbiology and antimicrobials

BACKGROUND : Chest computerized tomography (CT) scan is an important strategy that quantifies the severity of COVID-19 pneumonia. To what extent inactivated COVID-19 vaccines could impact the COVID-19 pneumonia on chest CT is not clear.

METHODS : This study recruited 357 SARS-COV-2 B.1.617.2 (Delta) variant-infected patients admitted to the Second Hospital of Nanjing from July to August 2021. An artificial intelligence-assisted CT imaging system was used to quantify the severity of COVID-19 pneumonia. We compared the volume of infection (VOI), percentage of infection (POI) and chest CT scores among patients with different vaccination statuses.

RESULTS : Of the 357 Delta variant-infected patients included for analysis, 105 were unvaccinated, 72 were partially vaccinated and 180 were fully vaccinated. Fully vaccination had the least lung injuries when quantified by VOI (median VOI of 222.4 cm3, 126.6 cm3 and 39.9 cm3 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001), POI (median POI of 7.60%, 3.55% and 1.20% in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001) and chest CT scores (median CT score of 8.00, 6.00 and 4.00 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001). After adjustment for age, sex, comorbidity, time from illness onset to hospitalization and viral load, fully vaccination but not partial vaccination was significantly associated with less lung injuries quantified by VOI {adjust coefficient[95%CI] for "full vaccination": - 106.10(- 167.30,44.89); p < 0.001}, POI {adjust coefficient[95%CI] for "full vaccination": - 3.88(- 5.96, - 1.79); p = 0.001} and chest CT scores {adjust coefficient[95%CI] for "full vaccination": - 1.81(- 2.72, - 0.91); p < 0.001}. The extent of reduction of pulmonary injuries was more profound in fully vaccinated patients with older age, having underlying diseases, and being female sex, as demonstrated by relatively larger absolute values of adjusted coefficients. Finally, even within the non-severe COVID-19 population, fully vaccinated patients were found to have less lung injuries.

CONCLUSION : Fully vaccination but not partially vaccination could significantly protect lung injury manifested on chest CT. Our study provides additional evidence to encourage a full course of vaccination.

Lai Miao, Wang Kai, Ding Chengyuan, Yin Yi, Lin Xiaoling, Xu Chuanjun, Hu Zhiliang, Peng Zhihang

2023-Mar-21

Artificial intelligence (AI), COVID-19, COVID-19 vaccines, Chest CT, Lung injury

General General

Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation

ArXiv Preprint

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each classification. In addition, ANN-Flux reduces the remaining useful life RMSE by 38% for the same test split of the dataset compared to past work, with significantly less computational cost.

Joseph Cohen, Xun Huan, Jun Ni

2023-03-23

General General

Using artificial intelligence to support rapid, mixed-methods analysis: Developing an automated qualitative assistant (AQUA).

In Annals of family medicine

Context: Qualitative research - crucial for understanding human behavior - remains underutilized, in part due to the time and cost of annotating qualitative data (coding). Artificial intelligence (AI) has been suggested as a means to reduce those burdens. Older AI techniques (Latent Semantic Indexing / Latent Dirichlet Allocation (LSI/LDA)) have fallen short, in part because qualitative data is rife with idiom, non-standard expressions, and jargon. Objective: To develop an AI platform using updated techniques to augment qualitative data coding. Study Design and Analysis: We previously completed traditional qualitative analysis of a large dataset, with 11 qualitative categories and 72 subcategories (categories), and a final Cohen's kappa ≥ 0.65 as a measure of human inter-coder reliability (ICR) after coding. We built our Automated Qualitative Assistant (AQUA) using a semi-classical approach, replacing LSI/LDA with a graph-theoretic topic extraction and clustering method. AQUA was given the previously-identified qualitative categories and tasked with coding free-text data into those categories. Item coding was scored using cosine-similarity. Population Studied: Pennsylvanian adults. Instrument: Free-text responses to five open ended questions related to the COVID-19 pandemic (e.g. "What worries you most about the COVID-19 pandemic?"). Outcome Measures: AQUA's coding was compared to human coding using Cohen's kappa. This was done on all categories in aggregate, and also on category clusters to identify category groups amenable to AQUA support. AQUA's time to complete coding was compared to the time taken by the human coding team. Dataset: Five unlimited free-text survey answers from 538 responders. Results: AQUA's kappa for all categories was low (kappa~0.45), reflecting the challenge of automated analysis of diverse language. However, for several 3-category combinations (with less linguistic diversity), AQUA performed comparably to human coders, with an ICR kappa range of 0.62 to 0.72 based on test-train split. AQUA's analysis (including human interpretation) took approximately 5 hours, compared to approximately 30 person hours for traditional coding. Conclusions: AQUA enables qualitative researchers to identify categories amenable to automated coding, and to rapidly conduct that coding on the entirety of very large datasets. This saves time and money, and avoids limitations inherent in limiting qualitative analysis to limited samples of a given dataset.

Lennon Robert, Calo William, Miller Erin, Zgierska Aleksandra, Van Scoy Lauren, Fraleigh Robert

2022-Apr-01

General General

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (Mpro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Charles Ssemuyiga, Edgar Mulumba Pius, Mahapatra Rajani Kanta

2023-Mar-21

Artificial intelligence, COVID-19, SARS-CoV-2 main protease, deep docking, molecular docking, molecular dynamics simulation, neural networks

General General

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

In Journal of health communication ; h5-index 36.0

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.

Ye Linglong, Chen Yang, Cai Yongming, Kao Yi-Wei, Wang Yuanxin, Chen Mingchih, Shia Ben-Chang, Qin Lei

2023-Mar-21

General General

Relationship Between Coronavirus Disease 2019 Vaccination Rates and Rare But Potentially Fatal Adverse Events: A Regression Discontinuity Analysis of Western Countries.

In Journal of Korean medical science

BACKGROUND : Owing to limited experience with the new vaccine platforms, discussion of vaccine safety is inevitable. However, media coverage of adverse events of special interest could influence the vaccination rate; thus, evaluating the outcomes of adverse events of special interest influencing vaccine administration is crucial.

METHODS : We conducted regression discontinuity in time analysis to calculate the local average treatment effect (LATE) using datasets from Our World in Data and Johns Hopkins University Center for Systems Science and Engineering. For the United States, the United Kingdom, and Europe, the cutoff points were April 23rd and June 23rd, April 7th, and the 14th week of 2021, respectively.

RESULTS : The LATE of the Advisory Committee on Immunization Practices (ACIP) meeting held on April 23rd was -0.249 for all vaccines, -0.133 (-0.189 to -0.076) for Pfizer, -0.064 (-0.115 to -0.012) for Moderna, and -0.038 (-0.047 to -0.030) for Johnson &amp; Johnson. Discontinuities were observed for all three types of vaccines in the United States. The June 23rd meeting of the ACIP (mRNA vaccines and myocarditis) did not convene any discontinuities. Furthermore, there was no significant drop in the weekly average vaccination rates in Europe following the European Medicines Agency (EMA) statement on April 7th. Conversely, there was a significant drop in the first-dose vaccination rates in the United Kingdom related to the EMA report. The first-dose vaccination rate for all vaccines changed by -0.104 (-0.176 to -0.032).

CONCLUSION : Although monitoring and reporting of adverse events of special interest are important, a careful approach towards public announcements is warranted.

Chae Seung Hoon, Park Hyung Jun, Radnaabaatar Munkhzul, Park Hojun, Jung Jaehun

2023-Mar-20

COVID-19, Regression Discontinuity Analysis, SARS-CoV-2

Public Health Public Health

Acceptability and Effectiveness of COVID-19 Contact Tracing Applications: A Case Study in Saudi Arabia of the Tawakkalna Application.

In Cureus

Background Contact tracing applications were introduced during the COVID-19 pandemic to mitigate the spread of the infection in several countries. In Saudi Arabia, the Tawakkalna application was developed. The Tawakkalna application is a mobile health solution aimed to track infection cases, save lives, and reduce the burden on health facilities. This study aims to explore the public's attitude to and acceptance levels of the Tawakkalna application and to evaluate its effectiveness regarding privacy and security. The main objective of this study is to investigate the user acceptability of contact tracing applications and explore the safety and privacy effectiveness of the COVID-19 contact tracing application, the Tawakkalna application. In addition, the study analyzes factors associated with acceptance levels and compares the results obtained to similar studies in other countries using similar applications. Methodology This study used a valid and reliable online survey that was used in similar studies conducted in other countries to assess the acceptability of the application. The survey was conducted from September to November 2021, and the final dataset included 205 participants. To investigate the privacy and security performance of the Tawakkalna application, we followed the investigation method used by similar research that investigated 28 contact tracing applications across Europe. Results Out of the 205 participants, 84.87% were in favor of the opt-in voluntary installation of the Tawakkalna application, and 49.75% of the participants were in favor of the opt-out automatic installation. Individuals' trust in the government had a huge impact on acceptance, with 60.98% of the participants supporting the application because they believed that the Tawakkalna application would help them stay healthy during the COVID-19 pandemic. Overall, 49% of the participants supporting the application also agreed to the de-identification of their collected data and providing it for research. The Tawakkalna application ranked at the top compared to other contact tracing applications regarding privacy and security. Conclusions The Tawakkalna application developed by the Saudi Data and Artificial Intelligence Authority was a response to the COVID-19 pandemic, which is considered the biggest public health crisis in recent times. The Saudi Arabian government gained the population's acceptance through effective endorsement and the spread of educational content through media channels. By complying with privacy policies, the Tawakkalna application is an effective tool to combat public health infectious diseases.

Dawood Safia, AlKadi Khulud

2023-Feb

acceptability, contact tracing, covid-19, mhealth, permission, privacy, privilege, security, tawakkalna

Public Health Public Health

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects.

In Computers in human behavior ; h5-index 125.0

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

Kwon Soyeon, Park Albert

2023-Jul

Consumer health information, Schema theory, Social media, Social network analysis, Unsupervised machine learning

General General

CBCovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

In Data in brief

In this work, we present the complete blood count data and PCR test results of a population of Ecuadorians from different provinces, primarily residing in the Andean region, especially in Quito. PCR was the standard test to detect Covid-19 during the pandemic since 2020. The data were obtained between March 1st and August 12th, 2021. Segurilab and Previne Salud laboratories performed the tests. The dataset contains about 400 clinical cases. Each patient agreed to participate in the study by sharing the results of their PCR (reverse transcription polymerase chain reaction) tests and CBC (complete blood count). CBC test measured several components and features of the blood, including red blood cells, white blood cells, hemoglobin, hematocrit, and platelets. The shared data are intended to provide researchers with input to analyze various events associated with the diagnosis of Covid-19 linked to potential diseases identified in the components measured in the CBC test. These data are helpful for pattern analysis of blood components in modeling prediction and clustering problems. The components measured in the complete blood count and CRP together can be helpful for the analysis of different medical conditions using machine learning algorithms.

Ordoñez-Avila R, Parraga-Alava J, Hormaza J Meza, Vaca-Cárdenas L, Portmann E, Terán L, Dorn M

2023-Apr

Ecuador, Hematological data, Machine learning, SARS-Cov-2

Public Health Public Health

Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland.

In JMIR medical education

BACKGROUND : The potential for digital health technologies, including machine learning (ML)-enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics.

OBJECTIVE : We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks.

METHODS : Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses.

RESULTS : In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs.

CONCLUSIONS : We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow's doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow's patients.

Blease Charlotte, Kharko Anna, Bernstein Michael, Bradley Colin, Houston Muiris, Walsh Ian, D Mandl Kenneth

2023-Mar-20

COVID-19, artificial intelligence, biomedical, design, digital health, general practitioners, machine learning, medical education, medical professional, medical students, survey, technology, tool

General General

COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.

In Network modeling and analysis in health informatics and bioinformatics

X-ray is a useful imaging modality widely utilized for diagnosing COVID-19 virus that infected a high number of people all around the world. The manual examination of these X-ray images may cause problems especially when there is lack of medical staff. Usage of deep learning models is known to be helpful for automated diagnosis of COVID-19 from the X-ray images. However, the widely used convolutional neural network architectures typically have many layers causing them to be computationally expensive. To address these problems, this study aims to design a lightweight differential diagnosis model based on convolutional neural networks. The proposed model is designed to classify the X-ray images belonging to one of the four classes that are Healthy, COVID-19, viral pneumonia, and bacterial pneumonia. To evaluate the model performance, accuracy, precision, recall, and F1-Score were calculated. The performance of the proposed model was compared with those obtained by applying transfer learning to the widely used convolutional neural network models. The results showed that the proposed model with low number of computational layers outperforms the pre-trained benchmark models, achieving an accuracy value of 89.89% while the best pre-trained model (Efficient-Net B2) achieved accuracy of 85.7%. In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy.

Hariri Muhab, Avşar Ercan

2023

COVID-19, Classification, Convolutional neural networks, Deep learning, Lung diseases, Transfer learning

General General

Precision recruitment for high-risk participants in a COVID-19 cohort study.

In Contemporary clinical trials communications

BACKGROUND : Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals.

METHODS : We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials.

RESULTS : When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts.

CONCLUSION : This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

Mezlini Aziz M, Caddigan Eamon, Shapiro Allison, Ramirez Ernesto, Kondow-McConaghy Helena M, Yang Justin, DeMarco Kerry, Naraghi-Arani Pejman, Foschini Luca

2023-Jun

CDC, Centers for Disease Control and Prevention, COVID-19, Clinical trials, GAMs, generalized additive models, Risk modeling

Radiology Radiology

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

In Frontiers in cellular and infection microbiology ; h5-index 53.0

BACKGROUND : There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.

METHODS : A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases.

RESULTS : The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists.

CONCLUSIONS : This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

Zhang Hong-Tao, Sun Ze-Yu, Zhou Juan, Gao Shen, Dong Jing-Hui, Liu Yuan, Bai Xu, Ma Jin-Lin, Li Ming, Li Guang, Cai Jian-Ming, Sheng Fu-Geng

2023

artificial intelligence, computed tomography (CT), coronavirus disease 2019 (COVID-19), deep learning, global average pooling (GAP)

General General

Applying Blockchain Technology in Network Public Opinion Risk Management System in Big Data Environment.

In Computational intelligence and neuroscience

Network public opinion represents public social opinion to a certain extent and has an important impact on formulating national policies and judgment. Therefore, China and other countries attach great importance to the study of online public opinion. However, the current researches lack the combination of theory and practical cases and lack the intersection of social and natural sciences. This work aims to overcome the technical defects of traditional management systems, break through the difficulties and pain points of existing network public opinion risk management, and improve the efficiency of network public opinion risk management. Firstly, a network public opinion isolation strategy based on the infectious disease propagation model is proposed, and the optimal control theory is used to realize a functional control model to maximize social utility. Secondly, blockchain technology is used to build a network public opinion risk management system. The system is used to conduct a detailed study on identifying and perceiving online public opinion risk. Finally, a Chinese word segmentation scheme based on Long Short-Term Memory (LSTM) network model and a text emotion recognition scheme based on a convolutional neural network are proposed. Both schemes are validated on a typical corpus. The results show that when the system has a control strategy, the number of susceptible drops significantly. Two days after the public opinion is generated, the number of susceptible people decreased from 1,000 to 250; 3 days after the public opinion is generated, the number of susceptible people stabilized. 2 days after the public opinion is generated, the number of lurkers increased from 100 to 620; 3 days after the public opinion is generated, the number of lurkers stabilized. The data demonstrate that the designed isolation control strategy is effective. Changes in public opinion among infected people show that quarantine control strategies played a significant role in the early days of Corona Virus Disease 2019. The rate of change in the number of infections is more affected when quarantine controls are increased, especially in the days leading up to the outbreak. When the system adopts the optimal control strategy, the influence scope of public opinion becomes smaller, and the control becomes easier. When the dimension of the word vector of emergent events is 200, its accuracy may be higher. This method provides certain ideas for blockchain and deep learning technology in network public opinion control.

Luo Zhenqing, Zhang Cheng

2023

General General

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease.

In Cell reports methods

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Zhang Zijun, Sauerwald Natalie, Cappuccio Antonio, Ramos Irene, Nair Venugopalan D, Nudelman German, Zaslavsky Elena, Ge Yongchao, Gaitas Angelo, Ren Hui, Brockman Joel, Geis Jennifer, Ramalingam Naveen, King David, McClain Micah T, Woods Christopher W, Henao Ricardo, Burke Thomas W, Tsalik Ephraim L, Goforth Carl W, Lizewski Rhonda A, Lizewski Stephen E, Weir Dawn L, Letizia Andrew G, Sealfon Stuart C, Troyanskaya Olga G

2023-Feb-27

RNA splicing, SARS-CoV-2, diagnostic biomarker, host response assays, infectious disease, viral infection

Public Health Public Health

An AI-enabled research support tool for the classification system of COVID-19.

In Frontiers in public health

The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM).

Tiwari Arti, Bhattacharjee Kamanasish, Pant Millie, Srivastava Shilpa, Snasel Vaclav

2023

Artificial Intelligence, COVID-19, bi-directional LSTM, classification, long short-term memory

Public Health Public Health

Mechanisms influencing the factors of urban built environments and coronavirus disease 2019 at macroscopic and microscopic scales: The role of cities.

In Frontiers in public health

In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept the world, exerting a tremendous impact on lifestyles. This study investigated changes in the infection rates of COVID-19 and the urban built environment in 45 areas in Manhattan, New York, and the relationship between the factors of the urban built environment and COVID-19. COVID-19 was used as the outcome variable, which represents the situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze the macroscopic (macro) and microscopic (micro) factors of the urban built environment. Computer vision was introduced to quantify the material space of urban places from street-level panoramic images of the urban streetscape. The study then extracted the microscopic factors of the urban built environment. The micro factors were composed of two parts. The first was the urban level, which was composed of urban buildings, Panoramic View Green View Index, roads, the sky, and buildings (walls). The second was the streets' green structure, which consisted of macrophanerophyte, bush, and grass. The macro factors comprised population density, traffic, and points of interest. This study analyzed correlations from multiple levels using linear regression models. It also effectively explored the relationship between the urban built environment and COVID-19 transmission and the mechanism of its influence from multiple perspectives.

Zhang Longhao, Han Xin, Wu Jun, Wang Lei

2023

COVID-19, computer vision, deep learning, relevance, street view images, urban built environment

Public Health Public Health

PCR-like performance of rapid test with permselective tunable nanotrap.

In Nature communications ; h5-index 260.0

Highly sensitive rapid testing for COVID-19 is essential for minimizing virus transmission, especially before the onset of symptoms and in asymptomatic cases. Here, we report bioengineered enrichment tools for lateral flow assays (LFAs) with enhanced sensitivity and specificity (BEETLES2), achieving enrichment of SARS-CoV-2 viruses, nucleocapsid (N) proteins and immunoglobulin G (IgG) with 3-minute operation. The limit of detection is improved up to 20-fold. We apply this method to clinical samples, including 83% with either intermediate (35%) or low viral loads (48%), collected from 62 individuals (n = 42 for positive and n = 20 for healthy controls). We observe diagnostic sensitivity, specificity, and accuracy of 88.1%, 100%, and 91.9%, respectively, compared with commercial LFAs alone achieving 14.29%, 100%, and 41.94%, respectively. BEETLES2, with permselectivity and tunability, can enrich the SARS-CoV-2 virus, N proteins, and IgG in the nasopharyngeal/oropharyngeal swab, saliva, and blood serum, enabling reliable and sensitive point-of-care testing, facilitating fast early diagnosis.

Park Seong Jun, Lee Seungmin, Lee Dongtak, Lee Na Eun, Park Jeong Soo, Hong Ji Hye, Jang Jae Won, Kim Hyunji, Roh Seokbeom, Lee Gyudo, Lee Dongho, Cho Sung-Yeon, Park Chulmin, Lee Dong-Gun, Lee Raeseok, Nho Dukhee, Yoon Dae Sung, Yoo Yong Kyoung, Lee Jeong Hoon

2023-Mar-18

General General

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (Mpro) using molecular docking and deep learning methods.

In Computers in biology and medicine

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (Mpro) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of Mpro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus Mpro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from -8.0 to -8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against Mpro. Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients.

Hossain Alomgir, Rahman Md Ekhtiar, Rahman Md Siddiqur, Nasirujjaman Khondokar, Matin Mohammad Nurul, Faruqe Md Omar, Rabbee Muhammad Fazle

2023-Mar-11

Catechin gallate, Deep learning, Main protease, Molecular docking, SARS-CoV-2

General General

Longitudinal proteomic investigation of COVID-19 vaccination.

In Protein & cell

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac®) at a 28-day interval. Using TMT-based proteomics, we identified 1,715 serum and 7,342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) at baseline using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers before vaccination for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.

Wang Yingrui, Zhu Qianru, Sun Rui, Yi Xiao, Huang Lingling, Hu Yifan, Ge Weigang, Gao Huanhuan, Ye Xinfu, Song Yu, Shao Li, Li Yantao, Li Jie, Guo Tiannan, Shi Junping

2023-Feb-06

COVID-19, machine learning, neutralizing antibodies (NAbs), proteomics, vaccination

Internal Medicine Internal Medicine

Artificial intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparison with CT scans.

In PloS one ; h5-index 176.0

BACKGROUND : Although lung ultrasound has been reported to be a portable, cost-effective, and accurate method to detect pneumonia, it has not been widely used because of the difficulty in its interpretation. Here, we aimed to investigate the effectiveness of a novel artificial intelligence-based automated pneumonia detection method using point-of-care lung ultrasound (AI-POCUS) for the coronavirus disease 2019 (COVID-19).

METHODS : We enrolled consecutive patients admitted with COVID-19 who underwent computed tomography (CT) in August and September 2021. A 12-zone AI-POCUS was performed by a novice observer using a pocket-size device within 24 h of the CT scan. Fifteen control subjects were also scanned. Additionally, the accuracy of the simplified 8-zone scan excluding the dorsal chest, was assessed. More than three B-lines detected in one lung zone were considered zone-level positive, and the presence of positive AI-POCUS in any lung zone was considered patient-level positive. The sample size calculation was not performed given the retrospective all-comer nature of the study.

RESULTS : A total of 577 lung zones from 56 subjects (59.4 ± 14.8 years, 23% female) were evaluated using AI-POCUS. The mean number of days from disease onset was 9, and 14% of patients were under mechanical ventilation. The CT-validated pneumonia was seen in 71.4% of patients at total 577 lung zones (53.3%). The 12-zone AI-POCUS for detecting CT-validated pneumonia in the patient-level showed the accuracy of 94.5% (85.1%- 98.1%), sensitivity of 92.3% (79.7%- 97.3%), specificity of 100% (80.6%- 100%), positive predictive value of 95.0% (89.6% - 97.7%), and Kappa of 0.33 (0.27-0.40). When simplified with 8-zone scan, the accuracy, sensitivity, and sensitivity were 83.9% (72.2%- 91.3%), 77.5% (62.5%- 87.7%), and 100% (80.6%- 100%), respectively. The zone-level accuracy, sensitivity, and specificity of AI-POCUS were 65.3% (61.4%- 69.1%), 37.2% (32.0%- 42.7%), and 97.8% (95.2%- 99.0%), respectively.

INTERPRETATION : AI-POCUS using the novel pocket-size ultrasound system showed excellent agreement with CT-validated COVID-19 pneumonia, even when used by a novice observer.

Kuroda Yumi, Kaneko Tomohiro, Yoshikawa Hitomi, Uchiyama Saori, Nagata Yuichi, Matsushita Yasushi, Hiki Makoto, Minamino Tohru, Takahashi Kazuhisa, Daida Hiroyuki, Kagiyama Nobuyuki

2023

General General

Artificial intelligence-based optimization for chitosan nanoparticles biosynthesis, characterization and in‑vitro assessment of its anti-biofilm potentiality.

In Scientific reports ; h5-index 158.0

Chitosan nanoparticles (CNPs) are promising biopolymeric nanoparticles with excellent physicochemical, antimicrobial, and biological properties. CNPs have a wide range of applications due to their unique characteristics, including plant growth promotion and protection, drug delivery, antimicrobials, and encapsulation. The current study describes an alternative, biologically-based strategy for CNPs biosynthesis using Olea europaea leaves extract. Face centered central composite design (FCCCD), with 50 experiments was used for optimization of CNPs biosynthesis. The artificial neural network (ANN) was employed for analyzing, validating, and predicting CNPs biosynthesis using Olea europaea leaves extract. Using the desirability function, the optimum conditions for maximum CNPs biosynthesis were determined theoretically and verified experimentally. The highest experimental yield of CNPs (21.15 mg CNPs/mL) was obtained using chitosan solution of 1%, leaves extract solution of 100%, initial pH 4.47, and incubation time of 60 min at 53.83°C. The SEM and TEM images revealed that CNPs had a spherical form and varied in size between 6.91 and 11.14 nm. X-ray diffraction demonstrates the crystalline nature of CNPs. The surface of the CNPs is positively charged, having a Zeta potential of 33.1 mV. FTIR analysis revealed various functional groups including C-H, C-O, CONH2, NH2, C-OH and C-O-C. The thermogravimetric investigation indicated that CNPs are thermally stable. The CNPs were able to suppress biofilm formation by P. aeruginosa, S. aureus and C. albicans at concentrations ranging from 10 to 1500 µg/mL in a dose-dependent manner. Inhibition of biofilm formation was associated with suppression of metabolic activity, protein/exopolysaccharide moieties, and hydrophobicity of biofilm encased cells (r ˃ 0.9, P = 0.00). Due to their small size, in the range of 6.91 to 11.14 nm, CNPs produced using Olea europaea leaves extract are promising for applications in the medical and pharmaceutical industries, in addition to their potential application in controlling multidrug-resistant microorganisms, especially those associated with post COVID-19 pneumonia in immunosuppressed patients.

El-Naggar Noura El-Ahmady, Dalal Shimaa R, Zweil Amal M, Eltarahony Marwa

2023-Mar-16

General General

OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans.

In Big data

Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Therefore, the classification of computed tomography (CT) scans alleviates the workload of experts, whose workload increased considerably during the pandemic. Convolutional neural network (CNN) architectures are successful for the classification of medical images. In this study, we have developed a new deep CNN architecture called OzNet. Moreover, we have compared it with pretrained architectures namely AlexNet, DenseNet201, GoogleNet, NASNetMobile, ResNet-50, SqueezeNet, and VGG-16. In addition, we have compared the classification success of three preprocessing methods with raw CT scans. We have not only classified the raw CT scans, but also have performed the classification with three different preprocessing methods, which are discrete wavelet transform (DWT), intensity adjustment, and gray to color red, green, blue image conversion on the data sets. Furthermore, it is known that the architecture's performance increases with the use of DWT preprocessing method rather than using the raw data set. The results are extremely promising with the CNN algorithms using the COVID-19 CT scans processed with the DWT. The proposed DWT-OzNet has achieved a high classification performance of more than 98.8% for each calculated metric.

Ozaltin Oznur, Yeniay Ozgur, Subasi Abdulhamit

2023-Mar-16

2D-DWT, CNN, COVID-19 CT scans, classification, intensity adjustment

General General

Pharmacists and pharmacy services in COVID-19 literature: A bibliometirc analysis.

In Exploratory research in clinical and social pharmacy

BACKGROUND : The COVID-19 pandemic had an enormous impact on the global economy and healthcare. Pharmacists were vital members of the healthcare system, and they participated in various strategies to reduce the effect of the pandemic. Numerous papers were published discussing their roles during the pandemic. Bibliometric analysis was used to measure the impact of publications on this topic and assessed them qualitatively and quantitatively over a specific time.

OBJECTIVE : Evaluate published literature pertaining to the roles of pharmacists and pharmacy services during the pandemic and identify gaps.

METHODS : An electronic search was conducted on PubMed database using a specific query. Eligible publications were published in English between January 2020 and January 2022 and discussed the role of pharmacists, pharmacies, and pharmacy departments during the pandemic. Clinical trials, studies on pharmacy education/training, and conference abstracts were excluded.

RESULTS : Of 954 records retrieved, 338 (35.4%) from 67 countries were included. Most papers (n = 113; 33.4%) were from the community pharmacy sector, followed by the clinical pharmacy sector (n = 89; 26.3%). Sixty-one (18%) papers were multinational, mostly involving two countries. The average number of citations of the included papers was 6 times (range 0-89). The most common MeSH terms were 'humans', 'hospitals', and 'telemedicine', where the former frequently co-appeared with the terms 'COVID-19' and 'pharmacists.'

CONCLUSIONS : Results from this study illustrate the innovative and proactive strategies developed by pharmacists during the pandemic. Pharmacists from around the world are encouraged to share their experiences for stronger healthcare systems to counter future pandemics and environmental disasters.

Thabit Abrar K, Alsulmi Wajd S, Aljereb Nourah M, Khojah Omnia M, Almehdar Khadeja O, Cobo Manuel Jesús, Jose Jimmy, Vélez-Estévez Antonio

2023-Mar

Bibliometric analysis, COVID-19, COVID-19, Coronavirus disease of 2019, DOI, Digital object identifier, MeSH, Medical subject heading, Pharmacists, Pharmacy, SciMAT, Science Mapping Analysis Software Tool, WHO, World Health Organization

General General

The softening of Chinese digital propaganda: Evidence from the People's Daily Weibo account during the pandemic.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : Social media infuses modern relationships with vitality and brings a series of information dissemination with subjective consciousness. Studies have indicated that official Chinese media channels are transforming their communication style from didactic hard persuasion to softened emotional management in the digital era. However, previous studies have rarely provided valid empirical evidence for the communicational transformation. The study fills the gap by providing a longitudinal time-series analysis to reveal the pattern of communication of Chinese digital Chinese official media from 2019 to 2022.

METHOD : The study crawler collected 43,259 posts from the People's Daily's Weibo account from 2019 to 2021. The study analyzed the textual data with using trained artificial intelligence models.

RESULTS : This study explored the practices of the People's Daily's Weibo account from 2019 to 2021, COVID-19 is hardly normalized as it is still used as the justification for extraordinary measures in China. This study confirmed that People's Daily's Weibo account posts are undergoing softenization transformation, with the use of soft news, positive energy promotion, and the embedding of sentiment. Although the outburst of COVID-19 temporarily increased the media's use of hard news, it only occur at the initial stage of the pandemic. Emotional posts occupy a nonnegligible amount of the People's Daily Weibo content. However, the majority of posts are emotionally neutral and contribute to shaping the authoritative image of the party press.

DISCUSSION : Overall, the People's Daily has softened their communication style on digital platforms and used emotional mobilization, distraction, and timely information provision to balance the political logic of building an authoritative media agency and the media logic of constructing audience relevance.

Zhang Chang, Zhang Dechun, Shao Hsuan Lei

2023

COVID-19, China, Weibo, propoganda, state-run media

Internal Medicine Internal Medicine

The use of Smart Environments and Robots for Infection Prevention Control: a systematic literature review.

In American journal of infection control ; h5-index 43.0

Infection prevention and control (IPC) is essential to prevent nosocomial infections. The implementation of automation technologies can aid outbreak response. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings. The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. The quality of the included studies was appraised by two authors. The search strategy returned 1520 citations and 17 papers were included in this review. This review identified three main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (i.e., air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. Increasing the awareness of health care workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries.

Piaggio Davide, Zarro Marianna, Pagliara Silvio, Andellini Martina, Almuhini Abdulaziz, Maccaro Alessia, Pecchia Leandro

2023-Mar-14

Infection prevention and control, artificial intelligence, hand hygiene, health 4.0, internet of things, robot

General General

Research on Supply Chain Financial Risk Prevention Based on Machine Learning.

In Computational intelligence and neuroscience

Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

Lei Yang, Qiaoming Hou, Tong Zhao

2023

General General

Weibo users and Academia's foci on tourism safety: Implications from institutional differences and digital divide.

In Heliyon

Tourism safety is essential for tourists and tourism practitioners. This study conducted a bibliometric analysis using VOSviewer and CiteSpace for 2018 articles indexed on the Web of Science (WoS). It also analysed 7293 Weibo posts between 1977 and 2022 using Python, MYSQL, AI sentiment, and Tableau. The first tourism safety publication on WoS appeared in 1977, while the first Weibo microblog dated was dated back to 2011. Compared to the information posted on Weibo, the annual publications about tourism safety on WoS recorded a stable increment. On Web of Science (WoS), the academic staff and universities produced the largest number of tourism safety posts. On the flip side, the most productive organisations on Weibo are government agencies in popular tourism destinations. "Accident", "medical tourism", "environment", "mediating role", and "hospitality" were important burst nodes in tourism safety on WoS. "Quality", "accident", and health-related words were the foci on both Weibo and WoS. On Web of Science, the top 10 most popular keywords of tourism safety-related articles could be classified into two groups: health ("Covid-19", "restoration", "pandemics", "Sars-Cov-2", "Sars", "mental health") and IT terminologies ("big data", "artificial intelligence"). It has been concluded that "artificial intelligence (AI)" is more likely to be included in the keywords on tourism researched by academia. In contrast, the public may not know about or use AI in the tourism industry. Besides, the top 10 most popular keywords on Weibo related to tourism risks and hazards were drowning and traffic risks and hazards, such as drowning and traffic risks. The digital divide may explain such a difference: the academic circle benefits more from the digital age than laypersons. It may also be the result of institutional differences and information asymmetry.

Zeng Liyun, Li Rita Yi Man, Zeng Huiling

2023-Mar

Artificial intelligence, Bibliometrics, Comparative analysis, Digital divide, Information asymmetry, Tourism safety, Web of science, Weibo

General General

Multi-weight susceptible-infected model for predicting COVID-19 in China.

In Neurocomputing

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.

Zhang Jun, Zheng Nanning, Liu Mingyu, Yao Dingyi, Wang Yusong, Wang Jianji, Xin Jingmin

2023-May-14

COVID-19 prediction, Data processing, Epidemic model, Multi-weight susceptible-infected model

Public Health Public Health

CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.

In Frontiers in public health

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.

Marefat Abdolreza, Marefat Mahdieh, Hassannataj Joloudari Javad, Nematollahi Mohammad Ali, Lashgari Reza

2023

COVID-19, Compact Convolutional Transformers, Convolutional Neural Networks, deep learning, vision transformers

General General

Artificial intelligence evaluation of COVID-19 restrictions and speech therapy effects on the autistic children's behavior.

In Scientific reports ; h5-index 158.0

In the present study, we aimed to quantify the effects of COVID-19 restrictions and speech treatment approaches during lockdowns on autistic children using CBCL and neuro-fuzzy artificial intelligence method. In this regard, a survey including CBCL questionnaire is prepared using online forms. In total, 87 children with diagnosed Autism spectrum disorders (ASD) participated in the survey. The influences of three treatment approaches of in-person, telehealth and public services along with no-treatment condition during lockdown were the main factors of the investigation. The main output factors were internalized and externalized problems in general and their eight subcategory syndromes. We examined the reports by parents/caregivers to find correlation between treatments and CBCL listed problems. Moreover, comparison of the eight syndromes rating scores from pre-lockdown to post-lockdown periods were performed. In addition, artificial intelligence method were engaged to find the influence of speech treatment during restrictions on the level of internalizing and externalizing problems. In this regard, a fully connected adaptive neuro fuzzy inference system is employed with type and duration of treatments as input and T-scores of the syndromes are the output of the network. The results indicate that restrictions alleviate externalizing problems while intensifying internalizing problems. In addition, it is concluded that in-person speech therapy is the most effective and satisfactory approach to deal with ASD children during stay-at-home periods.

Sabzevari Fereshteh, Amelirad Omid, Moradi Zohre, Habibi Mostafa

2023-Mar-15

General General

IRCM-Caps: An X-ray image detection method for COVID-19.

In The clinical respiratory journal

OBJECTIVE : COVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet.

METHODS : The proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal.

RESULT : The test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively.

CONCLUSION : The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset.

Qiu Shuo, Ma Jinlin, Ma Ziping

2023-Mar-15

CNN, COVID-19, CapseNet, X-ray, cascade network, deep learning

oncology Oncology

Investigation of liquid biopsy analytes in peripheral blood of individuals after SARS-CoV-2 infection.

In EBioMedicine

BACKGROUND : Post-acute COVID-19 syndrome (PACS) is linked to severe organ damage. The identification and stratification of at-risk SARS-CoV-2 infected individuals is vital to providing appropriate care. This exploratory study looks for a potential liquid biopsy signal for PACS using both manual and machine learning approaches.

METHODS : Using a high definition single cell assay (HDSCA) workflow for liquid biopsy, we analysed 100 Post-COVID patients and 19 pre-pandemic normal donor (ND) controls. Within our patient cohort, 73 had received at least 1 dose of vaccination prior to SARS-CoV-2 infection. We stratified the COVID patients into 25 asymptomatic, 22 symptomatic COVID-19 but not suspected for PACS and 53 PACS suspected. All COVID-19 patients investigated in this study were diagnosed between April 2020 and January 2022 with a median 243 days (range 16-669) from diagnosis to their blood draw. We did a histopathological examination of rare events in the peripheral blood and used a machine learning model to evaluate predictors of PACS.

FINDINGS : The manual classification found rare cellular and acellular events consistent with features of endothelial cells and platelet structures in the PACS-suspected cohort. The three categories encompassing the hypothesised events were observed at a significantly higher incidence in the PACS-suspected cohort compared to the ND (p-value < 0.05). The machine learning classifier performed well when separating the NDs from Post-COVID with an accuracy of 90.1%, but poorly when separating the patients suspected and not suspected of PACS with an accuracy of 58.7%.

INTERPRETATION : Both the manual and the machine learning model found differences in the Post-COVID cohort and the NDs, suggesting the existence of a liquid biopsy signal after active SARS-CoV-2 infection. More research is needed to stratify PACS and its subsyndromes.

FUNDING : This work was funded in whole or in part by Fulgent Genetics, Kathy and Richard Leventhal and Vassiliadis Research Fund. This work was also supported by the National Cancer InstituteU54CA260591.

Qi Elizabeth, Courcoubetis George, Liljegren Emmett, Herrera Ergueen, Nguyen Nathalie, Nadri Maimoona, Ghandehari Sara, Kazemian Elham, Reckamp Karen L, Merin Noah M, Merchant Akil, Mason Jeremy, Figueiredo Jane C, Shishido Stephanie N, Kuhn Peter

2023-Mar-13

COVID-19, Liquid biopsy, Long COVID, Post-COVID sequelae, Post-acute COVID-19 syndrome (PACS), SARS-CoV-2

Dermatology Dermatology

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality.

In JAMA dermatology ; h5-index 54.0

IMPORTANCE : Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination.

OBJECTIVE : To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients.

DESIGN, SETTING, AND PARTICIPANTS : This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone.

INTERVENTIONS : During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient's assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos.

MAIN OUTCOMES AND MEASURES : The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support.

RESULTS : Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%.

CONCLUSIONS AND RELEVANCE : In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

Vodrahalli Kailas, Ko Justin, Chiou Albert S, Novoa Roberto, Abid Abubakar, Phung Michelle, Yekrang Kiana, Petrone Paige, Zou James, Daneshjou Roxana

2023-Mar-15

General General

A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation.

In Molecular systems biology

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

Mao Weiguang, Miller Clare M, Nair Venugopalan D, Ge Yongchao, Amper Mary Anne S, Cappuccio Antonio, George Mary-Catherine, Goforth Carl W, Guevara Kristy, Marjanovic Nada, Nudelman German, Pincas Hanna, Ramos Irene, Sealfon Rachel S G, Soares-Schanoski Alessandra, Vangeti Sindhu, Vasoya Mital, Weir Dawn L, Zaslavsky Elena, Kim-Schulze Seunghee, Gnjatic Sacha, Merad Miriam, Letizia Andrew G, Troyanskaya Olga G, Sealfon Stuart C, Chikina Maria

2023-Mar-15

DNA methylation, SARS-CoV-2, machine learning model, temporal dynamics, trained immunity

General General

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees.

In Heliyon

BACKGROUND AND OBJECTIVE : A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

MATERIAL AND METHOD : This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

RESULTS : Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

CONCLUSIONS : This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Huyut Mehmet Tahir, Huyut Zübeyir

2023-Mar

Artificial intelligence, CHAID decision Trees, COVID-19, Coagulation tests, Ferritin, Immunological tests, Machine learning, Mortality risk biomarkers

General General

Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.

In Biomedical signal processing and control

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0 . 8415 ± 0 . 0217 while it can also estimate the risk of death with an AUC-ROC of 0 . 7992 ± 0 . 0104 . Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources.

Morís Daniel I, de Moura Joaquim, Marcos Pedro J, Rey Enrique Míguez, Novo Jorge, Ortega Marcos

2023-Jul

COVID-19, Classification, Clinical data, Feature selection, Machine learning

Pathology Pathology

Paving New Roads Using Allium sativum as a Repurposed Drug and Analyzing its Antiviral Action Using Artificial Intelligence Technology.

In Iranian journal of pharmaceutical research : IJPR

CONTEXT : The whole universe is facing a coronavirus catastrophe, and prompt treatment for the health crisis is primarily significant. The primary way to improve health conditions in this battle is to boost our immunity and alter our diet patterns. A common bulb veggie used to flavor cuisine is garlic. Compounds in the plant that are physiologically active are present, contributing to its pharmacological characteristics. Among several food items with nutritional value and immunity improvement, garlic stood predominant and more resourceful natural antibiotic with a broad spectrum of antiviral potency against diverse viruses. However, earlier reports have depicted its efficacy in the treatment of a variety of viral illnesses. Nonetheless, there is no information on its antiviral activities and underlying molecular mechanisms.

OBJECTIVES : The bioactive compounds in garlic include organosulfur (allicin and alliin) and flavonoid (quercetin) compounds. These compounds have shown immunomodulatory effects and inhibited attachment of coronavirus to the angiotensin-converting enzyme 2 (ACE2) receptor and the Mpro of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Further, we have discussed the contradictory impacts of garlic used as a preventive measure against the novel coronavirus.

METHOD : The GC/MS analysis revealed 18 active chemicals, including 17 organosulfur compounds in garlic. Using the molecular docking technique, we report for the first time the inhibitory effect of the under-consideration compounds on the host receptor ACE2 protein in the human body, providing a crucial foundation for understanding individual compound coronavirus resistance on the main protease protein of SARS-CoV-2. Allyl disulfide and allyl trisulfide, which make up the majority of the compounds in garlic, exhibit the most potent activity.

RESULTS : Conventional medicine has proven its efficiency from ancient times. Currently, our article's prime spotlight was on the activity of Allium sativum on the relegation of viral load and further highlighted artificial intelligence technology to study the attachment of the allicin compound to the SARS-CoV-2 receptor to reveal its efficacy.

CONCLUSIONS : The COVID-19 pandemic has triggered interest among researchers to conduct future research on molecular docking with clinical trials before releasing salutary remedies against the deadly malady.

Atoum Manar Fayiz, Padma Kanchi Ravi, Don Kanchi Ravi

2022-Dec

Allium sativum, Flavonoid, Immunomodulatory, SARS-CoV-2

Surgery Surgery

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

In Respiratory research ; h5-index 45.0

BACKGROUND : We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores.

METHODS : This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis.

RESULTS : Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores.

CONCLUSION : The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Kwok Stephen Wai Hang, Wang Guanjin, Sohel Ferdous, Kashani Kianoush B, Zhu Ye, Wang Zhen, Antpack Eduardo, Khandelwal Kanika, Pagali Sandeep R, Nanda Sanjeev, Abdalrhim Ahmed D, Sharma Umesh M, Bhagra Sumit, Dugani Sagar, Takahashi Paul Y, Murad Mohammad H, Yousufuddin Mohammed

2023-Mar-13

COVID-19, Machine learning algorithms, Mortality, Organ failure, Prediction models

General General

Benchmarking machine learning robustness in Covid-19 genome sequence classification.

In Scientific reports ; h5-index 158.0

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.

Ali Sarwan, Sahoo Bikram, Zelikovsky Alexander, Chen Pin-Yu, Patterson Murray

2023-Mar-13

General General

The predictive model for COVID-19 pandemic plastic pollution by using deep learning method.

In Scientific reports ; h5-index 158.0

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others.

Nanehkaran Yaser A, Licai Zhu, Azarafza Mohammad, Talaei Sona, Jinxia Xu, Chen Junde, Derakhshani Reza

2023-Mar-13

General General

Analysis on factors affecting tourist involvement in coffee tourism after the COVID-19 pandemic in Thailand.

In F1000Research

Background: The world economy is affected by the coronavirus disease (COVID-19) pandemic, which affects the coffee industry. Coffee tourism is an emerging new type of tourism in Thailand that is formed in response to the growing demand from visitors with a particular affinity for coffee. Coffee tourism may contribute considerably to the expansion of Thai tourism given proper guidance and assistance. Methods: This study used a stochastic neuro-fuzzy decision tree (SNF-DT) to analyze coffee tourism in Thailand. This research surveyed 400 international and Thai coffee tourists. According to this study, Thai visitors mostly visit coffee tourism locations in Thailand for enjoyment. They also wanted to visit coffee fields to obtain personal knowledge about coffee production and marketing. Responses from foreign coffee tourists indicated that many of their journeys to coffee tourism destinations were entirely for enjoyment rather than business. They also wanted to meet local tour guides and acquire handmade and locally produced things to better understand coffee tourism. Results: According to the study results, coffee tourism management in northern Thailand appears to be well received by international tourists. We also compared the suggested model with the traditional model to demonstrate its efficacy. The performance metrics are the prediction rate, prediction error, and accuracy. The estimated results for our proposed technique are prediction rate (95%), prediction error (97%), and accuracy (94%). Recommendations: Major global businesses such as tourism have been harmed by COVID-19's unprecedented effects. This study attempts to determine the role of coffee tourism in livelihoods based on real-time data using a machine-learning approach. More research is needed to analyse the factors of the coffee tourism experience using different machine learning approaches.

Madhyamapurush Warach

2022

COVID-19 pandemic, Coffee Tourism, Foreign Tourists, Stochastic Neuro-Fuzzy Decision Tree (SNF-DT)., Tourist Behaviors

General General

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch.

In IEEE sensors journal

Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.

Saleem Kiran, Saleem Misbah, Ahmad Rana Zeeshan, Javed Abdul Rehman, Alazab Mamoun, Gadekallu Thippa Reddy, Suleman Ahmad

2023-Jan

Covid-19, NetLogo, Situation-awareness, ambient intelligence, belief-desire-intention (BDI), healthcare

General General

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals.

In IEEE sensors journal

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.

Tapwal Riya, Misra Sudip, Deb Pallav Kumar

2023-Jan

Artificial intelligence, COVID-19, remote monitoring, sensors, smart bedsheet

General General

The role of the mass vaccination programme in combating the COVID-19 pandemic: An LSTM-based analysis of COVID-19 confirmed cases.

In Heliyon

The COVID-19 virus has impacted all facets of our lives. As a global response to this threat, vaccination programmes have been initiated and administered in numerous nations. The question remains, however, as to whether mass vaccination programmes result in a decrease in the number of confirmed COVID-19 cases. In this study, we aim to predict the future number of COVID-19 confirmed cases for the top ten countries with the highest number of vaccinations in the world. A well-known Deep Learning method for time series analysis, namely, the Long Short-Term Memory (LSTM) networks, is applied as the prediction method. Using three evaluation metrics, i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), we found that the model built by using LSTM networks could give a good prediction of the future number and trend of COVID-19 confirmed cases in the considered countries. Two different scenarios are employed, namely: 'All Time', which includes all historical data; and 'Before Vaccination', which excludes data collected after the mass vaccination programme began. The average MAPE scores for the 'All Time' and 'Before Vaccination' scenarios are 5.977% and 10.388%, respectively. Overall, the results show that the mass vaccination programme has a positive impact on decreasing and controlling the spread of the COVID-19 disease in those countries, as evidenced by decreasing future trends after the programme was implemented.

Hansun Seng, Charles Vincent, Gherman Tatiana

2023-Mar

COVID-19, Confirmed cases, Deep learning, LSTM, Mass vaccination

Public Health Public Health

Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China.

In Computers in human behavior ; h5-index 125.0

The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.

Luo Han, Meng Xiao, Zhao Yifei, Cai Meng

2023-Jul

COVID-19, Emotional response, Information authority, Information dissemination, Information richness, Topic influence

General General

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.

In Frontiers in oncology

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Ozdemir Emine Sila, Nussinov Ruth

2023

artificial intelligence, cancer therapeutics, drug discovery, host-pathogen interactions, machine learning, protein-protein interactions

Pathology Pathology

Artificial intelligence-based HDX (AI-HDX) prediction reveals fundamental characteristics to protein dynamics: Mechanisms on SARS-CoV-2 immune escape.

In iScience

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

Yu Jiali, Uzuner Ugur, Long Bin, Wang Zachary, Yuan Joshua S, Dai Susie Y

2023-Apr-21

Immunology, Virology

Public Health Public Health

An anti-infodemic virtual center for the Americas.

In Revista panamericana de salud publica = Pan American journal of public health

The Pan American Health Organization/World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools-pairing AI with human judgment-to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search queries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO's knowledge management team, and the Librarian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches.

Brooks Ian, D’Agostino Marcelo, Marti Myrna, McDowell Kate, Mejia Felipe, Betancourt-Cravioto Miguel, Gatzke Lisa, Hicks Elaine, Kyser Rebecca, Leicht Kevin, Pereira Dos Santos Eliane, Saw Jessica Jia-Wen, Tomio Ailin, Garcia Saiso Sebastian

2023

Americas, COVID-19, Public Health Informatics, artificial intelligence, communication, social media

Public Health Public Health

IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection.

In Molecular therapy. Nucleic acids

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sites in SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.

Zhang Guiyang, Tang Qiang, Feng Pengmian, Chen Wei

2023-Jun-13

MT: Bioinformatics, SARS-CoV-2, attention mechanism, bidirectional gated recurrent unit, deep learning, interpretation, phosphorylation

Ophthalmology Ophthalmology

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

In Journal of telemedicine and telecare ; h5-index 28.0

INTRODUCTION : Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19.

METHODS : Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence.

RESULTS : A total of 385 eyes from 195 screening participants were included (mean age 52.43  ±  14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6  ±  13.3 s) faster than the tele-ophthalmology grader (129  ±  41.0) and clinical ophthalmologist (68  ±  21.9, p < 0.001).

DISCUSSION : Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.

Zhu Aretha, Tailor Priya, Verma Rashika, Zhang Isis, Schott Brian, Ye Catherine, Szirth Bernard, Habiel Miriam, Khouri Albert S

2023-Mar-13

COVID-19, Tele-ophthalmology, deep learning artificial intelligence, vision screenings

General General

Computational prediction of interactions between Paxlovid and prescription drugs.

In Proceedings of the National Academy of Sciences of the United States of America

Pfizer's Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug-drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.

Kim Yeji, Ryu Jae Yong, Kim Hyun Uk, Lee Sang Yup

2023-Mar-21

COVID-19, DeepDDI2, Paxlovid, drug interactions

General General

Big data and infectious disease epidemiology: A bibliometric analysis and research agenda.

In Interactive journal of medical research

BACKGROUND : Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.

OBJECTIVE : This paper aims to synthesize research and identify hotspots of big data in infectious disease epidemiology.

METHODS : Bibliometric data from 3054 documents that satisfied the inclusion criteria were retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.

RESULTS : The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. It also placed the US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.

CONCLUSIONS : Proposals for future studies are made based on these findings. This study will provide healthcare informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

Amusa Lateef Babatunde, Twinomurinzi Hossana, Phalane Edith, Phaswana-Mafuya Refilwe Nancy

2022-Nov-29

General General

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities.

In IEEE sensors journal

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

Nisar Shibli, Wakeel Abdul, Tahir Wania, Tariq Muhammad

2023-Jan

COVID-19, artificial intelligence, cellular forensics, hidden patterns, pandemic, tracking systems

Public Health Public Health

A machine learning analysis of correlates of mortality among patients hospitalized with COVID-19.

In Scientific reports ; h5-index 158.0

It is vital to determine how patient characteristics that precede COVID-19 illness relate to COVID-19 mortality. This is a retrospective cohort study of patients hospitalized with COVID-19 across 21 healthcare systems in the US. All patients (N = 145,944) had COVID-19 diagnoses and/or positive PCR tests and completed their hospital stays from February 1, 2020 through January 31, 2022. Machine learning analyses revealed that age, hypertension, insurance status, and healthcare system (hospital site) were especially predictive of mortality across the full sample. However, multiple variables were especially predictive in subgroups of patients. The nested effects of risk factors such as age, hypertension, vaccination, site, and race accounted for large differences in mortality likelihood with rates ranging from about 2-30%. Subgroups of patients are at heightened risk of COVID-19 mortality due to combinations of preadmission risk factors; a finding of potential relevance to outreach and preventive actions.

Baker Timothy B, Loh Wei-Yin, Piasecki Thomas M, Bolt Daniel M, Smith Stevens S, Slutske Wendy S, Conner Karen L, Bernstein Steven L, Fiore Michael C

2023-Mar-11

General General

A novel use of an artificially intelligent Chatbot and a live, synchronous virtual question-and answer session for fellowship recruitment.

In BMC medical education

INTRODUCTION : Academic departments universally communicate information about their programs using static websites. In addition to websites, some programs have even ventured out into social media (SM). These bidirectional forms of SM interaction show great promise; even hosting a live Question and Answer (Q&A) session has the potential for program branding. Artificial Intelligence (AI) usage in the form of a chatbot has expanded on websites and in SM. The potential use of chatbots, for the purposes of trainee recruitment, is novel and underutilized. With this pilot study, we aimed to answer the question; can the use of an Artificially Intelligent Chatbot and a Virtual Question-and-Answer Session aid in recruitment in a Post-COVID-19 era?

METHODS : We held three structured Question-and-Answer Sessions over a period of 2 weeks. This preliminary study was performed after completion of the three Q&A sessions, in March-May, 2021. All 258 applicants to the pain fellowship program were invited via email to participate in the survey after attending one of the Q&A sessions. A 16-item survey assessing participants' perception of the chatbot was administered.

RESULTS : Forty-eight pain fellowship applicants completed the survey, for an average response rate of 18.6%. In all, 35 (73%) of survey respondents had used the website chatbot, and 84% indicated that it had found them the information they were seeking.

CONCLUSION : We employed an artificially intelligent chatbot on the department website to engage in a bidirectional exchange with users to adapt to changes brought on by the pandemic. SM engagement via chatbot and Q&A sessions can leave a favorable impression and improve the perception of a program.

Yi Peter K, Ray Neil D, Segall Noa

2023-Mar-11

Artificial intelligence, Graduate medical education, Innovation and technology, Recruitment, Social media

General General

New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning.

In BMC bioinformatics

BACKGROUND : In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2.

RESULTS : In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256.

CONCLUSIONS : The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.

de Souza Luísa C, Azevedo Karolayne S, de Souza Jackson G, Barbosa Raquel de M, Fernandes Marcelo A C

2023-Mar-11

CGR DFT, COVID-19, Deep learning, GSP, SARS-CoV-2

Public Health Public Health

A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.

In Scientific reports ; h5-index 158.0

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

Hammad Muhammed S, Ghoneim Vidan F, Mabrouk Mai S, Al-Atabany Walid I

2023-Mar-10

General General

A generalized distributed delay model of COVID-19: An endemic model with immunity waning.

In Mathematical biosciences and engineering : MBE

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.

Iyaniwura Sarafa A, Musa Rabiu, Kong Jude D

2023-Jan-12

** COVID-19 , SARS-CoV-2 , distributed delay equations , endemic model , global stability , immunity waning , linear chain trick , vaccination **

General General

Research of mortality risk prediction based on hospital admission data for COVID-19 patients.

In Mathematical biosciences and engineering : MBE

As COVID-19 continues to spread across the world and causes hundreds of millions of infections and millions of deaths, medical institutions around the world keep facing a crisis of medical runs and shortages of medical resources. In order to study how to effectively predict whether there are risks of death in patients, a variety of machine learning models have been used to learn and predict the clinical demographics and physiological indicators of COVID-19 patients in the United States of America. The results show that the random forest model has the best performance in predicting the risk of death in hospitalized patients with COVID-19, as the COVID-19 patients' mean arterial pressures, ages, C-reactive protein tests' values, values of blood urea nitrogen and their clinical troponin values are the most important implications for their risk of death. Healthcare organizations can use the random forest model to predict the risks of death based on data from patients admitted to a hospital due to COVID-19, or to stratify patients admitted to a hospital due to COVID-19 based on the five key factors this can optimize the diagnosis and treatment process by appropriately arranging ventilators, the intensive care unit and doctors, thus promoting the efficient use of limited medical resources during the COVID-19 pandemic. Healthcare organizations can also establish databases of patient physiological indicators and use similar strategies to deal with other pandemics that may occur in the future, as well as save more lives threatened by infectious diseases. Governments and people also need to take action to prevent possible future pandemics.

Shen Qian

2023-Jan-11

** COVID-19 , death risk feature , emergency triage strategy , ensemble learning , machine learning , tree model **

General General

An application of artificial intelligence for investigating the effect of COVID-19 lockdown on three-dimensional temperature variation in equatorial Africa.

In Geoscience frontiers

We present interesting application of artificial intelligence for investigating effect of the COVID-19 lockdown on 3-dimensional temperature variation across Nigeria (2°-15° E, 4°-14° N), in equatorial Africa. Artificial neural networks were trained to learn time-series temperature variation patterns using radio occultation measurements of atmospheric temperature from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). Data used for training, validation and testing of the neural networks covered period prior to the lockdown. There was also an investigation into the viability of solar activity indicator (represented by the sunspot number) as an input for the process. The results indicated that including the sunspot number as an input for the training did not improve the network prediction accuracy. The trained network was then used to predict values for the lockdown period. Since the network was trained using pre-lockdown dataset, predictions from the network are regarded as expected temperatures, should there have been no lockdown. By comparing with the actual COSMIC measurements during the lockdown period, effects of the lockdown on atmospheric temperatures were deduced. In overall, the mean altitudinal temperatures rose by about 1.1 °C above expected values during the lockdown. An altitudinal breakdown, at 1 km resolution, reveals that the values were typically below 0.5 °C at most of the altitudes, but exceeded 1 °C at 28 and 29 km altitudes. The temperatures were also observed to drop below expected values at altitudes of 0-2 km, and 17-20 km.

Okoh Daniel, Onuorah Loretta, Rabiu Babatunde, Obafaye Aderonke, Audu Dauda, Yusuf Najib, Owolabi Oluwafisayo

2022-Mar

COVID-19 lockdown, Equatorial Africa, Neural network, Sunspot number, Temperature, Time-series

Radiology Radiology

CoviExpert: COVID-19 detection from chest X-ray using CNN.

In Measurement. Sensors

COVID-19 continues to threaten the world with its impact and severity. This pandemic has created a sense of havoc and shook the world stretching the medical fraternity to an unimaginable extent, who are now facing fatigue and exhaustion. Due to the rapid increase in cases all across the globe demanding extensive medical care, people are hunting for resources like testing facilities, medical drugs and even hospital beds. Even people with mild to moderate infection are panicking and mentally giving up due to anxiety and desperation. To combat these issues, it is necessary to find an inexpensive and faster way to save lives and bring about a much-needed change. The most fundamental way through which this can be achieved is with the help of radiology which involves examination of Chest X rays. They are primarily used for the diagnosis of this disease. But due to panic and severity of this disease a recent trend of performing CT scans has been observed. This has been under scrutiny since it exposes patients to a very high level of radiation known to increase the probability of cancer. As quoted by the AIIMS Director, one CT scan is equivalent to around 300-400 Chest X-rays. Also, it is relatively a much costlier testing method. Hence, in this report, we have presented a Deep learning approach which can detect covid 19 positive cases from Chest X ray images. It involves creation of a Deep learning based Convolutional Neural Network (CNN) using Keras (python library) and integrating the model with a front-end user interface for ease of use. This leads up to the creation of a software which we have named as CoviExpert. It uses the sequential Keras model which is built layer by layer. All the layers are trained independently to make independent predictions which are then combined to give the final output. 1584 images of Chest X-rays of both COVID-19 positive and negative patients have been used as training data. 177 images have been used as testing data. The proposed approach gives a classification accuracy of 99%. CoviExpert can be used on any device by any medical professional to detect Covid positive patients within a few seconds.

Arivoli A, Golwala Devdatt, Reddy Rayirth

2022-Oct

CNN, COVID-19, CT scan, CoviExpert, Deep learning, X-ray

General General

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.

In Journal of business research

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Ortiz-Barrios Miguel, Arias-Fonseca Sebastián, Ishizaka Alessio, Barbati Maria, Avendaño-Collante Betty, Navarro-Jiménez Eduardo

2023-May

Artificial Intelligence (AI), Covid-19, Discrete-Event Simulation (DES), Healthcare, Intensive Care Unit (ICU), Random Forest (RF)

General General

Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the extent of data drift and provide insights about its characteristics for sepsis onset prediction. This study will help elucidate the nature of data drift for prediction of sepsis and similar diseases. This may aid with the development of more effective patient monitoring systems that can stratify risk for dynamic disease states in hospitals.

METHODS : We devise a series of simulations that measure the effects of data drift in patients with sepsis, using electronic health records (EHR). We simulate multiple scenarios in which data drift may occur, namely the change in the distribution of the predictor variables (covariate shift), the change in the statistical relationship between the predictors and the target (concept shift), and the occurrence of a major healthcare event (major event) such as the COVID-19 pandemic. We measure the impact of data drift on model performances, identify the circumstances that necessitate model retraining, and compare the effects of different retraining methodologies and model architecture on the outcomes. We present the results for two different MLAs, eXtreme Gradient Boosting (XGB) and Recurrent Neural Network (RNN).

RESULTS : Our results show that the properly retrained XGB models outperform the baseline models in all simulation scenarios, hence signifying the existence of data drift. In the major event scenario, the area under the receiver operating characteristic curve (AUROC) at the end of the simulation period is 0.811 for the baseline XGB model and 0.868 for the retrained XGB model. In the covariate shift scenario, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.853 and 0.874 respectively. In the concept shift scenario and under the mixed labeling method, the retrained XGB models perform worse than the baseline model for most simulation steps. However, under the full relabeling method, the AUROC at the end of the simulation period for the baseline and retrained XGB models is 0.852 and 0.877 respectively. The results for the RNN models were mixed, suggesting that retraining based on a fixed network architecture may be inadequate for an RNN. We also present the results in the form of other performance metrics such as the ratio of observed to expected probabilities (calibration) and the normalized rate of positive predictive values (PPV) by prevalence, referred to as lift, at a sensitivity of 0.8.

CONCLUSION : Our simulations reveal that retraining periods of a couple of months or using several thousand patients are likely to be adequate to monitor machine learning models that predict sepsis. This indicates that a machine learning system for sepsis prediction will probably need less infrastructure for performance monitoring and retraining compared to other applications in which data drift is more frequent and continuous. Our results also show that in the event of a concept shift, a full overhaul of the sepsis prediction model may be necessary because it indicates a discrete change in the definition of sepsis labels, and mixing the labels for the sake of incremental training may not produce the desired results.

Rahmani Keyvan, Thapa Rahul, Tsou Peiling, Casie Chetty Satish, Barnes Gina, Lam Carson, Foon Tso Chak

2022-Nov-19

Clinical decision support, Data drift, Machine learning, Sepsis

Public Health Public Health

Does social distancing impact pediatric upper airway infections? An observational controlled study and a brief literature review.

In American journal of otolaryngology ; h5-index 23.0

PURPOSE : SARS-CoV-2 pandemic has reduced social interaction even among children. The objective of the study was to assess the role of social distancing in the course of common pediatric upper airway recurrent diseases.

MATERIALS AND METHODS : Patients aged ≤14 years with at least one ENT-related clinical condition were retrospectively recruited. All patients had two outpatient evaluations in the same period (April - September): the control group had the first evaluation in 2018 and second in 2019, whereas the case group had the first evaluation in 2019 and second in 2020. Patients of each group were individually compared between their two visits and deemed improved/unchanged/worsened for each specific ENT condition. The percentage of children improved/unchanged/worsened were then collectively compared between the two groups for each condition.

RESULTS : Patients who experienced social distancing presented a significantly higher improvement rate than controls for recurrent acute otitis media episodes (35.1 % vs. 10.8 %; Fisher's exact test p = 0.033) and for tympanogram type (54.5 % vs. 11.1 %, Fisher's exact test p = 0.009).

CONCLUSIONS : The anti-contagion social restrictions decreased the prevalence of middle ear infections and effusion in children. Further studies on larger cohorts are required to better elucidate these findings.

Franchella Sebastiano, Favaretto Niccolò, Frigo Annachiara, Franz Leonardo, Pilo Simona, Mularoni Francesca, Marciani Silvia, Nicolai Piero, Marioni Gino, Cazzador Diego

2023-Mar-01

COVID-19, Pediatric, SARS-CoV-2, Social distancing, Upper airways infection

Public Health Public Health

Determinants of COVID-19 vaccine hesitancy among students and parents in Sentinel Schools Network of Catalonia, Spain.

In PloS one ; h5-index 176.0

Vaccine hesitancy is defined as a delay in acceptance of vaccines despite its availability, caused by many determinants. Our study presents the key reasons, determinants and characteristics associated with COVID-19 vaccine acceptability among students over 16 years and parents of students under 16 years and describe the COVID-19 vaccination among students in the settings of sentinel schools of Catalonia, Spain. This is a cross-sectional study that includes 3,383 students and the parents between October 2021 and January 2022. We describe the student's vaccination status and proceed a univariate and multivariate analysis using a Deletion Substitution Addition (DSA) machine learning algorithm. Vaccination against COVID-19 reached 70.8% in students under 16 years and 95.8% in students over 16 years at the end of the study project. The acceptability among unvaccinated students was 40.9% and 20.8% in October and January, respectively, and among parents was proportionally higher among students aged 5-11 (70.2%) in October and aged 3-4 (47.8%) in January. The key reason to not vaccinate themselves, or their children, were concern about side effects, insufficient research about the effect of the vaccine in children, rapid development of vaccines, necessity for more information and previous infection by SARS-CoV-2. Several variables were associated with refusal end hesitancy. For students, the main ones were risk perception and use of alternative therapies. For parents, the age of students, sociodemographic variables, socioeconomic impact related to the pandemic, and use of alternative therapies were more evident. Monitoring vaccine acceptance and refusal among children and their parents has been important to understand the interaction between different multilevel determinants and we hope it will be useful to improve public health strategies for future interventions in this population.

Ganem Fabiana, Folch Cinta, Colom-Cadena Andreu, Bordas Anna, Alonso Lucia, Soriano-Arandes Antoni, Casabona Jordi

2023

General General

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.

In PloS one ; h5-index 176.0

BACKGROUND : The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19.

METHODS : Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.

RESULTS : Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes.

CONCLUSIONS : This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model's components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies.

Moradi Hamidreza, Bunnell H Timothy, Price Bradley S, Khodaverdi Maryam, Vest Michael T, Porterfield James Z, Anzalone Alfred J, Santangelo Susan L, Kimble Wesley, Harper Jeremy, Hillegass William B, Hodder Sally L

2023

General General

A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans.

In PloS one ; h5-index 176.0

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Akl Ahmed A, Hosny Khalid M, Fouda Mostafa M, Salah Ahmad

2023

Radiology Radiology

COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.

In European radiology ; h5-index 62.0

OBJECTIVES : We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions.

METHODS : This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks.

RESULTS : The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis.

CONCLUSIONS : This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.

Wen Ru, Zhang Mudan, Xu Rui, Gao Yingming, Liu Lin, Chen Hui, Wang Xingang, Zhu Wenyan, Lin Huafang, Liu Chen, Zeng Xianchun

2023-Mar-09

Bibliometrics, COVID-19, CiteSpace, Medical imaging, Treads

Radiology Radiology

Artificial intelligence for assistance of radiology residents in chest CT evaluation for COVID-19 pneumonia: a comparative diagnostic accuracy study.

In Acta radiologica (Stockholm, Sweden : 1987)

BACKGROUND : In hospitals, it is crucial to rule out coronavirus disease 2019 (COVID-19) timely and reliably. Artificial intelligence (AI) provides sufficient accuracy to identify chest computed tomography (CT) scans with signs of COVID-19.

PURPOSE : To compare the diagnostic accuracy of radiologists with different levels of experience with and without assistance of AI in CT evaluation for COVID-19 pneumonia and to develop an optimized diagnostic pathway.

MATERIAL AND METHODS : The retrospective, single-center, comparative case-control study included 160 consecutive participants who had undergone chest CT scan between March 2020 and May 2021 without or with confirmed diagnosis of COVID-19 pneumonia in a ratio of 1:3. Index tests were chest CT evaluation by five radiological senior residents, five junior residents, and an AI software. Based on the diagnostic accuracy in every group and on comparison of groups, a sequential CT assessment pathway was developed.

RESULTS : Areas under receiver operating curves were 0.95 (95% confidence interval [CI]=0.88-0.99), 0.96 (95% CI=0.92-1.0), 0.77 (95% CI=0.68-0.86), and 0.95 (95% CI=0.9-1.0) for junior residents, senior residents, AI, and sequential CT assessment, respectively. Proportions of false negatives were 9%, 3%, 17%, and 2%, respectively. With the developed diagnostic pathway, junior residents evaluated all CT scans with the support of AI. Senior residents were only required as second readers in 26% (41/160) of the CT scans.

CONCLUSION : AI can support junior residents with chest CT evaluation for COVID-19 and reduce the workload of senior residents. A review of selected CT scans by senior residents is mandatory.

Mlynska Lucja, Malouhi Amer, Ingwersen Maja, Güttler Felix, Gräger Stephanie, Teichgräber Ulf

2023-Mar-08

Artificial intelligence, COVID-19, SARS-CoV-2, computed tomography, deep learning, neural networks

Radiology Radiology

Machine learning prediction for COVID-19 disease severity at hospital admission.

In BMC medical informatics and decision making ; h5-index 38.0

IMPORTANCE : Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation.

OBJECTIVE : To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data.

DESIGN, SETTING, AND PARTICIPANTS : We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest's feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods.

MAIN OUTCOMES AND MEASURES : Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation.

RESULTS : This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html .

CONCLUSIONS AND RELEVANCE : In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.

Raman Ganesh, Ashraf Bilal, Demir Yusuf Kemal, Kershaw Corey D, Cheruku Sreekanth, Atis Murat, Atis Ahsen, Atar Mustafa, Chen Weina, Ibrahim Ibrahim, Bat Taha, Mete Mutlu

2023-Mar-07

COVID-19, Classification, Laboratory markers, Machine learning, Prediction, SARS-CoV-2, Scoring

General General

Effects of Antidepressants on COVID Outcome: A Retrospective Study on Large Scale Electronic Health Record Data.

In Interactive journal of medical research

BACKGROUND : Antidepressants are a type of medication used to treat clinical depression or prevent it recurring. Antidepressants exert an anticholinergic effect in varying degrees and various classes of antidepressants also can produce a different effect on immune function. While early usage of antidepressants has notional role on COVID-19 outcomes, the relationship between the risk of COVID-19 severity and the use of all kinds of antidepressants is not properly investigated before due to the exceeding cost involved with clinical trials. Large-scale observational data such as electronic health records and recent advancement of statistical analysis provide ample opportunity to virtualize clinical trial to discover detrimental effects of early usage of these drugs.

OBJECTIVE : By mining a large-scale electronic health record data set of COVID-19 positive patients, we aim to identify common drugs that are associated with COVID-19 outcome. However, whereas the statisticians have made great progress toward using such rich association estimation methods for risk estimation, precise effects of the medicines as treatments require causal models. Thus, our central aim of this paper lies on investigating electronic health record analytic for causal effect estimation and utilize that in discovering causal effects of early antidepressants use on COVID-19 outcomes. As a secondary aim, we develop methods for validating our causal effect estimation pipeline.

METHODS : We focus on antidepressants, a commonly used category of drugs that have been linked to unexpected effects on diverse inflammatory and cardiovascular outcomes and infer early use of such drug use effects on COVID-19 outcomes. However, whereas the machine learning and statistics community have made great progress toward using rich inference models, precise effects of the medicines as treatments require causal models, for which there is significantly less theoretical and practical guidance available. We used National COVID Cohort Collaborative (N3C), a database aggregating health history for over 12+ million people in the USA, including 5+ million with a positive COVID-19 test. We selected 241,952 COVID-19 positive patients with at least one year of medical history and age>13 that included 18,584-dimensional covariate vector for each person and 16 different antidepressants usage histories. We used propensity score weighting based on logistic regression method to estimate causal effect on whole data. Then we used Node2Vec embedding method to encode SNOMED medical code and apply random forest regression to estimate causal effect. We use both methods to estimate causal effects of antidepressants on COVID-19 outcome. We also selected few negatively effective conditions on COVID-19 outcomes and estimated their effects using our proposed methods to validate their efficacy.

RESULTS : Average Treatment Effect (ATE) of using any one of the antidepressants is -0.076 with 95% CI from -0.082 to - 0.069 with propensity score weighting method. The result is statistically significant at p<0.0001. In case of the method using SNOMED medical embedding, the ATE of using any one of the antidepressants is -0.423 with 95% CI from -0.382 to -0.463. This result is also statistically significant at p<0.0001.

CONCLUSIONS : In this study, we apply multiple causal inference methods incorporating with a novel application of health embeddings to investigate the effects of antidepressants on COVID-19 outcome. Additionally, we propose a novel non-affecting drug effect analysis-based evaluation technique to justify the efficacy of proposed method. This study offers causal inference methods on large-scale EHR data to discover common antidepressants' effects on COVID-19 hospitalization, or a worse outcome. The study finds that common antidepressants may increase risk of COVID-19 complications and uncovers a pattern where certain antidepressants are associated with lower risk of hospitalization. While discovering detrimental effects of these drugs on outcome could guide preventive care, identification of beneficial effects would allow us to propose drug repurposing for COVID-19 treatment.

Rahman Md Mahmudur, Mahi Atqiya Munawara, Melamed Rachel D, Alam Mohammad Arif Ul

2023-Mar-05

General General

Development and validation of a respiratory-responsive vocal biomarker-based tool for generalizable detection of respiratory impairment: independent case-control studies in multiple respiratory conditions including asthma, chronic obstructive pulmonary disease, and COVID-19.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Vocal biomarker-based machine learning approaches have shown promising results in detecting various health conditions, including respiratory diseases such as asthma. In this study, we aim to validate a respiratory-responsive vocal-biomarker (RRVB) platform initially trained on an asthma and healthy volunteer dataset for its ability to differentiate, without modification, active COVID-19 infection vs. healthy volunteers in patients presenting to hospitals in the US and India.

OBJECTIVE : The objective of this study was to determine whether the RRVB model can differentiate patients with active COVID-19 infection vs. asymptomatic healthy volunteers by assessing its sensitivity, specificity, and odds ratio. Another objective was to evaluate whether the RRVB model outputs correlate with symptom severity in COVID-19.

METHODS : A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a dataset of about 1,700 patients with a confirmed asthma diagnosis vs. a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and cough. In the present study, a total of 497 participants (46% male, 54% female; 94% < 65 years, 6% >= 65 years; 51% Marathi, 45% English, 5% Spanish speakers) were enrolled across four clinical sites in US and India and provided voice samples and symptom reports on their personal smartphones. The participants included symptomatic COVID-19 positive and negative patients as well as asymptomatic healthy volunteers. The RRVB model performance was assessed by comparison with clinical diagnosis of COVID-19 confirmed by RT-PCR.

RESULTS : The RRVB model's ability to differentiate patients with respiratory conditions vs. healthy controls was previously demonstrated on validation data in asthma, COPD, ILD and cough with odds ratios of 4.3, 9.1, 3.1, and 3.9 respectively. The same RRVB model in the present study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and odds ratio of 4.64 (p<0.0001). Patients experiencing respiratory symptoms were detected more frequently than those not experiencing respiratory symptoms and completely asymptomatic patients (78.4% vs. 67.4% vs. 68.0%).

CONCLUSIONS : The RRVB model has shown good generalizability across respiratory conditions, geographies, and language. Results in COVID-19 demonstrate its meaningful potential to serve as a pre-screening tool for identifying subjects at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model could encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path to development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.

CLINICALTRIAL : ClinicalTrials.gov (NCT04582331.

Kaur Savneet, Larsen Erik, Harper James, Purandare Bharat, Uluer Ahmet, Hasdianda Mohammad Adrian, Umale Nikita, Killeen James, Castillo Edward, Jariwala Sunit

2023-Feb-28

General General

Interpretation of lung disease classification with light attention connected module.

In Biomedical signal processing and control

Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts' opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes.

Choi Youngjin, Lee Hongchul

2023-Jul

Attention, ECA-Net, Grad-CAM, Lung disease, Respiratory sound, eXplainable AI

General General

Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.

In Frontiers in aging neuroscience ; h5-index 64.0

BACKGROUND AND OBJECTIVES : The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions.

METHODS : The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model.

RESULTS : For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate).

CONCLUSION : Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.

Ma Ling-Yan, Shi Wei-Kun, Chen Cheng, Wang Zhan, Wang Xue-Mei, Jin Jia-Ning, Chen Lu, Ren Kang, Chen Zhong-Lue, Ling Yun, Feng Tao

2023

Parkinson’s disease, computer vision, machine learning, postural stability, remote, rigidity

General General

STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information.

In Biomedical signal processing and control

The modern urban population features a high population density and a fast population flow, and COVID-19 has strong transmission ability, long incubation period, and other characteristics. Considering only the time sequence of COVID-19 transmission cannot effectively respond to the current epidemic transmission situation. The distance between cities and population density information also have a significant impact on the transmission of the virus. Currently, cross-domain transmission prediction models do not fully exploit the time-space information and fluctuation trend of data, and cannot reasonably predict the trend of infectious diseases by integrating time-space multi-source information. To solve this problem, this paper proposes the COVID-19 prediction network (STG-Net) based on multivariate spatio-temporal information, which introduces the Spatial Information Mining module (SIM) and the Temporal Information Mining module (TIM) to mine the spatio-temporal information of the data in a deeper level, and uses the slope feature method to further mine the fluctuation trend of the data. Also, we introduce the Gramian Angular Field module (GAF), which converts one-dimensional data into two-dimensional images, further enhancing the network's feature mining capability in the time and feature dimension, ultimately combining spatiotemporal information to predict daily newly confirmed cases. We tested the network on datasets from China, Australia, the United Kingdom, France, and Netherlands. The experimental results show that STG-Net has better prediction performance than existing prediction models, with an average decision coefficient R2 of 98.23% on the datasets from five countries, as well as good long- and short-term prediction ability and overall good robustness.

Song Yucheng, Chen Huaiyi, Song Xiaomeng, Liao Zhifang, Zhang Yan

2023-Jul

COVID-19, Confirmed cases forecasting, Deep learning, STG-Net, Spatial information, Time series

General General

Continuous diagnosis and prognosis by controlling the update process of deep neural networks.

In Patterns (New York, N.Y.)

Continuous diagnosis and prognosis are essential for critical patients. They can provide more opportunities for timely treatment and rational allocation. Although deep-learning techniques have demonstrated superiority in many medical tasks, they frequently forget, overfit, and produce results too late when performing continuous diagnosis and prognosis. In this work, we summarize the four requirements; propose a concept, continuous classification of time series (CCTS); and design a training method for deep learning, restricted update strategy (RU). The RU outperforms all baselines and achieves average accuracies of 90%, 97%, and 85% on continuous sepsis prognosis, COVID-19 mortality prediction, and eight disease classifications, respectively. The RU can also endow deep learning with interpretability, exploring disease mechanisms through staging and biomarker discovery. We find four sepsis stages, three COVID-19 stages, and their respective biomarkers. Further, our approach is data and model agnostic. It can be applied to other diseases and even in other fields.

Sun Chenxi, Li Hongyan, Song Moxian, Cai Derun, Zhang Baofeng, Hong Shenda

2023-Feb-10

COVID-19, biomarker, continuous classification, deep learning, diagnosis, disease staging, prognosis, sepsis, time series

General General

Present and future perspectives in early diagnosis and monitoring for progressive fibrosing interstitial lung diseases.

In Frontiers in medicine

Progressive fibrosing interstitial lung diseases (PF-ILDs) represent a group of conditions of both known and unknown origin which continue to worsen despite standard treatments, leading to respiratory failure and early mortality. Given the potential to slow down progression by initiating antifibrotic therapies where appropriate, there is ample opportunity to implement innovative strategies for early diagnosis and monitoring with the goal of improving clinical outcomes. Early diagnosis can be facilitated by standardizing ILD multidisciplinary team (MDT) discussions, implementing machine learning algorithms for chest computed-tomography quantitative analysis and novel magnetic-resonance imaging techniques, as well as measuring blood biomarker signatures and genetic testing for telomere length and identification of deleterious mutations in telomere-related genes and other single-nucleotide polymorphisms (SNPs) linked to pulmonary fibrosis such as rs35705950 in the MUC5B promoter region. Assessing disease progression in the post COVID-19 era also led to a number of advances in home monitoring using digitally-enabled home spirometers, pulse oximeters and other wearable devices. While validation for many of these innovations is still in progress, significant changes to current clinical practice for PF-ILDs can be expected in the near future.

Stanel Stefan Cristian, Rivera-Ortega Pilar

2023

PF-ILD, PPF, idiopathic pulmonary fibrosis, interstitial lung disease, progressive fibrosing interstitial lung disease, progressive pulmonary fibrosis

Cardiology Cardiology

A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation.

In Nature machine intelligence

CITE-seq, a single-cell multi-omics technology that measures RNA and protein expression simultaneously in single cells, has been widely applied in biomedical research, especially in immune related disorders and other diseases such as influenza and COVID-19. Despite the proliferation of CITE-seq, it is still costly to generate such data. Although data integration can increase information content, this raises computational challenges. First, combining multiple datasets is prone to batch effects that need to be addressed. Secondly, it is difficult to combine multiple CITE-seq datasets because the protein panels in different datasets may only partially overlap. Integrating multiple CITE-seq and single-cell RNA-seq (scRNA-seq) datasets is important because this allows the utilization of as many data as possible to uncover cell population heterogeneity. To overcome these challenges, we present sciPENN, a multi-use deep learning approach that supports CITE-seq and scRNA-seq data integration, protein expression prediction for scRNA-seq, protein expression imputation for CITE-seq, quantification of prediction and imputation uncertainty, and cell type label transfer from CITE-seq to scRNA-seq. Comprehensive evaluations spanning multiple datasets demonstrate that sciPENN outperforms other current state-of-the-art methods.

Lakkis Justin, Schroeder Amelia, Su Kenong, Lee Michelle Y Y, Bashore Alexander C, Reilly Muredach P, Li Mingyao

2022-Nov

CITE-seq, deep learning, protein prediction, single-cell RNA-seq, single-cell multi-omics

General General

Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review.

In Informatics in medicine unlocked

The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.

Arji Goli, Ahmadi Hossein, Avazpoor Pejman, Hemmat Morteza

2023

COVID-19, Healthcare supply chain, Literature review, Pandemics, Supply chain management

General General

Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19.

In Frontiers in artificial intelligence

Recently, research is emerging highlighting the potential of cannabinoids' beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of cannabinoid-based medicines since COVID-19 was declared a pandemic. The objective of this research is 3 fold: i) to evaluate the relationship of the clinical delivery of cannabinoid-based medicine for anxiety, depression and sleep scores by utilizing machine learning specifically rough set methods; ii) to discover patterns based on patient features such as specific cannabinoid recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (CAT) scores over a period of time; and iii) to predict whether new patients could potentially experience either an increase or decrease in CAT scores. The dataset for this study was derived from patient visits to Ekosi Health Centres, Canada over a 2 year period including the COVID timeline. Extensive pre-processing and feature engineering was performed. A class feature indicative of their progress or lack thereof due to the treatment received was introduced. Six Rough/Fuzzy-Rough classifiers as well as Random Forest and RIPPER classifiers were trained on the patient dataset using a 10-fold stratified CV method. The highest overall accuracy, sensitivity and specificity measures of over 99% was obtained using the rule-based rough-set learning model. In this study, we have identified rough-set based machine learning model with high accuracy that could be utilized for future studies regarding cannabinoids and precision medicine.

Ramanna Sheela, Ashrafi Negin, Loster Evan, Debroni Karen, Turner Shelley

2023

cannabinoid medicine, electronic health records, machine learning, mental health, rough sets, rough-fuzzy sets

General General

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.

In Frontiers in artificial intelligence

INTRODUCTION : The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing.

METHOD : In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19.

RESULT : With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies.

CONCLUSION : These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

Askari Nasab Kazem, Mirzaei Jamal, Zali Alireza, Gholizadeh Sarfenaz, Akhlaghdoust Meisam

2023

artificial intelligence, coronavirus, cough, deep learning, machine learning, respiratory sounds

General General

MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization.

In Journal of X-ray science and technology

BACKGROUND : COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed.

OBJECTIVE : The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images.

METHODS : Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes.

RESULTS : The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches.

CONCLUSION : The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.

Deepak Gerard, Madiajagan M, Kulkarni Sanjeev, Ahmed Ahmed Najat, Gopatoti Anandbabu, Ammisetty Veeraswamy

2023-Feb-28

COVID-19, chest X-Ray, deep-Q-neural networks, hybrid median bilateral filter, robust feature neural network

General General

Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia.

In Scientific reports ; h5-index 158.0

In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)-based on publicly available national statistics-founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots-aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.

Shonchoy Abu S, Mahzab Moogdho M, Mahmood Towhid I, Ali Manhal

2023-Mar-06

Public Health Public Health

Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients.

In Journal of integrative bioinformatics

To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19.

Casano Nicolò, Santini Silvano Junior, Vittorini Pierpaolo, Sinatti Gaia, Carducci Paolo, Mastroianni Claudio Maria, Ciardi Maria Rosa, Pasculli Patrizia, Petrucci Emiliano, Marinangeli Franco, Balsano Clara

2023-Mar-07

COVID-19, ROX index, SARS-CoV-2, emergency medicine, machine learning

General General

Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods.

In Journal of molecular graphics & modelling

The main protease of SARS-CoV-2 (called Mpro or 3CLpro) is essential for processing polyproteins encoded by viral RNA. Several Mpro mutations were found in SARS-CoV-2 variants, which are related to higher transmissibility, pathogenicity, and resistance to neutralization antibodies. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. In this study, we used a hybrid simulation method to generate intermediate structures along the six lowest frequency normal modes and sample the conformational space and characterize the structural dynamics and global motions of WT SARS-CoV-2 Mpro and 48 mutations, including mutations found in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. We tried to contribute to the elucidation of the effects of mutation in the structural dynamics of SARS-CoV-2 Mpro. A machine learning analysis was performed following the investigation regarding the influence of the K90R, P99L, P108S, and N151D mutations on the dimeric interface assembling of the SARS-CoV-2 Mpro. The parameters allowed the selection of potential structurally stable dimers, which demonstrated that some single surface aa substitutions not located at the dimeric interface (K90R, P99L, P108S, and N151D) are able to induce significant quaternary changes. Furthermore, our results demonstrated, by a Quantum Mechanics method, the influence of SARS-CoV-2 Mpro mutations on the catalytic mechanism, confirming that only one of the chains of the WT and mutant SARS-CoV-2 Mpros are prone to cleave substrates. Finally, it was also possible to identify the aa residue F140 as an important factor related to the increasing enzymatic reactivity of a significant number of SARS-CoV-2 Mpro conformations generated by the normal modes-based simulations.

Gasparini P, Philot E A, Pantaleão S Q, Torres-Bonfim N E S M, Kliousoff A, Quiroz R C N, Perahia D, Simões R P, Magro A J, Scott A L

2023-Feb-22

Main protease, Molecular dynamics, Mutation, Normal modes, Quantum mechanics, Residue F140, SARS-CoV-2, Structural dynamics

oncology Oncology

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals.

In Journal of biophotonics

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detecting seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built a classification models based on principal component analysis (PCA) associated with linear discriminant analysis (LDA) and artificial neural network (ANN). Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA. This article is protected by copyright. All rights reserved.

Karas Boris Yu, Sitnikova Vera E, Nosenko Tatiana, Dedkov Vladimir G, Arsentieva Natalia A, Gavrilenko Natalia V, Moiseev Ivan S, Totolian Areg A, Kajava Andrey V, Uspenskaya Mayya V

2023-Mar-03

ANN, ATR-FTIR spectroscopy, COVID-19, PCA-LDA, chemometric, plasma, sepsis

General General

Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU.

In Artificial intelligence in medicine ; h5-index 34.0

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.

Gani Md Osman, Kethireddy Shravan, Adib Riddhiman, Hasan Uzma, Griffin Paul, Adibuzzaman Mohammad

2023-Mar

Causal inference, Critical care, Expert augmented knowledge, Oxygen therapy, Structural causal model

General General

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.

In Artificial intelligence in medicine ; h5-index 34.0

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments. Based on this review, the use of an ensemble of ML algorithms that analyze clinical and biological data (i.e., plasma proteomics) of COVID-19 patients is designed and deployed to evaluate the potential use of AI for early COVID-19 patient triage. The proposed pipeline is evaluated using three publicly available datasets for training and testing. Three ML "tasks" are defined, and several algorithms are tested through a hyperparameter tuning method to identify the highest-performance models. As overfitting is one of the typical pitfalls for such approaches (mainly due to the size of the training/validation datasets), a variety of evaluation metrics are used to mitigate this risk. In the evaluation procedure, recall scores ranged from 0.6 to 0.74 and F1-score from 0.62 to 0.75. The best performance is observed via Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms. Additionally, input data (proteomics and clinical data) were ranked based on corresponding Shapley additive explanation (SHAP) values and evaluated for their prognosticated capacity and immuno-biological credence. This "interpretable" approach revealed that our ML models could discern critical COVID-19 cases predominantly based on patient's age and plasma proteins on B cell dysfunction, hyper-activation of inflammatory pathways like Toll-like receptors, and hypo-activation of developmental and immune pathways like SCF/c-Kit signaling. Finally, the herein computational workflow is corroborated in an independent dataset and MLP superiority along with the implication of the abovementioned predictive biological pathways are corroborated. Regarding limitations of the presented ML pipeline, the datasets used in this study contain less than 1000 observations and a significant number of input features hence constituting a high-dimensional low-sample (HDLS) dataset which could be sensitive to overfitting. An advantage of the proposed pipeline is that it combines biological data (plasma proteomics) with clinical-phenotypic data. Thus, in principle, the presented approach could enable patient triage in a timely fashion if used on already trained models. However, larger datasets and further systematic validation are needed to confirm the potential clinical value of this approach. The code is available on Github: https://github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics.

Dimitsaki Stella, Gavriilidis George I, Dimitriadis Vlasios K, Natsiavas Pantelis

2023-Mar

Artificial intelligence, COVID-19, Forecasting, Machine Learning, Severity prediction

General General

The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests.

In Artificial intelligence in medicine ; h5-index 34.0

Symbolic learning is the logic-based approach to machine learning, and its mission is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. Interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. In order to improve their performances, interval temporal decision trees can be embedded into interval temporal random forests, mimicking the corresponding schema at the propositional level. In this article we consider a dataset of cough and breath sample recordings of volunteer subjects, labeled with their COVID-19 status, originally collected by the University of Cambridge. By interpreting such recordings as multivariate time series, we study the problem of their automated classification using interval temporal decision trees and forests. While this problem has been approached with the same dataset as well as with other datasets, in all cases, non-symbolic learning methods (usually, deep learning-based) have been applied to solve it; in this article we apply a symbolic approach, and show that it does not only outperform the state-of-the-art obtained with the same dataset, but its results are also superior to those of most non-symbolic techniques applied on other datasets. As an added bonus, thanks to the symbolic nature of our approach, we are also able to extract explicit knowledge to help physicians characterize typical COVID-positive cough and breath.

Manzella F, Pagliarini G, Sciavicco G, Stan I E

2023-Mar

COVID-19, Interval temporal decision trees and forests, Symbolic models

General General

The effect of COVID-19 on self-reported safety incidents in aviation: An examination of the heterogeneous effects using causal machine learning.

In Journal of safety research

INTRODUCTION : Disruptions to aviation operations occur daily on a micro-level with negligible impacts beyond the inconvenience of rebooking and changing aircrew schedules. The unprecedented disruption in global aviation due to COVID-19 highlighted a need to evaluate emergent safety issues rapidly.

METHOD : This paper uses causal machine learning to examine the heterogeneous effects of COVID-19 on reported aircraft incursions/excursions. The analysis utilized self report data from NASA Aviation Safety Reporting System collected from 2018 to 2020. The report attributes include self identified group characteristics and expert categorization of factors and outcomes. The analysis identified attributes and subgroup characteristics that were most sensitive to COVID-19 in inducing incursions/excursions. The method included the generalized random forest and difference-in-difference techniques to explore causal effects.

RESULTS : The analysis indicates first officers are more prone to experiencing incursion/excursion events during the pandemic. In addition, events categorized with the human factors confusion, distraction, and the causal factor fatigue increased incursion/excursion events.

PRACTICAL APPLICATIONS : Understanding the attributes associated with the likelihood of incursion/excursion events provides policymakers and aviation organizations insights to improve prevention mechanisms for future pandemics or extended periods of reduced aviation operations.

Choi Youngran, Gibson James R

2023-Feb

Aviation incursions/excursions, COVID-19, Heterogeneous treatment effects, Machine learning

Internal Medicine Internal Medicine

A Computational Approach in the Diagnostic Process of COVID-19: The Missing Link between the Laboratory and Emergency Department.

In Frontiers in bioscience (Landmark edition)

BACKGROUND : The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the right evaluation of viral infection. According to the Centers for Disease Control and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in respiratory samples is the gold standard for confirming the disease. However, it has practical limitations as time-consuming procedures and a high rate of false-negative results. We aim to assess the accuracy of COVID-19 classifiers based on Arificial Intelligence (AI) and statistical classification methods adapted on blood tests and other information routinely collected at the Emergency Departments (EDs).

METHODS : Patients admitted to the ED of Careggi Hospital from April 7th-30th 2020 with pre-specified features of suspected COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19 likely/unlikely case, based on clinical features and bedside imaging support. Considering the limits of each method to identify a case of COVID-19, further evaluation was performed after an independent clinical review of 30-day follow-up data. Using this as a gold standard, several classifiers were implemented: Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor (K-NN), Naive Bayes (NB).

RESULTS : Most of the classifiers show a ROC >0.80 on both internal and external validation samples but the best results are obtained applying RF, LR and NN. The performance from the external validation sustains the proof of concept to use such mathematical models fast, robust and efficient for a first identification of COVID-19 positive patients. These tools may constitute both a bedside support while waiting for RT-PCR results, and a tool to point to a deeper investigation, by identifying which patients are more likely to develop into positive cases within 7 days.

CONCLUSIONS : Considering the obtained results and with a rapidly changing virus, we believe that data processing automated procedures may provide a valid support to the physicians facing the decision to classify a patient as a COVID-19 case or not.

Lanzilao Luisa, Mariniello Antonella, Polenzani Bianca, Aldinucci Alessandra, Nazerian Peiman, Prota Alessio, Grifoni Stefano, Tonietti Barbara, Neri Chiara, Turco Livia, Fanelli Alessandra, Amedei Amedeo, Stanghellini Elena

2023-Feb-22

COVID-19, automated classifiers, diagnosis, laboratory medicine, machine learning, “physicians gestalt”

General General

The future of automated infection detection: Innovation to transform practice (Part III/III).

In Antimicrobial stewardship & healthcare epidemiology : ASHE

Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.

Branch-Elliman Westyn, Sundermann Alexander J, Wiens Jenna, Shenoy Erica S

2023

Radiology Radiology

Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans.

In PloS one ; h5-index 176.0

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.

Khademi Sadaf, Heidarian Shahin, Afshar Parnian, Enshaei Nastaran, Naderkhani Farnoosh, Rafiee Moezedin Javad, Oikonomou Anastasia, Shafiee Akbar, Babaki Fard Faranak, Plataniotis Konstantinos N, Mohammadi Arash

2023

Public Health Public Health

Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.

In Frontiers in public health

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.

Janko Vito, Reščič Nina, Vodopija Aljoša, Susič David, De Masi Carlo, Tušar Tea, Gradišek Anton, Vandepitte Sophie, De Smedt Delphine, Javornik Jana, Gams Matjaž, Luštrek Mitja

2023

COVID-19, epidemiological modeling, intervention plans, machine learning, multi-objective optimization

Public Health Public Health

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

In Frontiers in public health

PURPOSE : The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery.

METHODS : First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data.

RESULTS : Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826-0.851] and 0.922 [95% CI: 0.913-0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911-0.925] and 0.960 [95% CI: 0.955-0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12-2.15 s across three devices tested.

CONCLUSION : DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Yang Lily Wei Yun, Ng Wei Yan, Lei Xiaofeng, Tan Shaun Chern Yuan, Wang Zhaoran, Yan Ming, Pargi Mohan Kashyap, Zhang Xiaoman, Lim Jane Sujuan, Gunasekeran Dinesh Visva, Tan Franklin Chee Ping, Lee Chen Ee, Yeo Khung Keong, Tan Hiang Khoon, Ho Henry Sun Sien, Tan Benedict Wee Bor, Wong Tien Yin, Kwek Kenneth Yung Chiang, Goh Rick Siow Mong, Liu Yong, Ting Daniel Shu Wei

2023

Artificial Intelligence, COVID-19, Natural Language Processing, conversational chatbot, health education, pandemic education

General General

Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset.

In Scientific reports ; h5-index 158.0

The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48-71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

de Paiva Bruno Barbosa Miranda, Pereira Polianna Delfino, de Andrade Claudio Moisés Valiense, Gomes Virginia Mara Reis, Souza-Silva Maira Viana Rego, Martins Karina Paula Medeiros Prado, Sales Thaís Lorenna Souza, de Carvalho Rafael Lima Rodrigues, Pires Magda Carvalho, Ramos Lucas Emanuel Ferreira, Silva Rafael Tavares, de Freitas Martins Vieira Alessandra, Nunes Aline Gabrielle Sousa, de Oliveira Jorge Alzira, de Oliveira Maurílio Amanda, Scotton Ana Luiza Bahia Alves, da Silva Carla Thais Candida Alves, Cimini Christiane Corrêa Rodrigues, Ponce Daniela, Pereira Elayne Crestani, Manenti Euler Roberto Fernandes, Rodrigues Fernanda d’Athayde, Anschau Fernando, Botoni Fernando Antônio, Bartolazzi Frederico, Grizende Genna Maira Santos, Noal Helena Carolina, Duani Helena, Gomes Isabela Moraes, Costa Jamille Hemétrio Salles Martins, di Sabatino Santos Guimarães Júlia, Tupinambás Julia Teixeira, Rugolo Juliana Machado, Batista Joanna d’Arc Lyra, de Alvarenga Joice Coutinho, Chatkin José Miguel, Ruschel Karen Brasil, Zandoná Liege Barella, Pinheiro Lílian Santos, Menezes Luanna Silva Monteiro, de Oliveira Lucas Moyses Carvalho, Kopittke Luciane, Assis Luisa Argolo, Marques Luiza Margoto, Raposo Magda Cesar, Floriani Maiara Anschau, Bicalho Maria Aparecida Camargos, Nogueira Matheus Carvalho Alves, de Oliveira Neimy Ramos, Ziegelmann Patricia Klarmann, Paraiso Pedro Gibson, de Lima Martelli Petrônio José, Senger Roberta, Menezes Rochele Mosmann, Francisco Saionara Cristina, Araújo Silvia Ferreira, Kurtz Tatiana, Fereguetti Tatiani Oliveira, de Oliveira Thainara Conceição, Ribeiro Yara Cristina Neves Marques Barbosa, Ramires Yuri Carlotto, Lima Maria Clara Pontello Barbosa, Carneiro Marcelo, Bezerra Adriana Falangola Benjamin, Schwarzbold Alexandre Vargas, de Moura Costa André Soares, Farace Barbara Lopes, Silveira Daniel Vitorio, de Almeida Cenci Evelin Paola, Lucas Fernanda Barbosa, Aranha Fernando Graça, Bastos Gisele Alsina Nader, Vietta Giovanna Grunewald, Nascimento Guilherme Fagundes, Vianna Heloisa Reniers, Guimarães Henrique Cerqueira, de Morais Julia Drumond Parreiras, Moreira Leila Beltrami, de Oliveira Leonardo Seixas, de Deus Sousa Lucas, de Souza Viana Luciano, de Souza Cabral Máderson Alvares, Ferreira Maria Angélica Pires, de Godoy Mariana Frizzo, de Figueiredo Meire Pereira, Guimarães-Junior Milton Henriques, de Paula de Sordi Mônica Aparecida, da Cunha Severino Sampaio Natália, Assaf Pedro Ledic, Lutkmeier Raquel, Valacio Reginaldo Aparecido, Finger Renan Goulart, de Freitas Rufino, Guimarães Silvana Mangeon Meirelles, Oliveira Talita Fischer, Diniz Thulio Henrique Oliveira, Gonçalves Marcos André, Marcolino Milena Soriano

2023-Mar-01

General General

Biases associated with database structure for COVID-19 detection in X-ray images.

In Scientific reports ; h5-index 158.0

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

Arias-Garzón Daniel, Tabares-Soto Reinel, Bernal-Salcedo Joshua, Ruz Gonzalo A

2023-Mar-01

General General

Speaking with mask in the COVID-19 era: Multiclass machine learning classification of acoustic and perceptual parameters.

In The Journal of the Acoustical Society of America

The intensive use of personal protective equipment often requires increasing voice intensity, with possible development of voice disorders. This paper exploits machine learning approaches to investigate the impact of different types of masks on sustained vowels /a/, /i/, and /u/ and the sequence /a'jw/ inside a standardized sentence. Both objective acoustical parameters and subjective ratings were used for statistical analysis, multiple comparisons, and in multivariate machine learning classification experiments. Significant differences were found between mask+shield configuration and no-mask and between mask and mask+shield conditions. Power spectral density decreases with statistical significance above 1.5 kHz when wearing masks. Subjective ratings confirmed increasing discomfort from no-mask condition to protective masks and shield. Machine learning techniques proved that masks alter voice production: in a multiclass experiment, random forest (RF) models were able to distinguish amongst seven masks conditions with up to 94% validation accuracy, separating masked from unmasked conditions with up to 100% validation accuracy and detecting the shield presence with up to 86% validation accuracy. Moreover, an RF classifier allowed distinguishing male from female subject in masked conditions with 100% validation accuracy. Combining acoustic and perceptual analysis represents a robust approach to characterize masks configurations and quantify the corresponding level of discomfort.

Calà F, Manfredi C, Battilocchi L, Frassineti L, Cantarella G

2023-Feb

General General

Using machine learning models to predict the willingness to carry lightweight goods by bike and kick-scooter.

In Transportation research interdisciplinary perspectives

The social transformation caused by the COVID-19 pandemic can help cities become healthier and more sustainable, with more space for active modes of transportation. This research addresses people's willingness to go shopping by bike or kick-scooter and to transport lightweight goods in cities with low maturity for cycling and scooting. Data collection was based on a survey, applied in the two largest cities of Brazil (São Paulo and Rio de Janeiro) and Portugal (Lisbon and Porto). The dataset was processed considering only two categories of respondents (i.e., potential users and regular users) and then four machine learning models (K-Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest) were applied to predict shopping by bike or kick-scooter. In terms of all performance measures, the Support Vector Machine model was the optimum. The results indicate that people are willing to transport lightweight goods by bike or kick-scooter, as long as the infrastructure is safe and comfortable. This research contributes to understanding mobility behavior changes and identifying barriers to going shopping by bike or kick-scooter. It also presents some policy recommendations for improving cycling and scooting use for shopping, which public authorities can carry out.

Silveira-Santos Tulio, Manuel Vassallo Jose, Torres Ewerton

2022-Mar

Behavioral change, COVID-19, Classifier model, Machine learning, Shopping trips, Urban cycling and scooting

General General

The role of digital social innovations to address SDGs: A systematic review.

In Environment, development and sustainability

The impact of the COVID-19 pandemic has increased the search for solutions to social problems associated with the Sustainable Development Goals (SDGs). Main actors are turning to Digital Social Innovations (DSIs), defined as collaborative innovations where enterprises, users and communities collaborate using digital technologies to promote solutions at scale and speed, connecting innovation, the social world and digital ecosystems to reach the 2030 Agenda. This study aims to identify how digital transformations and social innovations solve social problems and address SDGs. We conducted a systematic review based on a sample of 45 peer-reviewed articles published from 2010 to 2022, combining a bibliometric study and a content analysis focusing on opportunities and threats impacting these fields. We observed the spread and increasing use of technologies associated with all 17 SDGs, specially blockchain, IoT, artificial intelligence, and autonomous robots that are increasing their role and presence exponentially, completely changing the current way of doing things, offering a dramatic evolution in many different segments, such as health care, smart cities, agriculture, and the combat against poverty and inequalities. We identified many threats concerning ethics, especially with the increased use of public data, and concerns about the impacts on the labor force and the possible instability and impact it may cause in low skill/low pay jobs. We expect that our findings advance the concept of digital social innovations and the benefits of its adoption to promote social advancements.

Dionisio Marcelo, de Souza Junior Sylvio Jorge, Paula Fábio, Pellanda Paulo César

2023-Feb-23

Digital transformation, Industry 4.0, Social innovation, Systematic literature review, UN sustainable goals

General General

TransCode: Uncovering COVID-19 transmission patterns via deep learning.

In Infectious diseases of poverty ; h5-index 31.0

BACKGROUND : The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

METHODS : We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available.

RESULTS : The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.

CONCLUSIONS : The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

Ren Jinfu, Liu Mutong, Liu Yang, Liu Jiming

2023-Feb-28

COVID-19, Deep transfer learning, Densely populated regions, Human mobility and contact behaviors, Meta-population, Spatiotemporal transmission dynamics and heterogeneity, TransCode

General General

New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning.

In Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.

Ceccon Denny M, Amaral Paulo Henrique R, Andrade Lídia M, da Silva Maria I N, Andrade Luis A F, Moraes Thais F S, Bagno Flavia F, Rocha Raissa P, de Almeida Marques Daisymara Priscila, Ferreira Geovane Marques, Lourenço Alice Aparecida, Ribeiro Ágata Lopes, Coelho-Dos-Reis Jordana G A, da Fonseca Flavio G, Gonzalez J C

2023-Feb-28

Artificial intelligence, COVID-19, Label-free diagnosis, Machine learning, Optical spectroscopy

General General

Open Science Discovery of Potent Non-Covalent SARS-CoV-2 Main Protease Inhibitors

bioRxiv Preprint

The COVID-19 pandemic was a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Pandemics could be prevented in principle by accessible, easily deployable broad-spectrum oral antivirals. Here we report the results of the COVID Moonshot, a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical series that is differentiated from current Mpro inhibitors in that it maintains a new non-covalent, non-peptidic scaffold with nanomolar potency. Our approach leveraged crowdsourcing, high-throughput structural biology, machine learning, and exascale molecular simulations and high-throughput chemistry. In the process, we generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>840 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

The COVID Moonshot Consortium, ; Achdout, H.; Aimon, A.; Alonzi, D. S.; Arbon, R.; Bar-David, E.; Barr, H.; Ben-Shmuel, A.; Bennett, J.; Bilenko, V. A.; Bilenko, V. A.; Boby, M. L.; Borden, B.; Boulet, P.; Bowman, G. R.; Brun, J.; Brwewitz, L.; BVNBS, S.; Calmiano, M.; Carbery, A.; Carney, D.; Cattermole, E.; Chang, E.; Chernyshenko, E.; Chodera, J. D.; Clyde, A.; Coffland, J. E.; Cohen, G.; Cole, J.; Contini, A.; Cox, L.; Croll, T. I.; Cvitkovic, M.; Dias, A.; Donckers, K.; Dotson, D. L.; Douangamath, A.; Duberstein, S.; Dudgeon, T.; Dunnett, L.; Eastman, P. K.; Erez, N.; Eyermann, C. J.; Fa

2023-03-02

Surgery Surgery

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine.

In The EPMA journal

BACKGROUND : Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM).

METHODS : Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified.

RESULTS : Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FCij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients.

CONCLUSION : The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log2fold change (log2FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM.

SUPPLEMENTARY INFORMATION : The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

Zhu Kun, Chen Zhonghua, Xiao Yi, Lai Dengming, Wang Xiaofeng, Fang Xiangming, Shu Qiang

2023-Feb-21

COVID-19, CSF1R, Immune cells, Machine learning, Monocytes, Nomogram, PI16, Predictive Preventive Personalized medicine (PPPM / 3PM), Predictive model, Triage

Public Health Public Health

Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework.

In Computational economics

This paper seeks to identify the causal impact of educational human capital on social distancing behavior at workplace in Turkey using district-level data for the period of April 2020 - February 2021. We adopt a unified causal framework, predicated on domain knowledge, theory-justified constraints anda data-driven causal structure discovery using causal graphs. We answer our causal query by employing machine learning prediction algorithms; instrumental variables in the presence of latent confounding and Heckman's model in the presence of selection bias. Results show that educated regions are able to distance-work and educational human capital is a key factor in reducing workplace mobility, possibly through its impact on employment. This pattern leads to higher workplace mobility for less educated regions and translates into higher Covid-19 infection rates. The future of the pandemic lies in less educated segments of developing countries and calls for public health action to decrease its unequal and pervasive impact.

Bilgel Fırat, Karahasan Burhan Can

2023-Feb-21

Causal structure discovery, Do-calculus, Instrumental variables, Machine learning, Sample selection, Workplace mobility

General General

Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network.

In Journal of healthcare engineering

The infectious coronavirus disease (COVID-19) has become a great threat to global human health. Timely and rapid detection of COVID-19 cases is very crucial to control its spreading through isolation measures as well as for proper treatment. Though the real-time reverse transcription-polymerase chain reaction (RT-PCR) test is a widely used technique for COVID-19 infection, recent researches suggest chest computed tomography (CT)-based screening as an effective substitute in cases of time and availability limitations of RT-PCR. In consequence, deep learning-based COVID-19 detection from chest CT images is gaining momentum. Furthermore, visual analysis of data has enhanced the opportunities of maximizing the prediction performance in this big data and deep learning realm. In this article, we have proposed two separate deformable deep networks converting from the conventional convolutional neural network (CNN) and the state-of-the-art ResNet-50, to detect COVID-19 cases from chest CT images. The impact of the deformable concept has been observed through performance comparative analysis among the designed deformable and normal models, and it is found that the deformable models show better prediction results than their normal form. Furthermore, the proposed deformable ResNet-50 model shows better performance than the proposed deformable CNN model. The gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions' localization effort at the final convolutional layer and has been found excellent. Total 2481 chest CT images have been used to evaluate the performance of the proposed models with a train-valid-test data splitting ratio of 80 : 10 : 10 in random fashion. The proposed deformable ResNet-50 model achieved training accuracy of 99.5% and test accuracy of 97.6% with specificity of 98.5% and sensitivity of 96.5% which are satisfactory compared with related works. The comprehensive discussion demonstrates that the proposed deformable ResNet-50 model-based COVID-19 detection technique can be useful for clinical applications.

Foysal Md, Hossain A B M Aowlad, Yassine Abdulsalam, Hossain M Shamim

2023

General General

Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network.

In Environmental development

The need for stability in the economy for world development has been a challenge due to the COVID-19 pandemic. In addition, the increase of natural disasters and their aftermath have been increasing causing damages to infrastructure, the economy, livelihood, and lives in general. This study aimed to determine factors affecting the intention to donate for victims of Typhoon Odette, a recent super typhoon that hit the Philippines leading to affect 38 out of 81 provinces of the most natural disaster-prone countries. Determining the most significant factor affecting the intention to donate may help in increasing the engagement of donations among other people to help establish a more stable economy to heighten world development. With the use of deep learning neural network, a 97.12% accuracy was obtained for the classification model. It could be deduced that when donors understand and perceive both severity and vulnerability to be massive and highly damaging, then a more positive intention to donate to victims of typhoons will be observed. In addition, the influence of other people, the holiday season when the typhoon happened, and the media as a platform have greatly contributed to heightening the intention to donate and control over the donor's behavior. The findings of this study could be applied and utilized by government agencies and donation platforms to help engage and promote communication among donors. Moreover, the framework and methodology considered in this study may be extended to evaluate intention, natural disasters, and behavioral studies worldwide.

German Josephine D, Ong Ardvin Kester S, Redi Anak Agung Ngurah Perwira, Prasetyo Yogi Tri, Robas Kirstien Paola E, Nadlifatin Reny, Chuenyindee Thanatorn

2023-Mar

Deep learning neural network, Donation, Natural disaster, Typhoon odette, Typhoon victims

General General

Emerging trends in point-of-care biosensing strategies for molecular architectures and antibodies of SARS-CoV-2.

In Biosensors & bioelectronics: X

COVID-19, a highly contagious viral infection caused by the occurrence of severe acute respiratory syndrome coronavirus (SARS-CoV-2), has turned out to be a viral pandemic then ravaged many countries worldwide. In the recent years, point-of-care (POC) biosensors combined with state-of-the-art bioreceptors, and transducing systems enabled the development of novel diagnostic tools for rapid and reliable detection of biomarkers associated with SARS-CoV-2. The present review thoroughly summarises and discusses various biosensing strategies developed for probing SARS-CoV-2 molecular architectures (viral genome, S Protein, M protein, E protein, N protein and non-structural proteins) and antibodies as a potential diagnostic tool for COVID-19. This review discusses the various structural components of SARS-CoV-2, their binding regions and the bioreceptors used for recognizing the structural components. The various types of clinical specimens investigated for rapid and POC detection of SARS-CoV-2 is also highlighted. The importance of nanotechnology and artificial intelligence (AI) approaches in improving the biosensor performance for real-time and reagent-free monitoring the biomarkers of SARS-CoV-2 is also summarized. This review also encompasses existing practical challenges and prospects for developing new POC biosensors for clinical monitoring of COVID-19.

Karuppaiah Gopi, Vashist Arti, Nair Madhavan, Veerapandian Murugan, Manickam Pandiaraj

2023-May

Biomarkers, COVID-19, Electrochemical and optical biosensors, Infectious diseases, Nanobiosensors

General General

Exploration of SARS-CoV-2 Mpro Noncovalent Natural Inhibitors Using Structure-Based Approaches.

In ACS omega

With the emergence of antibody-evasive omicron subvariants (BA.2.12.1, BA.4, and BA.5), which can compromise the efficacy of vaccination, it is of utmost importance to widen the finite therapeutic options for COVID-19. Although more than 600 co-crystal complexes of Mpro with inhibitors have been revealed, utilizing them to search for novel Mpro inhibitors remains limited. Although there were two major groups of Mpro inhibitors, covalent and noncovalent inhibitors, noncovalent inhibitors were our main focus due to the safety concerns with their covalent counterparts. Hence, this study aimed to explore Mpro noncovalent inhibition ability of phytochemicals extracted from Vietnamese herbals by combining multiple structure-based approaches. By closely inspecting 223 complexes of Mpro with noncovalent inhibitors, a 3D-pharmacophore model representing typical chemical features of Mpro noncovalent inhibitors was generated with good validation scores (sensitivity = 92.11%, specificity = 90.42%, accuracy = 90.65%, and goodness-of-hit score = 0.61). Afterward, the pharmacophore model was applied to explore the potential Mpro inhibitors from our in-house Vietnamese phytochemical database, revealing 18 substances, 5 of which were in vitro assayed. The remaining 13 substances were then examined by induced-fit molecular docking, revealing 12 suitable compounds. A machine-learning activity prediction model was developed to rank the hit, suggesting nigracin and calycosin-7-O-β-glucopyranoside as promising Mpro natural noncovalent inhibitors.

Duong Cuong Quoc, Nguyen Phuong Thuy Viet

2023-Feb-21

General General

SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach.

In SN computer science

UNLABELLED : SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naïve Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods.

SUPPLEMENTARY INFORMATION : The online version contains supplementary material available at 10.1007/s42979-023-01703-6.

Jeyananthan Pratheeba

2023

COVID-19 diagnosis, Differently expressed genes, Feature selection, GO analysis, Machine learning models, Transcriptome data

Public Health Public Health

Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification.

In JMIR infodemiology

BACKGROUND : COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation.

OBJECTIVE : We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA).

METHODS : We collected tweets using CBD- and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives.

RESULTS : We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets.

CONCLUSIONS : This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.

Turner Jason, Kantardzic Mehmed, Vickers-Smith Rachel, Brown Andrew G

2023

COVID-19, Twitter, cannabidiol, content analysis, deep learning, health information, infodemic, infodemiology, language model, misinformation, pandemic, sentence vector, social media, transformer

General General

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification.

In Neural networks : the official journal of the International Neural Network Society

The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The "MSID" dataset, short form of "Monkeypox Skin Images Dataset", which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.

Bala Diponkor, Hossain Md Shamim, Hossain Mohammad Alamgir, Abdullah Md Ibrahim, Rahman Md Mizanur, Manavalan Balachandran, Gu Naijie, Islam Mohammad S, Huang Zhangjin

2023-Feb-22

Classification, Convolutional neural network, Dataset, Deep learning, Machine learning, Monkeypox disease

General General

How to avoid a local epidemic becoming a global pandemic.

In Proceedings of the National Academy of Sciences of the United States of America

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Stenseth Nils Chr, Schlatte Rudolf, Liu Xiaoli, Pielke Roger, Li Ruiyun, Chen Bin, Bjørnstad Ottar N, Kusnezov Dimitri, Gao George F, Fraser Christophe, Whittington Jason D, Bai Yuqi, Deng Ke, Gong Peng, Guan Dabo, Xiao Yixiong, Xu Bing, Johnsen Einar Broch

2023-Mar-07

Disease X, coupled simulation model, data science, digital twin model, epidemiology

General General

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence.

In PloS one ; h5-index 176.0

This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students' performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students' marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students' wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement.

Subirats Laia, Palacios Corral Aina, Pérez-Ruiz Sof Ia, Fort Santi, Sacha Go Mez-Mon Ivas

2023

Internal Medicine Internal Medicine

Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma.

In Neural computing & applications

When the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.

Kurt Zuhal, Işık Şahin, Kaya Zeynep, Anagün Yıldıray, Koca Nizameddin, Çiçek Sümeyye

2023-Feb-20

COVID-19 detection, CT scan, Deep learning, EfficientNet, K-means, Lung parenchyma

Public Health Public Health

Impact of human mobility on COVID-19 transmission according to mobility distance, locations and demographic factors in the Greater Bay area of China：a population-based study.

In JMIR public health and surveillance

BACKGROUND : Mobility restriction is one of the primary measures used to restrain the spread of COVID-19 in the pandemic all over the world. Governments implemented and relaxed various mobility restriction measures in the absence of evidence for almost three years, which caused severe adverse outcomes in health, society and economy.

OBJECTIVE : This study aims to quantify the impact of mobility reduction on COVID-19 transmission according to mobility distance, locations and demographic factors to identify hotspots of transmission and guide public health policies.

METHODS : Millions of the anonymized, aggregated mobile phone position data between Jan 1 and Feb 24, 2020 was collected for the nine mega cities Greater Bay Area (GBA), China. A generalized linear model (GLM) was established to test the association between mobility volume (number of trips) and COVID-19 transmission. Subgroups analysis was also performed for sex, age, travel locations and travel distance. The statistical interaction terms were included in a variety of models that express different relations between the involved variables.

RESULTS : The GLM analysis demonstrated a significant association between the COVID-19 growth rate ratio (GR) and mobility volume. A stratification analysis revealed a higher effect of mobility volume on the COVID-19 growth rate ratio (GR) among people aged 50-59 years (a decrease of 13.17% for GR per 10% reduction of mobility volume for persons 50-59 years, P <.001) than for other age groups (a decrease of 7.80%, 10.43%, 7.48%, 8.01%, 10.43% for age groups of ≤18, 19-29, 30-39, 40-49, ≥ 60 years, respectively, Pinteraction=.024). The impact of mobility reduction on COVID-19 transmission was higher in transit stations and shopping areas: a decrease of 0.67, 0.53, 0.30, 0.37, 0.44, 0.32 for instantaneous reproduction number R(t) per 10% reduction in mobility volume to transit stations, shopping, work, school, recreation, and other locations, separately (Pinteraction=.016). The association between reduction in mobility volume and COVID-19 transmission was lower with decreasing mobility distance as there was significant interaction between mobility volume and mobility distance on R(t) (Pinteraction<.001). Specifically, the R(t) reduced by 11.97% per 10% reduction of mobility volume when the mobility distance increased to 110% (Spring Festival), by 6.74% when distance remained unchanged and by 1.52% when the distance decreased to 90%.

CONCLUSIONS : The association between mobility reduction and COVID-19 transmission significantly varied by mobility distance, locations and age. The substantially higher impact of mobility volume on COVID-19 transmission in longer travel distance, certain age groups, and for specific travel destinations highlights the potential to optimize the effectiveness of mobility restriction strategies. The results from our study demonstrate the power of having a mobility network using mobile phone data for surveillance that monitor movement at a detailed level to measure the potential impacts of future pandemics.

Xia Jizhe, Yin Kun, Yue Yang, Li Qingquan, Wang Xiling, Hu Dongsheng, Wang Xiong, Du Zhanwei, Cowling Ben J, Chen Erzhen, Zhou Ying

2023-Feb-23

Ophthalmology Ophthalmology

Glaucoma and Telemedicine.

In Journal of glaucoma

The coronavirus disease 2019 (COVID-19) pandemic drastically impacted global health, forcing institutions to provide alternative models of safe and reliable health care. In this context, telemedicine has been successfully used to overcome distance barriers and improve access to medical services. Teleglaucoma is the application of telemedicine to screen and monitor glaucoma, a chronic and progressive optic neuropathy. Teleglaucoma screening aims to detect the disease at an earlier stage, especially in high-risk populations and underserved areas, also identifying patients who require more urgent treatment. Teleglaucoma monitoring seeks to provide remote management through virtual clinics, where classical in-person visits are replaced by synchronous data collection (clinical measurements) performed by non-ophthalmologists and asynchronous review (decision-making) by ophthalmologists. This may be employed for low-risk patients with early disease, improving health care logistics, reducing number of face-to-face consultations, and saving time and costs. New technologies may also allow home monitoring of patients in teleglaucoma programs, with addition of artificial intelligence methods, which are expected to increase accuracy of remote glaucoma screening/ monitoring, and to support clinical decision-making. However, for incorporation of teleglaucoma into clinical practice, a complex system for collection, transfer, flow, and interpretation of data is still necessary, in addition to clearer regulatory markers by government agencies and medical entities.

Brandão-de-Resende Camilo, de Alcântara Liliane de Abreu Rosa, Vasconcelos-Santos Daniel Vítor, Diniz-Filho Alberto

2023-Feb-28

Public Health Public Health

Artificial Intelligence Functionalities During the COVID-19 Pandemic.

In Disaster medicine and public health preparedness

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has led us to use virtual solutions and emerging technologies such as artificial intelligence (AI). Recent studies have clearly demonstrated the role of AI in health care and medical practice; however, a comprehensive review can identify potential yet not fulfilled functionalities of such technologies in pandemics. Therefore, this scoping review study aims at assessing AI functionalities in the COVID-19 pandemic in 2022.

METHODS : A systematic search was carried out in PubMed, Cochran Library, Scopus, Science Direct, ProQuest, and Web of Science from 2019 to May 9, 2022. Researchers selected the articles according to the search keywords. Finally, the articles mentioning the functionalities of AI in the COVID-19 pandemic were evaluated. Two investigators performed this process.

RESULTS : Initial search resulted in 9123 articles. After reviewing the title, abstract, and full text of these articles, and applying the inclusion and exclusion criteria, 4 articles were selectd for the final analysis. All 4 were cross-sectional studies. Two studies (50%) were performed in the United States, 1 (25%) in Israel, and 1 (25%) in Saudi Arabia. They covered the functionalities of AI in the prediction, detection, and diagnosis of COVID-19.

CONCLUSIONS : To the extent of the researchers' knowledge, this study is the first scoping review that assesses the AI functionalities in the COVID-19 pandemic. Health-care organizations need decision support technologies and evidence-based apparatuses that can perceive, think, and reason not dissimilar to human beings. Potential functionalities of such technologies can be used to predict mortality, detect, screen, and trace current and former patients, analyze health data, prioritize high-risk patients, and better allocate hospital resources in pandemics, and generally in health-care settings.

Ahmadi Marzaleh Milad, Peyravi Mahmoudreza, Mousavi Shahrokh, Sarpourian Fatemeh, Seyedi Milad, Shalyari Naseh

2023-Feb-27

COVID-19, artificial intelligence, deep learning, machine learning, neural networks

Public Health Public Health

Can we predict critical care mortality with non-conventional inflammatory markers in SARS-CoV-2 infected patients?

In Clinical hemorheology and microcirculation

BACKGROUND : Severe COVID-19 disease is associated with multiple organ involvement,then failure and often fatal outcomes.In addition,inflammatory mechanisms and cytokine storms,documented in many COVID-19 patients,are responsible for the progression of the disease and high mortality rates.Inflammatory parameters,such as procalcitonin(PCT) and C-reactive protein(CRP), are widely used in clinical practice.

OBJECTIVE : To evaluate the predictive power of non-conventional inflammatory markers regarding mortality risk.

METHODS : In our prospective study 52 patients were followed for 5 days after admission to an intensive care unit immediately with severe SARS-CoV-2 infection.We compared leukocyte-,platelet antisedimentation rate (LAR, PAR),neutrophil lymphocyte ratio(NLR), CRP, PCT levels.

RESULTS : In non-surviving(NSU) patients LAR remained largely constant from D1 to D4 with a statistically significant drop(p <  0.05) only seen on D5.The NSU group showed statistically significant(p <  0.05) elevated LAR medians on D4 and D5, compared to the SU group.NLR values were continually higher in the non-survivor group.The difference between the SU and NSU groups were statistically significant on every examined day.PAR, CRP and PCT levels didn't show any significant differences between the SU and NSU groups.

CONCLUSIONS : In conclusion, this study suggests that LAR and NLR are especially worthy of further investigation as prognostic markers.LAR might be of particular relevance as it is not routinely obtained in current clinical practice.It would seem beneficial to include LAR in data sets to train prognostic artificial intelligence.

Rozanovic Martin, Domokos Kamilla, Márovics Gergő, Rohonczi Mirtill, Csontos Csaba, Bogár Lajos, Rendeki Szilárd, Kiss Tamás, Rozanovic Melánia Nacira, Loibl Csaba

2023-Feb-18

C-reactive protein, COVID-19, infection, inflammatory response, leukocyte antisedimentation rate, neutrophil-lymphocyte ratio, procalcitonin

General General

A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach.

In Multimedia tools and applications

Since the beginning of the covid-19 crisis, people from all over the world have used social media platforms to publish their opinions, sentiments, and ideas about the coronavirus epidemic and their news. Due to the nature of social networks, users share an immense amount of data every day in a freeway, which gives them the possibility to express opinions and sentiments about the coronavirus pandemic regardless of the time and the place. Moreover, The rapid number of exponential cases globally has become the apprehension of panic, fear, and anxiety among people. In this paper, we propose a new sentiment analysis approach to detect sentiments in Moroccan tweets related to covid-19 from March to October 2020. The proposed model is a recommender approach using the advantages of recommendation systems for classifying each tweet into three classes: positive, negative, or neutral. Experimental results show that our method gives good accuracy(86%) and outperforms the well-known machine learning algorithms. We find also that the sentiments of users changed from period to period, and that the evolution of the epidemiological situation in morocco affects the sentiments of users.

Madani Youness, Erritali Mohammed, Bouikhalene Belaid

2023-Feb-22

Classification, Collaborative filtering, Covid-19, Recommendation system, Sentiment analysis

General General

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.

In Multimedia tools and applications

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients.

Sharma Pulkit, Arya Rhythm, Verma Richa, Verma Bindu

2023-Feb-21

COVID-19 detection, Capsule networks, Chest X-ray classification, Medical imaging

Public Health Public Health

Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision.

In Journal of Shanghai Jiaotong University (science)

This study focuses on a robot vision localization method for coping with the operational task of automatic nasal swab sampling. The application is important in the detection and epidemic prevention of Corona Virus Disease 2019 (COVID-19) to alleviate the large-scale negative impact of individuals suffering from pneumonia owing to COVID-19. In this method, the idea of a hierarchical decision network is used to consider the strong infectious characteristics of the COVID-19, which is followed by processing the robot behavior constraint condition. The visual navigation and positioning method using a single-arm robot for sampling is also planned, which considers the operation characteristics of medical staff. In the decision network, the risk factor for potential contact infection caused by swab sampling operations is established to avoid the spread among personnel. A robot visual servo control with artificial intelligence characteristics is developed to achieve a stable and safe nasal swab sampling operation. Experiments demonstrate that the proposed method can achieve good vision positioning for the robots and provide technical support for managing new major public health situations.

Li Guozhi, Zou Shuizhong, Ding Shuxue

2023-Feb-21

hierarchical decision, nasal swab sampling, surgical robot, vision servo

Internal Medicine Internal Medicine

A dataset of COVID-19 x-ray chest images.

In Data in brief

The distinction between normal chest x-ray (CXR) images and abnormal ones containing features of disease (e.g., opacities, consolidation, etc.) is important for accurate medical diagnosis. CXR images contain valuable information concerning the physiological and pathological state of the lungs and airways. In addition, they provide information about the heart, chest bones, and some arteries (e.g., Aorta and pulmonary arteries). Deep learning artificial intelligence has taken great strides in the development of sophisticated medical models in a wide range of applications. More specifically, it has been shown to provide highly accurate diagnosis and detection tools. The dataset presented in this article contains the chest x-ray images from the examination of confirmed COVID-19 subjects, who were admitted for a multiday stay at a local hospital in northern Jordan. To provide a diverse dataset, only one CXR image per subject was included in the data. The dataset can be used for the development of automated methods that detect COVID-19 from CXR images (COVID-19 vs. normal) and distinguish pneumonia caused by COVID-19 from other pulmonary diseases. ©202x The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Fraiwan Mohammad, Khasawneh Natheer, Khassawneh Basheer, Ibnian Ali

2023-Apr

Artificial intelligence, COVID-19, Chest X-ray, Deep learning, Detection, Diagnosis

General General

Investigating brain cortical activity in patients with post-COVID-19 brain fog.

In Frontiers in neuroscience ; h5-index 72.0

Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.

Wojcik Grzegorz M, Shriki Oren, Kwasniewicz Lukasz, Kawiak Andrzej, Ben-Horin Yarden, Furman Sagi, Wróbel Krzysztof, Bartosik Bernadetta, Panas Ewelina

2023

COVID-19, EEG, ERP, LDA, brain fog, cortical activity

Public Health Public Health

Barriers to and solutions for representative inclusion across the lifespan and in life course research: The need for structural competency highlighted by the COVID-19 pandemic.

In Journal of clinical and translational science

Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the "Lifespan and Life Course Research: integrating strategies" "Un-Meeting" to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.

LeCroy Madison N, Potter Lindsey N, Bandeen-Roche Karen, Bianco Monica E, Cappola Anne R, Carter Ebony B, Dayan Peter S, Eckstrom Elizabeth, Edwards Dorothy F, Farabi Sarah S, Fisher Sheehan D, Giordano Judy, Hanson Heidi A, Jenkins Emerald, Juhn Young, Kaskel Frederick, Stake Christine E, Reeds Dominic N, Schleiss Mark R, Wafford Q Eileen, McColley Susanna A

2023

Life course research, research participation, social determinants of health, special populations, structural competency

General General

Multi-scale Triplet Hashing for Medical Image Retrieval.

In Computers in biology and medicine

For medical image retrieval task, deep hashing algorithms are widely applied in large-scale datasets for auxiliary diagnosis due to the retrieval efficiency advantage of hash codes. Most of which focus on features learning, whilst neglecting the discriminate area of medical images and hierarchical similarity for deep features and hash codes. In this paper, we tackle these dilemmas with a new Multi-scale Triplet Hashing (MTH) algorithm, which can leverage multi-scale information, convolutional self-attention and hierarchical similarity to learn effective hash codes simultaneously. The MTH algorithm first designs multi-scale DenseBlock module to learn multi-scale information of medical images. Meanwhile, a convolutional self-attention mechanism is developed to perform information interaction of the channel domain, which can capture the discriminate area of medical images effectively. On top of the two paths, a novel loss function is proposed to not only conserve the category-level information of deep features and the semantic information of hash codes in the learning process, but also capture the hierarchical similarity for deep features and hash codes. Extensive experiments on the Curated X-ray Dataset, Skin Cancer MNIST Dataset and COVID-19 Radiography Dataset illustrate that the MTH algorithm can further enhance the effect of medical retrieval compared to other state-of-the-art medical image retrieval algorithms.

Chen Yaxiong, Tang Yibo, Huang Jinghao, Xiong Shengwu

2023-Feb-08

Convolutional self-attention, Deep hashing, Hierarchical similarity, Medical image retrieval

General General

Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization.

In The journal of physical chemistry. B

A potential therapeutic strategy for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding to the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant variant, named sACE22.v.2.4, that exhibits subnanomolar affinity to the receptor-binding domain (RBD) of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identify other ACE2 variants that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified multiple ACE2 double mutants that bind SARS-CoV-2 S with tighter affinity as compared to the wild type, most notably L79V;N90D that binds RBD similarly to ACE22.v.2.4. The experimental validation of the double mutants successfully demonstrates the use of machine learning approaches for engineering protein-protein interactions and identifying high-affinity ACE2 peptides for targeting SARS-CoV-2.

Chan Matthew C, Chan Kui K, Procko Erik, Shukla Diwakar

2023-Feb-24

General General

Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation.

In Journal of imaging

A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection's progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.

Khan Hassan Ali, Gong Xueqing, Bi Fenglin, Ali Rashid

2023-Feb-13

CNN, COVID-19, CT scans, X-rays, classification, convolutional neural network, segmentation, watershed segmentation

General General

Masked Face Recognition Using Histogram-Based Recurrent Neural Network.

In Journal of imaging

Masked face recognition (MFR) is an interesting topic in which researchers have tried to find a better solution to improve and enhance performance. Recently, COVID-19 caused most of the recognition system fails to recognize facial images since the current face recognition cannot accurately capture or detect masked face images. This paper introduces the proposed method known as histogram-based recurrent neural network (HRNN) MFR to solve the undetected masked face problem. The proposed method includes the feature descriptor of histograms of oriented gradients (HOG) as the feature extraction process and recurrent neural network (RNN) as the deep learning process. We have proven that the combination of both approaches works well and achieves a high true acceptance rate (TAR) of 99 percent. In addition, the proposed method is designed to overcome the underfitting problem and reduce computational burdens with large-scale dataset training. The experiments were conducted on two benchmark datasets which are RMFD (Real-World Masked Face Dataset) and Labeled Face in the Wild Simulated Masked Face Dataset (LFW-SMFD) to vindicate the viability of the proposed HRNN method.

Chong Wei-Jie Lucas, Chong Siew-Chin, Ong Thian-Song

2023-Feb-08

deep learning, histogram of gradients, masked face recognition, neural network, recurrent

General General

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images.

In Journal of imaging

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.

Prinzi Francesco, Militello Carmelo, Conti Vincenzo, Vitabile Salvatore

2023-Jan-30

COVID-19 prognosis, chest X-ray images, machine learning models, predictive capability, radiomic features, wavelet kernels, wavelet-derived features

General General

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

In Journal of cardiovascular development and disease

Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.

Ovcharenko Evgeny, Kutikhin Anton, Gruzdeva Olga, Kuzmina Anastasia, Slesareva Tamara, Brusina Elena, Kudasheva Svetlana, Bondarenko Tatiana, Kuzmenko Svetlana, Osyaev Nikolay, Ivannikova Natalia, Vavin Grigory, Moses Vadim, Danilov Viacheslav, Komossky Egor, Klyshnikov Kirill

2023-Jan-23

C-reactive protein, COVID-19, blood urea nitrogen, chronic kidney disease, coronary artery disease, lymphocyte count, machine learning, neural networks, neutrophil-to-lymphocyte ratio, prognostication

Pathology Pathology

Variant-specific deleterious mutations in the SARS-CoV-2 genome reveal immune responses and potentials for prophylactic vaccine development.

In Frontiers in pharmacology

Introduction: Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has had a disastrous effect worldwide during the previous three years due to widespread infections with SARS-CoV-2 and its emerging variations. More than 674 million confirmed cases and over 6.7 million deaths have been attributed to successive waves of SARS-CoV-2 infections as of 29th January 2023. Similar to other RNA viruses, SARS-CoV-2 is more susceptible to genetic evolution and spontaneous mutations over time, resulting in the continual emergence of variants with distinct characteristics. Spontaneous mutations of SARS-CoV-2 variants increase its transmissibility, virulence, and disease severity and diminish the efficacy of therapeutics and vaccines, resulting in vaccine-breakthrough infections and re-infection, leading to high mortality and morbidity rates. Materials and methods: In this study, we evaluated 10,531 whole genome sequences of all reported variants globally through a computational approach to assess the spread and emergence of the mutations in the SARS-CoV-2 genome. The available data sources of NextCladeCLI 2.3.0 (https://clades.nextstrain.org/) and NextStrain (https://nextstrain.org/) were searched for tracking SARS-CoV-2 mutations, analysed using the PROVEAN, Polyphen-2, and Predict SNP mutational analysis tools and validated by Machine Learning models. Result: Compared to the Wuhan-Hu-1 reference strain NC 045512.2, genome-wide annotations showed 16,954 mutations in the SARS-CoV-2 genome. We determined that the Omicron variant had 6,307 mutations (retrieved sequence:1947), including 67.8% unique mutations, more than any other variant evaluated in this study. The spike protein of the Omicron variant harboured 876 mutations, including 443 deleterious mutations. Among these deleterious mutations, 187 were common and 256 were unique non-synonymous mutations. In contrast, after analysing 1,884 sequences of the Delta variant, we discovered 4,468 mutations, of which 66% were unique, and not previously reported in other variants. Mutations affecting spike proteins are mostly found in RBD regions for Omicron, whereas most of the Delta variant mutations drawn to focus on amino acid regions ranging from 911 to 924 in the context of epitope prediction (B cell & T cell) and mutational stability impact analysis protruding that Omicron is more transmissible. Discussion: The pathogenesis of the Omicron variant could be prevented if the deleterious and persistent unique immunosuppressive mutations can be targeted for vaccination or small-molecule inhibitor designing. Thus, our findings will help researchers monitor and track the continuously evolving nature of SARS-CoV-2 strains, the associated genetic variants, and their implications for developing effective control and prophylaxis strategies.

Islam Md Aminul, Shahi Shatila, Marzan Abdullah Al, Amin Mohammad Ruhul, Hasan Mohammad Nayeem, Hoque M Nazmul, Ghosh Ajit, Barua Abanti, Khan Abbas, Dhama Kuldeep, Chakraborty Chiranjib, Bhattacharya Prosun, Wei Dong-Qing

2023

COVID-19, SARS-CoV-2, deleterious mutation, delta variant, immune response, omicron variant, unique mutation, vaccine designing

Public Health Public Health

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets.

In Digital health

OBJECTIVE : Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia.

METHODS : Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries.

RESULTS : Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively.

CONCLUSIONS : Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated.

To Quyen G, To Kien G, Huynh Van-Anh N, Nguyen Nhung Tq, Ngo Diep Tn, Alley Stephanie, Tran Anh Nq, Tran Anh Np, Pham Ngan Tt, Bui Thanh X, Vandelanotte Corneel

2023

deep learning, neural network, social media, stance analysis, twitter, vaccine hesitancy

General General

A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges.

In Frontiers in neurology

In the last 3 years, almost all medical resources have been reserved for the screening and treatment of patients with coronavirus disease (COVID-19). Due to a shortage of medical staff and equipment, diagnosing sleep disorders, such as obstructive sleep apnea (OSA), has become more difficult than ever. In addition to being diagnosed using polysomnography at a hospital, people seem to pay more attention to alternative at-home OSA detection solutions. This study aims to review state-of-the-art assessment techniques for out-of-center detection of the main characteristics of OSA, such as sleep, cardiovascular function, oxygen balance and consumption, sleep position, breathing effort, respiratory function, and audio, as well as recent progress in the implementation of data acquisition and processing and machine learning techniques that support early detection of severe OSA levels.

Tran Ngoc Thai, Tran Huu Nam, Mai Anh Tuan

2023

COVID-19, OSA, SCOPER, machine learning, wearable device

oncology Oncology

Targeted plasma proteomics reveals signatures discriminating COVID-19 from sepsis with pneumonia.

In Respiratory research ; h5-index 45.0

BACKGROUND : COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features.

METHODS : We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients.

RESULTS : We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers.

CONCLUSIONS : This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis.

Palma Medina Laura M, Babačić Haris, Dzidic Majda, Parke Åsa, Garcia Marina, Maleki Kimia T, Unge Christian, Lourda Magda, Kvedaraite Egle, Chen Puran, Muvva Jagadeeswara Rao, Cornillet Martin, Emgård Johanna, Moll Kirsten, Michaëlsson Jakob, Flodström-Tullberg Malin, Brighenti Susanna, Buggert Marcus, Mjösberg Jenny, Malmberg Karl-Johan, Sandberg Johan K, Gredmark-Russ Sara, Rooyackers Olav, Svensson Mattias, Chambers Benedict J, Eriksson Lars I, Pernemalm Maria, Björkström Niklas K, Aleman Soo, Ljunggren Hans-Gustaf, Klingström Jonas, Strålin Kristoffer, Norrby-Teglund Anna

2023-Feb-24

COVID-19, Community acquired pneumonia, Olink proximity extension assays, Sepsis, Septic shock

Radiology Radiology

Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Two years into the COVID-19 pandemic and with more than five million deaths worldwide, the healthcare establishment continues to struggle with every new wave of the pandemic resulting from a new coronavirus variant. Research has demonstrated that there are variations in the symptoms, and even in the order of symptom presentations, in COVID-19 patients infected by different SARS-CoV-2 variants (e.g., Alpha and Omicron). Textual data in the form of admission notes and physician notes in the Electronic Health Records (EHRs) is rich in information regarding the symptoms and their orders of presentation. Unstructured EHR data is often underutilized in research due to the lack of annotations that enable automatic extraction of useful information from the available extensive volumes of textual data.

METHODS : We present the design of a COVID Interface Terminology (CIT), not just a generic COVID-19 terminology, but one serving a specific purpose of enabling automatic annotation of EHRs of COVID-19 patients. CIT was constructed by integrating existing COVID-related ontologies and mining additional fine granularity concepts from clinical notes. The iterative mining approach utilized the techniques of 'anchoring' and 'concatenation' to identify potential fine granularity concepts to be added to the CIT. We also tested the generalizability of our approach on a hold-out dataset and compared the annotation coverage to the coverage obtained for the dataset used to build the CIT.

RESULTS : Our experiments demonstrate that this approach results in higher annotation coverage compared to existing ontologies such as SNOMED CT and Coronavirus Infectious Disease Ontology (CIDO). The final version of CIT achieved about 20% more coverage than SNOMED CT and 50% more coverage than CIDO. In the future, the concepts mined and added into CIT could be used as training data for machine learning models for mining even more concepts into CIT and further increasing the annotation coverage.

CONCLUSION : In this paper, we demonstrated the construction of a COVID interface terminology that can be utilized for automatically annotating EHRs of COVID-19 patients. The techniques presented can identify frequently documented fine granularity concepts that are missing in other ontologies thereby increasing the annotation coverage.

Keloth Vipina K, Zhou Shuxin, Lindemann Luke, Zheng Ling, Elhanan Gai, Einstein Andrew J, Geller James, Perl Yehoshua

2023-Feb-24

COVID-19 ontologies, Concept mining, EHR annotation, Interface terminology

Cardiology Cardiology

Association of statin use with outcomes of patients admitted with COVID-19: an analysis of electronic health records using superlearner.

In BMC infectious diseases ; h5-index 58.0

IMPORTANCE : Statin use prior to hospitalization for Coronavirus Disease 2019 (COVID-19) is hypothesized to improve inpatient outcomes including mortality, but prior findings from large observational studies have been inconsistent, due in part to confounding. Recent advances in statistics, including incorporation of machine learning techniques into augmented inverse probability weighting with targeted maximum likelihood estimation, address baseline covariate imbalance while maximizing statistical efficiency.

OBJECTIVE : To estimate the association of antecedent statin use with progression to severe inpatient outcomes among patients admitted for COVD-19.

DESIGN, SETTING AND PARTICIPANTS : We retrospectively analyzed electronic health records (EHR) from individuals ≥ 40-years-old who were admitted between March 2020 and September 2022 for ≥ 24 h and tested positive for SARS-CoV-2 infection in the 30 days before to 7 days after admission.

EXPOSURE : Antecedent statin use-statin prescription ≥ 30 days prior to COVID-19 admission.

MAIN OUTCOME : Composite end point of in-hospital death, intubation, and intensive care unit (ICU) admission.

RESULTS : Of 15,524 eligible COVID-19 patients, 4412 (20%) were antecedent statin users. Compared with non-users, statin users were older (72.9 (SD: 12.6) versus 65.6 (SD: 14.5) years) and more likely to be male (54% vs. 51%), White (76% vs. 71%), and have ≥ 1 medical comorbidity (99% vs. 86%). Unadjusted analysis demonstrated that a lower proportion of antecedent users experienced the composite outcome (14.8% vs 19.3%), ICU admission (13.9% vs 18.3%), intubation (5.1% vs 8.3%) and inpatient deaths (4.4% vs 5.2%) compared with non-users. Risk differences adjusted for labs and demographics were estimated using augmented inverse probability weighting with targeted maximum likelihood estimation using Super Learner. Statin users still had lower rates of the composite outcome (adjusted risk difference: - 3.4%; 95% CI: - 4.6% to - 2.1%), ICU admissions (- 3.3%; - 4.5% to - 2.1%), and intubation (- 1.9%; - 2.8% to - 1.0%) but comparable inpatient deaths (0.6%; - 1.3% to 0.1%).

CONCLUSIONS AND RELEVANCE : After controlling for confounding using doubly robust methods, antecedent statin use was associated with minimally lower risk of severe COVID-19-related outcomes, ICU admission and intubation, however, we were not able to corroborate a statin-associated mortality benefit.

Rivera Adovich S, Al-Heeti Omar, Petito Lucia C, Feinstein Mathew J, Achenbach Chad J, Williams Janna, Taiwo Babafemi

2023-Feb-24

COVID-19, Critical care, Mortality, Observational studies, Statin, Targeted maximum likelihood estimation

Public Health Public Health

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds.

In Medical & biological engineering & computing ; h5-index 32.0

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network-based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets.

Ulukaya Sezer, Sarıca Ahmet Alp, Erdem Oğuzhan, Karaali Ali

2023-Feb-24

Coronavirus, Coughing, Deep learning, Ensemble learning, Telehealth

General General

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

bioRxiv Preprint

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves rapidly and drug resistant strains have emerged. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning based on experimental data from knockout screens and a drug screen. As gold standard, we assembled perturbed genes reducing virus replication or protecting the host cells. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance with a balanced accuracy of 0.82 (knockout based classifier) and 0.71 (drugs screen based classifier), suggesting patterns of intrinsic data consistency. The predicted host dependency factors were enriched in sets of genes particularly coding for development, morphogenesis, and neural related processes. Focusing on development and morphogenesis-associated gene sets, we found {beta}-catenin to be central and selected PRI-724, a canonical {beta}-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in CPE development, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept may support focusing and accelerating the discovery of host dependency factors and the design of antiviral therapies.

Kelch, M. A.; Vera-Guapi, A. L.; Beder, T.; Oswald, M.; Hiemisch, A.; Beil, N.; Wajda, P.; Ciesek, S.; Erfle, H.; Toptan, T.; König, R.

2023-02-24

General General

Make it or break it: On-time vaccination intent at the time of Covid-19.

In Vaccine ; h5-index 70.0

On-time effective vaccination is critical to curbing a pandemic, but this is often hampered by citizens' hesitancy to get quickly vaccinated. This research concentrates on the hypothesis that, besides traditional factors in the literature, vaccination success would hinge on two dimensions: a) addressing a broader set of risk perception factors than health-related issues only, and b) securing sufficient social and institutional trust at the time of vaccination campaign launch. We test this hypothesis regarding Covid-19 vaccination preferences in six European countries and at the early stage of the pandemic by April 2020. We find that addressing the two roadblock dimensions could further boost Covid-19 vaccination coverage by 22%. The study also offers three extra innovations. The first is that the traditional segmentation logic between vaccine "acceptors", "hesitants" and "refusers" is further justified by the fact that segments have different attitudes: refusers care less about health issues than they are worried about family tensions and finance (dimension 1 of our hypothesis). In contrast, hesitants are the battlefield for more transparency by media and government actions (dimension 2 of our hypothesis). The second added value is that we extend our hypothesis testing with a supervised non-parametric machine learning technique (Random Forests). Again, consistent with our hypothesis, this method picks up higher-order interaction between risk and trust variables that strongly predict on-time vaccination intent. We finally explicitly adjust survey responses to account for possible reporting bias. Among others, vaccine-reluctant citizens may under-report their limited will to get vaccinated.

Bughin Jacques, Cincera Michele, Peters Kelly, Reykowska Dorota, Żyszkiewicz Marcin, Ohme Rafal

2023-Feb-08

Covid-19, Machine Learning, Random-forest, Social trust, Vaccine strategy

Public Health Public Health

Telehealth utilization in U.S. medicare beneficiaries aged 65 years and older during the COVID-19 pandemic.

In BMC public health ; h5-index 82.0

BACKGROUND : The COVID-19 pandemic has become a serious public health concern for older adults and amplified the value of deploying telehealth solutions. The purpose of this study was to investigate telehealth offered by providers among U.S. Medicare beneficiaries aged 65 years and older during the COVID-19 pandemic.

METHODS : This cross-sectional study analyzed Medicare beneficiaries aged 65 years and older using data from the Medicare Current Beneficiary Survey, Winter 2021 COVID-19 Supplement ([Formula: see text]). We identified variables that were associated with telehealth offered by primary care physicians and beneficiaries' access to the Internet through a multivariate classification analysis utilizing Random Forest machine learning techniques.

FINDINGS : For study participants interviewed by telephone, 81.06% of primary care providers provided telehealth services, and 84.62% of the Medicare beneficiaries had access to the Internet. The survey response rates for each outcome were 74.86% and 99.55% respectively. The two outcomes were positively correlated ([Formula: see text]). The Our machine learning model predicted the outcomes accurately utilizing 44 variables. Residing area and race/ethnicity were most informative for predicting telehealth coverage, and Medicare-Medicaid dual eligibility and income were most informative for predicting Internet access. Other strong correlates included age, ability to access basic needs and certain mental and physical health conditions. Interactions were found among statuses of residing area, age, Medicare Advantage and heart conditions that intensified the disparity of outcomes.

CONCLUSIONS : We found that telehealth offered by providers likely increased during the COVID-19 pandemic for older beneficiaries, providing important access to care for certain subgroups. Policymakers must continue to identify effective means of delivering telehealth services, modernize the framework of regulatory, accreditation and reimbursement, and address disparities in access to telehealth with a particular focus on underserved communities.

Lu Min, Liao Xinyi

2023-Feb-20

COVID-19, Medicare, Older adults, Primary care, Telehealth, Telemedicine

Public Health Public Health

A digital health platform to manage COVID-19: decentralizing technology to empower rural and remote jurisdictions.

In Rural and remote health

INTRODUCTION : The variation of coronavirus disease (COVID-19) outbreaks across rural and remote jurisdictions makes it imperative to invest in scalable digital health platforms to not only minimize the impact of subsequent COVID-19 outbreaks, but also to utilize such approaches to predict and prevent future communicable and non-communicable diseases.

METHODS : The methodology of the digital health platform comprised: (1) Ethical Real-Time Surveillance to Monitor Risk: evidence-based artificial intelligence-driven individual and community risk assessment of COVID-19 by engaging citizens using their own smartphones; (2) Citizen Empowerment and Data Ownership: active engagement of citizens using smartphone application (app) features, while enabling data ownership; and (3) Privacy: development of algorithms that store sensitive data directly on mobile devices.

RESULTS : The result is a community-engaged, innovative, and scalable digital health platform, with three key features: (1) Prevention: this feature is based on risky and healthy behaviours, and has the sophistication to continuously engage citizens; (2) Public Health Communication: based on their risk profile and behaviour, citizens receive specific public health communication that helps them make informed decisions; and (3) Precision Medicine: risk assessment and behaviour modification is individualized so that the frequency, type, and intensity of engagement is based on individual risk profile.

DISCUSSION : This digital health platform enables the decentralization of digital technology to effect systems-level changes. With more than 6 billion smartphone subscriptions globally, digital health platforms enable direct engagement with large populations in near real-time to monitor, mitigate, and manage public health crises, particularly in rural communities that do not have equitable access to health services.

Katapally Tarun

2023-Jan

General General

Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning.

In PloS one ; h5-index 176.0

BACKGROUND : After the COVID-19 pandemic, the world has made efforts to recover from the chaotic situation. Vaccination is a way to help control infectious diseases, and many people have been vaccinated against COVID-19 by this point. However, an extremely small number of those who received the vaccine have experienced diverse side effects.

METHODS AND FINDINGS : In this study, we examined people who experienced adverse events with the COVID-19 vaccine by gender, age, vaccine manufacturer, and dose of vaccinations by using the Vaccine Adverse Event Reporting System datasets. Then we used a language model to vectorize symptom words and reduced their dimensionality. We also clustered symptoms by using unsupervised machine learning and analyzed the characteristics of each symptom cluster. Lastly, to discover any association rules among adverse events, we used a data mining approach. The frequency of adverse events was higher for women than men, for Moderna than for Pfizer or Janssen, and for the first dose than for the second dose. However, we found that characteristics of vaccine adverse events, including gender, vaccine manufacturer, age, and underlying diseases were different for each symptom cluster, and that fatal cases were significantly related to a particular cluster (associated with hypoxia). Also, as a result of the association analysis, the {chills ↔ pyrexia} and {vaccination site pruritus ↔ vaccination site erythema} rules had the highest support value of 0.087 and 0.046, respectively.

CONCLUSIONS : We aim to contribute accurate information on the adverse events of the COVID-19 vaccine to relieve public anxiety due to unconfirmed statements about vaccines.

Cheon Saeyeon, Methiyothin Thanin, Ahn Insung

2023

General General

Comprehensive classification of proteins based on structures that engage lipids by COMPOSEL.

In Biophysical chemistry

Structures can now be predicted for any protein using programs like AlphaFold and Rosetta, which rely on a foundation of experimentally determined structures of architecturally diverse proteins. The accuracy of such artificial intelligence and machine learning (AI/ML) approaches benefits from the specification of restraints which assist in navigating the universe of folds to converge on models most representative of a given protein's physiological structure. This is especially pertinent for membrane proteins, with structures and functions that depend on their presence in lipid bilayers. Structures of proteins in their membrane environments could conceivably be predicted from AI/ML approaches with user-specificized parameters that describe each element of the architecture of a membrane protein accompanied by its lipid environment. We propose the Classification Of Membrane Proteins based On Structures Engaging Lipids (COMPOSEL), which builds on existing nomenclature types for monotopic, bitopic, polytopic and peripheral membrane proteins as well as lipids. Functional and regulatory elements are also defined in the scripts, as shown with membrane fusing synaptotagmins, multidomain PDZD8 and Protrudin proteins that recognize phosphoinositide (PI) lipids, the intrinsically disordered MARCKS protein, caveolins, the β barrel assembly machine (BAM), an adhesion G-protein coupled receptor (aGPCR) and two lipid modifying enzymes - diacylglycerol kinase DGKε and fatty aldehyde dehydrogenase FALDH. This demonstrates how COMPOSEL communicates lipid interactivity as well as signaling mechanisms and binding of metabolites, drug molecules, polypeptides or nucleic acids to describe the operations of any protein. Moreover COMPOSEL can be scaled to express how genomes encode membrane structures and how our organs are infiltrated by pathogens such as SARS-CoV-2.

Overduin Michael, Kervin Troy A, Klarenbach Zachary, Adra Trixie Rae C, Bhat Rakesh K

2023-Feb-08

Bilayer insertion, Intrinsically disordered region, Ligand complex, Lipid interaction, Monotopic, Myristoylation, Palmitoylation, Peripheral membrane domain, Polytopic, Protein classification, Transmembrane

General General

Interpreting biologically informed neural networks for enhanced biomarker discovery and pathway analysis

bioRxiv Preprint

The advent of novel methods in mass spectrometry-based proteomics allows for the identification of biomarkers and biological pathways which are crucial for the understanding of complex diseases. However, contemporary analytical methods often omit essential information, such as protein abundance and protein co-regulation, and therefore miss crucial relationships in the data. Here, we introduce a generalized workflow that incorporates proteins, their abundances, and associated pathways into a deep learning-based methodology to improve biomarker identification and pathway analysis through the creation and interpretation of biologically informed neural networks (BINNs). We successfully employ BINNs to differentiate between two subphenotypes of septic acute kidney injury (AKI) and COVID-19 from the plasma proteome and utilize feature attribution-methods to introspect the networks to identify which proteins and pathways are important for distinguishing between subphenotypes. Compared to existing methods, BINNs achieved the highest predictive accuracy and revealed that metabolic processes were key to differentiating between septic AKI subphenotypes, while the immune system was more important to the classification of COVID-19 subphenotypes. The methodology behind creating, interpreting, and visualizing BINNs were implemented in a free and open source Python-package: https://github.com/InfectionMedicineProteomics/BINN.

Hartman, E.; Scott, A. M.; Malmström, L.; Malmström, J.

2023-02-21

General General

Multilingual Content Moderation: A Case Study on Reddit

ArXiv Preprint

Content moderation is the process of flagging content based on pre-defined platform rules. There has been a growing need for AI moderators to safeguard users as well as protect the mental health of human moderators from traumatic content. While prior works have focused on identifying hateful/offensive language, they are not adequate for meeting the challenges of content moderation since 1) moderation decisions are based on violation of rules, which subsumes detection of offensive speech, and 2) such rules often differ across communities which entails an adaptive solution. We propose to study the challenges of content moderation by introducing a multilingual dataset of 1.8 Million Reddit comments spanning 56 subreddits in English, German, Spanish and French. We perform extensive experimental analysis to highlight the underlying challenges and suggest related research problems such as cross-lingual transfer, learning under label noise (human biases), transfer of moderation models, and predicting the violated rule. Our dataset and analysis can help better prepare for the challenges and opportunities of auto moderation.

Meng Ye, Karan Sikka, Katherine Atwell, Sabit Hassan, Ajay Divakaran, Malihe Alikhani

2023-02-19

General General

Understanding how the use of AI decision support tools affect critical thinking and over-reliance on technology by drug dispensers in Tanzania

ArXiv Preprint

The use of AI in healthcare is designed to improve care delivery and augment the decisions of providers to enhance patient outcomes. When deployed in clinical settings, the interaction between providers and AI is a critical component for measuring and understanding the effectiveness of these digital tools on broader health outcomes. Even in cases where AI algorithms have high diagnostic accuracy, healthcare providers often still rely on their experience and sometimes gut feeling to make a final decision. Other times, providers rely unquestioningly on the outputs of the AI models, which leads to a concern about over-reliance on the technology. The purpose of this research was to understand how reliant drug shop dispensers were on AI-powered technologies when determining a differential diagnosis for a presented clinical case vignette. We explored how the drug dispensers responded to technology that is framed as always correct in an attempt to measure whether they begin to rely on it without any critical thought of their own. We found that dispensers relied on the decision made by the AI 25 percent of the time, even when the AI provided no explanation for its decision.

Ally Jr Salim, Megan Allen, Kelvin Mariki, Kevin James Masoy, Jafary Liana

2023-02-19

General General

Turning any bed into an intensive care unit with the Internet of things and artificial intelligence technology. Presenting the enhanced mechanical ventilator.

In F1000Research

The recent Coronavirus disease 2019 (COVID-19) pandemic displayed weaknesses in the healthcare infrastructures worldwide and exposed a lack of specialized personnel to cover the demands of a massive calamity. We have developed a portable ventilator that uses real-time vitals read from the patient to estimate -- through artificial intelligence -- the optimal operation point. The ventilator has redundant telecommunication capabilities; therefore, the remote assistance model can protect specialists and relatives from highly contagious agents. Additionally, we have designed a system that automatically publishes information in a proprietary cloud centralizer to keep physicians and relatives informed. The system was tested in a residential last-mile connection, and transaction times below the second were registered. The timing scheme allows us to operate up to 200 devices concurrently on these lowest-specification transmission control protocol/internet protocol (TCP/IP) services, promptly transmitting data for online processing and reporting. The ventilator is a proof of concept of automation that has behavioral and cognitive inputs to cheaply, yet reliably, extend the installed capacity of the healthcare systems and multiply the response of the skilled medical personnel to cover high-demanding scenarios and improve service quality.

Pulido Morales Leidy Lorena, Buitrago Romero Juan Sebastian, Ardila Sanchez Ismael A, Yepes-Calderon Fernando

2022

AWS implementations, Artificial Neuronal Networks in medicine, Artificial intelligence in medicine, Covid 19 mitigation, Evalu@ implementations, Intense care units everywhere, Technology in healthcare, mechanical ventilators

General General

Infrared image method for possible COVID-19 detection through febrile and subfebrile people screening.

In Journal of thermal biology

This study proposed an infrared image-based method for febrile and subfebrile people screening to comply with the society need for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on facial infrared imaging for possible COVID-19 early detection in people with and without fever (subfebrile state); (ii) Using 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RT-qPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used through a convolutional neural network (CNN) to develop the algorithm that took facial infrared images as input and classified the tested individuals in three groups: fever (high risk), subfebrile (medium risk), and no fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 °C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected subfebrile group. The COVID-19 (+) main risk factor was to be in the subfebrile group, in comparison to age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general.

Brioschi Marcos Leal, Dalmaso Neto Carlos, Toledo Marcos de, Neves Eduardo Borba, Vargas José Viriato Coelho, Teixeira Manoel Jacobsen

2023-Feb

Artificial intelligence, Convolutional neural network, Infrared imaging

General General

An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules.

In Journal of pharmaceutical and biomedical analysis

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.

Yang Xiaohua, Wang Shangqi, Qi Lina, Chen Shujing, Du Kunze, Shang Ye, Guo Jiading, Fang Shiming, Li Jin, Zhang Han, Chang Yanxu

2023-Feb-11

COVID-19, Deep-learning MDF, Qingjin Yiqi Granules, UHPLC-Q-TOF/MS, UHPLC-sMRM

Radiology Radiology

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.

OBJECTIVE : We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.

METHODS : This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration.

RESULTS : The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859).

CONCLUSIONS : The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Lee Hyun Woo, Yang Hyun Jun, Kim Hyungjin, Kim Ue-Hwan, Kim Dong Hyun, Yoon Soon Ho, Ham Soo-Youn, Nam Bo Da, Chae Kum Ju, Lee Dabee, Yoo Jin Young, Bak So Hyeon, Kim Jin Young, Kim Jin Hwan, Kim Ki Beom, Jung Jung Im, Lim Jae-Kwang, Lee Jong Eun, Chung Myung Jin, Lee Young Kyung, Kim Young Seon, Lee Sang Min, Kwon Woocheol, Park Chang Min, Kim Yun-Hyeon, Jeong Yeon Joo, Jin Kwang Nam, Goo Jin Mo

2023-Feb-16

AI model, COVID-19, artificial intelligence, clinical outcome, deep learning, machine learning, medical imaging, prediction model, prognosis, radiography, thoracic

General General

Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE.

In Heliyon

Nitrogen dioxide (NO2) is the most active pollutant gas emitted in the industrial era and is highly correlated with human activities. Tracking NO2 emissions and predicting their concentrations represent important steps toward controlling pollution and setting rules to protect people's health indoors, such as in factories, and in outdoor environments. The concentration of NO2 was affected by the COVID-19 lockdown period and decreased because of restrictions on outdoor activities. In this study, the concentration of NO2 was predicted at 14 ground stations in the United Arab Emirates (UAE) during December 2020 based on training over a full time period of two years (2019-2020). Statistical and machine learning models, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and nonlinear autoregressive neural network (NAR-NN), are used with both open- and closed-loop architectures. The mean absolute percentage error (MAPE) was used to evaluate the performance of the models, and the results ranged from "very good" (MAPE of 8.64% at the Liwa station with the closed loop) to "acceptable" (MAPE of 42.45% at the Khadejah School station with the open loop). The results show that the predictions based on the open loop are generally better than those based on the closed loop because they yield statistically significantly lower MAPE values. For both loop types, we selected stations exhibiting the lowest, medium, and highest MAPE values as representative cases. In addition, we demonstrated that the MAPE value is highly correlated with the relative standard deviation of NO2 concentration values.

Al Yammahi Aishah, Aung Zeyar

2023-Feb

ARIMA, Classical statistics, LSTM, Machine learning, NAR, NO2, SARIMA

General General

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

In BMC bioinformatics

BACKGROUND : Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks.

METHODS : We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs.

RESULTS : In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19.

CONCLUSIONS : In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.

Hashemi S Morteza, Zabihian Arash, Hooshmand Mohsen, Gharaghani Sajjad

2023-Feb-15

COVID-19, Deep learning, Drug repurposing, Matrix factorization

General General

Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning

ArXiv Preprint

This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 coronavirus variant identification. We analyze the SARS-CoV-2 spike sequences in a distributed way, without data sharing, to detect different variants of the rapidly mutating coronavirus. A vast amount of sequencing data of SARS-CoV-2 is available due to various genomic monitoring initiatives by several nations. However, privacy concerns involving patient health information and national public health conditions could hinder openly sharing this data. In this work, we propose a lightweight FL paradigm to cooperatively analyze the spike protein sequences of SARS-CoV-2 privately, using the locally stored data to train a prediction model from remote nodes. Our method maintains the confidentiality of local data (that could be stored in different locations) yet allows us to reliably detect and identify different known and unknown variants of the novel coronavirus SARS-CoV-2. We compare the performance of our approach on spike sequence data with the recently proposed state-of-the-art methods for classification from spike sequences. Using the proposed approach, we achieve an overall accuracy of $93\%$ on the coronavirus variant identification task. To the best of our knowledge, this is the first work in the federated learning paradigm for biological sequence analysis. Since the proposed model is distributed in nature, it could scale on ``Big Data'' easily. We plan to use this proof-of-concept to implement a privacy-preserving pandemic response strategy.

Prakash Chourasia, Taslim Murad, Zahra Tayebi, Sarwan Ali, Imdad Ullah Khan, Murray Patterson

2023-02-17

General General

LDANet: Automatic lung parenchyma segmentation from CT images.

In Computers in biology and medicine

Automatic segmentation of the lung parenchyma from computed tomography (CT) images is helpful for the subsequent diagnosis and treatment of patients. In this paper, based on a deep learning algorithm, a lung dense attention network (LDANet) is proposed with two mechanisms: residual spatial attention (RSA) and gated channel attention (GCA). RSA is utilized to weight the spatial information of the lung parenchyma and suppress feature activation in irrelevant regions, while the weights of each channel are adaptively calibrated using GCA to implicitly predict potential key features. Then, a dual attention guidance module (DAGM) is designed to maximize the integration of the advantages of both mechanisms. In addition, LDANet introduces a lightweight dense block (LDB) that reuses feature information and a positioned transpose block (PTB) that realizes accurate positioning and gradually restores the image resolution until the predicted segmentation map is generated. Experiments are conducted on two public datasets, LIDC-IDRI and COVID-19 CT Segmentation, on which LDANet achieves Dice similarity coefficient values of 0.98430 and 0.98319, respectively, outperforming a state-of-the-art lung segmentation model. Additionally, the effectiveness of the main components of LDANet is demonstrated through ablation experiments.

Chen Ying, Feng Longfeng, Zheng Cheng, Zhou Taohui, Liu Lan, Liu Pengfei, Chen Yi

2023-Feb-10

CT images, DAGM, LDB, Lung parenchyma segmentation

General General

Artificial intelligence assessment of the potential of tocilizumab along with corticosteroids therapy for the management of COVID-19 evoked acute respiratory distress syndrome.

In PloS one ; h5-index 176.0

Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Segú-Vergés Cristina, Artigas Laura, Coma Mireia, Peck Richard W

2023

General General

Using machine learning to improve our understanding of COVID-19 infection in children.

In PloS one ; h5-index 176.0

PURPOSE : Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not.

METHODS : A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection.

RESULTS : Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross validation and SHAP model was used to determine feature importance. The radiological features such as pneumonia, small airways disease, and atelectasis (confounded with catheter) were found to be highly associated with predicting the status of COVID-19 infection.

CONCLUSIONS : In this sample, radiological X-ray films can predict the status of COVID-19 infection with good accuracy. The multivariate model including symptoms presented around the time of COVID-19 test yielded good prediction score.

Piparia Shraddha, Defante Andrew, Tantisira Kelan, Ryu Julie

2023

General General

Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics.

OBJECTIVE : This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics.

METHODS : We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models.

RESULTS : We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery.

CONCLUSIONS : This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

Liu Yongtai, Yin Zhijun, Ni Congning, Yan Chao, Wan Zhiyu, Malin Bradley

2023-Feb-15

COVID-19, Twitter, data, epidemic, machine learning, management, model, prevention, rural, sentiment analysis, social media, topic analysis, training, urban, vaccination, word embedding

General General

Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.

In SN computer science

Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive; eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.

Ghashghaei Sara, Wood David A, Sadatshojaei Erfan, Jalilpoor Mansooreh

2023

COVID-19 lung abnormalities, Computed tomography (CT) scan analysis, Confusion matrices, Grayscale image attributes, Machine and deep learning predictions, Visual and algorithmic classifications

General General

A multimodal facial cues based engagement detection system in e-learning context using deep learning approach.

In Multimedia tools and applications

Due to the COVID-19 crisis, the education sector has been shifted to a virtual environment. Monitoring the engagement level and providing regular feedback during e-classes is one of the major concerns, as this facility lacks in the e-learning environment due to no physical observation of the teacher. According to present study, an engagement detection system to ensure that the students get immediate feedback during e-Learning. Our proposed engagement system analyses the student's behaviour throughout the e-Learning session. The proposed novel approach evaluates three modalities based on the student's behaviour, such as facial expression, eye blink count, and head movement, from the live video streams to predict student engagement in e-learning. The proposed system is implemented based on deep-learning approaches such as VGG-19 and ResNet-50 for facial emotion recognition and the facial landmark approach for eye-blinking and head movement detection. The results from different modalities (for which the algorithms are proposed) are combined to determine the EI (engagement index). Based on EI value, an engaged or disengaged state is predicted. The present study suggests that the proposed facial cues-based multimodal system accurately determines student engagement in real time. The experimental research achieved an accuracy of 92.58% and showed that the proposed engagement detection approach significantly outperforms the existing approaches.

Gupta Swadha, Kumar Parteek, Tekchandani Rajkumar

2023-Feb-10

Deep learning, Emotion detection, Engagement detection, Eye-blinking, Facial expressions, Head-movement, Online learning, Real-time

Public Health Public Health

A Potential Role of the Spike Protein in Neurodegenerative Diseases: A Narrative Review.

In Cureus

Human prion protein and prion-like protein misfolding are widely recognized as playing a causal role in many neurodegenerative diseases. Based on in vitro and in vivo experimental evidence relating to prion and prion-like disease, we extrapolate from the compelling evidence that the spike glycoprotein of SARS-CoV-2 contains extended amino acid sequences characteristic of a prion-like protein to infer its potential to cause neurodegenerative disease. We propose that vaccine-induced spike protein synthesis can facilitate the accumulation of toxic prion-like fibrils in neurons. We outline various pathways through which these proteins could be expected to distribute throughout the body. We review both cellular pathologies and the expression of disease that could become more frequent in those who have undergone mRNA vaccination. Specifically, we describe the spike protein's contributions, via its prion-like properties, to neuroinflammation and neurodegenerative diseases; to clotting disorders within the vasculature; to further disease risk due to suppressed prion protein regulation in the context of widely prevalent insulin resistance; and to other health complications. We explain why these prion-like characteristics are more relevant to vaccine-related mRNA-induced spike proteins than natural infection with SARS-CoV-2. We note with an optimism an apparent loss of prion-like properties among the current Omicron variants. We acknowledge that the chain of pathological events described throughout this paper is only hypothetical and not yet verified. We also acknowledge that the evidence we usher in, while grounded in the research literature, is currently largely circumstantial, not direct. Finally, we describe the implications of our findings for the general public, and we briefly discuss public health recommendations we feel need urgent consideration. An earlier version of this article was previously posted to the Authorea preprint server on August 16, 2022.

Seneff Stephanie, Kyriakopoulos Anthony M, Nigh Greg, McCullough Peter A

2023-Feb

amyloidosis, cd16+ monocytes, diabetes, exosomes, g quadruplexes, mrna vaccines, neurodegeneration, prion disease, sars-cov-2, spike protein

Public Health Public Health

Scientific novelty beyond the experiment.

In Microbial biotechnology

Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigm-changing-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is on microbiology studies that are more-recent, discussing these in the context of the scientific process and the types of scientific novelty that they represent. These include several studies carried out during the 2020 to 2022 lockdowns of the COVID-19 pandemic when access to research laboratories was disallowed (or limited). We interviewed the authors of some of the featured microbiology-related papers and-although we ourselves are involved in laboratory experiments and practical fieldwork-also drew from our own research experiences showing that such studies can not only produce new scientific findings but can also transcend barriers between disciplines, act counter to scientific reductionism, integrate biological data across different timescales and levels of complexity, and circumvent constraints imposed by practical techniques. In relation to urgent research needs, we believe that climate change and other global challenges may require approaches beyond the experiment.

Hallsworth John E, Udaondo Zulema, Pedrós-Alió Carlos, Höfer Juan, Benison Kathleen C, Lloyd Karen G, Cordero Radamés J B, de Campos Claudia B L, Yakimov Michail M, Amils Ricardo

2023-Feb-14

General General

A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset.

In Microprocessors and microsystems

Feature selection is one of the most important challenges in machine learning and data science. This process is usually performed in the data preprocessing phase, where the data is transformed to a proper format for further operations by machine learning algorithm. Many real-world datasets are highly dimensional with many irrelevant, even redundant features. These kinds of features do not improve classification accuracy and can even shrink down performance of a classifier. The goal of feature selection is to find optimal (or sub-optimal) subset of features that contain relevant information about the dataset from which machine learning algorithms can derive useful conclusions. In this manuscript, a novel version of firefly algorithm (FA) is proposed and adapted for feature selection challenge. Proposed method significantly improves performance of the basic FA, and also outperforms other state-of-the-art metaheuristics for both, benchmark bound-constrained and practical feature selection tasks. Method was first validated on standard unconstrained benchmarks and later it was applied for feature selection by using 21 standard University of California, Irvine (UCL) datasets. Moreover, presented approach was also tested for relatively novel COVID-19 dataset for predicting patients health, and one microcontroller microarray dataset. Results obtained in all practical simulations attest robustness and efficiency of proposed algorithm in terms of convergence, solutions' quality and classification accuracy. More precisely, the proposed approach obtained the best classification accuracy on 13 out of 21 total datasets, significantly outperforming other competitor methods.

Bacanin Nebojsa, Venkatachalam K, Bezdan Timea, Zivkovic Miodrag, Abouhawwash Mohamed

2023-Apr

COVID-19 dataset, Feature selection, Firefly algorithm, Genetic operators, Quasi-reflection-based learning, Swarm intelligence

General General

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

In Computational and structural biotechnology journal

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Clancy Jeffrey, Hoffmann Curtis S, Pickett Brett E

2023

AUC, Area under the curve, Bioinformatics, Biomarkers, COVID-19, COVID-19, Coronavirus Disease of 2019, DEG, Differentially expressed gene, Data mining, GEO, Gene Expression Omnibus, GO, Gene Ontology, RNA, RNA-sequencing, ROC, Receiver-operator characteristic, SARS-CoV-2, SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2, Virus

General General

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions.

In Complex & intelligent systems

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

Alamoodi A H, Zaidan B B, Albahri O S, Garfan Salem, Ahmaro Ibraheem Y Y, Mohammed R T, Zaidan A A, Ismail Amelia Ritahani, Albahri A S, Momani Fayiz, Al-Samarraay Mohammed S, Jasim Ali Najm

2023-Feb-03

COVID-19, Data privacy, Federated learning, Monoclonal antibodies, Multi-criterion decision making, Treatment

General General

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties.

In ACS omega

Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak.

Šušteršič Tijana, Gribova Varvara, Nikolic Milica, Lavalle Philippe, Filipovic Nenad, Vrana Nihal Engin

2023-Feb-07

General General

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation.

In Journal of bionic engineering

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu's variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

Abualigah Laith, Habash Mahmoud, Hanandeh Essam Said, Hussein Ahmad MohdAziz, Shinwan Mohammad Al, Zitar Raed Abu, Jia Heming

2023-Feb-07

Bioinspired, Image segmentation, Meta-heuristic algorithm, Multi-level thresholding, Reptile Search Algorithm, Salp Swarm Algorithm

Public Health Public Health

Governing AI during a pandemic crisis: Initiatives at the EU level.

In Technology in society

After the outbreak of Covid-19, the European Commission (EC) promptly took the initiative to lead and coordinate a common European response. The actions unfolded in several directions, paving the way to the uptake of AI-related solutions and placing hope in these tools to face crises, namely of a public health and global nature. In this article, we focus on initiatives for the uptake of AI-related solutions from the experimental level towards implementation. The Repository of AI and Robotics solutions, launched in 2020, is an example of an initiative put forth to leverage and disseminate knowledge on AI, expanding the fields of application and fostering the development and adaptation of cutting-edge technologies to explore how they can assist in tackling specific tasks during a public health crisis. Using this database, the article outlines AI as a hope for handling specific needs and tasks thus supporting humans in overcoming obstacles and improving the chances for an effective response. Other initiatives frame the uptake of AI from a regulatory and risk mitigation perspective, focusing on establishing frameworks for AI governance in an ethical and trustworthy manner by defining principles and standards that aim to protect the underlying values deemed fundamental. A third approach intends to portray how AI is more than a promise and has proved its role in global geographies through investment, successful testing and adoption.

Fontes Catarina, Corrigan Caitlin, Lütge Christoph

2023-Feb-03

AI Ethics, AI Governance, Artificial intelligence, Covid-19, Robotics

General General

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction.

In Biomedical signal processing and control

COVID-19 has spread all over the world, causing serious panic around the globe. Chest computed tomography (CT) images are integral in confirming COVID positive patients. Several investigations were conducted to improve or maintain the image reconstruction quality for the sample image reconstruction. Deep learning (DL) methods have recently been proposed to achieve fast reconstruction, but many have focused on a single domain, such as the image domain of k-space. In this research, the highly under-sampled enhanced battle royale self-attention based bi-directional long short-term (EBRSA-bi LSTM) CT image reconstruction model is proposed to reconstruct the image from the under-sampled data. The research is adapted with two phases, namely, pre-processing and reconstruction. The extended cascaded filter (ECF) is proposed for image pre-processing and tends to suppress the noise and enhance the reconstruction accuracy. In the reconstruction model, the battle royale optimization (BrO) is intended to diminish the loss function of the reconstruction network model and weight updation. The proposed model is tested with two datasets, COVID-CT- and SARS-CoV-2 CT. The reconstruction accuracy of the proposed model with two datasets is 93.5 % and 97.7 %, respectively. Also, the image quality assessment parameters such as Peak-Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Structural Similarity Index metric (SSIM) are evaluated, and it yields an outcome of (45 and 46 dB), (0.0026 and 0.0022) and (0.992, 0.996) with two datasets.

Sarvari A V P, Sridevi K

2023-May

Computed tomography (CT), Deep learning, Image reconstruction, K-space data, Under-sampling

General General

Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques.

In Heliyon

PURPOSE : The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task.

METHODS : This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms.

RESULTS : The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%).

CONCLUSIONS : The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.

Ilu Saratu Yusuf, Prasad Rajesh

2023-Feb

ARIMA, And clustering, Coronavirus, Feature selection, Machine learning, Prediction

Surgery Surgery

Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia.

In Frontiers in immunology ; h5-index 100.0

INTRODUCTION : Despite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive.

METHODS : Here, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients.

RESULTS : Differential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients.

DISCUSSION : Aberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression.

Jeon Kyeongseok, Kim Yuri, Kang Shin Kwang, Park Uni, Kim Jayoun, Park Nanhee, Koh Jaemoon, Shim Man-Shik, Kim Minsoo, Rhee Youn Ju, Jeong Hyeongseok, Lee Siyoung, Park Donghyun, Lim Jinyoung, Kim Hyunsu, Ha Na-Young, Jo Hye-Yeong, Kim Sang Cheol, Lee Ju-Hee, Shon Jiwon, Kim Hoon, Jeon Yoon Kyung, Choi Youn-Soo, Kim Hye Young, Lee Won-Woo, Choi Murim, Park Hyun-Young, Park Woong-Yang, Kim Yeon-Sook, Cho Nam-Hyuk

2023

COVID-19, IFNa, IL-12p40, SARS-CoV-2, inflammation, pneumonia

Public Health Public Health

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic.

In Frontiers in nutrition

OBJECTIVE : The COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.

METHODS : The present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants' data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.

RESULTS : Of participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R 2, MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.

CONCLUSION : During the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.

Wang Qihe, Chu Haiyun, Qu Pengfeng, Fang Haiqin, Liang Dong, Liu Sana, Li Jinliang, Liu Aidong

2023

BMI change, COVID-19 pandemic, China, doctors and nurses, machine learning

General General

A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions.

In Computer networks

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

Ali Yasir, Khan Habib Ullah

2023-Apr

COVID-19, Coronavirus, Healthcare, Mobile computing, Pervasive computing, SARS-COV-2

General General

Predicting the antigenic evolution of SARS-COV-2 with deep learning

bioRxiv Preprint

The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution via in silico directed evolution. As demonstrated by existing SARS-COV-2 variants, MLAEP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLAEP are also found in immunocompromised covid patients and newly emerging variants, like XBB1.5. The predictions of MLAEP were validated by conducting in vitro neutralizing antibody binding assay, which demonstrated that the model-generated variants displayed significantly increased immune evasion ability compared with the controls. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants. Our model is available at https://mlaep.cbrc.kaust.edu.sa.

Han, W.; Chen, N.; Xu, X.; Sahil, A.; Zhou, J.; Li, Z.; Zhong, H.; Gao, E.; Zhang, R.; Wang, Y.; Sun, S.; Gao, X.; Cheung, P. P.-H.

2023-02-14

Public Health Public Health

Home alone: A population neuroscience investigation of brain morphology substrates.

In NeuroImage ; h5-index 117.0

As a social species, ready exchange with peers is a pivotal asset - our "social capital". Yet, single-person households have come to pervade metropolitan cities worldwide, with unknown consequences in the long run. Here, we systematically explore the morphological manifestations associated with singular living in ∼40,000 UK Biobank participants. The uncovered population-level signature spotlights the highly associative default mode network, in addition to findings such as in the amygdala central, cortical and corticoamygdaloid nuclei groups, as well as the hippocampal fimbria and dentate gyrus. Both positive effects, equating to greater gray matter volume associated with living alone, and negative effects, which can be interpreted as greater grey matter associations with not living alone, were found across the cortex and subcortical structures Sex-stratified analyses revealed male-specific neural substrates, including somatomotor, saliency and visual systems, while female-specific neural substrates centred on the dorsomedial prefrontal cortex. In line with our demographic profiling results, the discovered neural pattern of living alone is potentially linked to alcohol and tobacco consumption, anxiety, sleep quality as well as daily TV watching. The persistent trend for solitary living will require new answers from public-health decision makers. SIGNIFICANCE STATEMENT: Living alone has profound consequences for mental and physical health. Despite this, there has been a rapid increase in single-person households worldwide, with the long-term consequences yet unknown. In the largest study of its kind, we investigate how the objective lack of everyday social interaction, through living alone, manifests in the brain. Our population neuroscience approach uncovered a gray matter signature that converged on the 'default network', alongside targeted subcortical, sex and demographic profiling analyses. The human urge for social relationships is highlighted by the evolving COVID-19 pandemic. Better understanding of how social isolation relates to the brain will influence health and social policy decision-making of pandemic planning, as well as social interventions in light of global shifts in houseful structures.

Noonan MaryAnn, Zajner Chris, Bzdok Danilo

2023-Feb-11

Bayesian hierarchical modelling, Population neuroscience, amygdala nuclei groups, hippocampus subfields, social brain

General General

Classifying COVID-19 patients from Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation.

In JMIR formative research

BACKGROUND : The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest X-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention.

OBJECTIVE : We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and Healthy patients from chest X-ray (CXR) images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared to existing models.

METHODS : A retrospective study was conducted to analyze 5 public datasets containing 4,200 chest X-ray (CXR) images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were pre-processed to undergo image segmentation, enhancement, and feature extraction. The best-performing machine learning technique was selected and combined into a Multi-Layer Hybrid Classification model for COVID-19 (MLHC-COVID-19). The model consisted of two layers. The first layer was designed to differentiate healthy subjects from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients.

RESULTS : The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F-measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis (CADx) system, which was made publicly available.

CONCLUSIONS : The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.

Phumkuea Thanakorn, Wongsirichot Thakerng, Damkliang Kasikrit, Navasakulpong Asma

2023-Feb-13

General General

Hybrid model for early identification post-Covid-19 sequelae.

In Journal of ambient intelligence and humanized computing

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

de Andrade Evandro Carvalho, Pinheiro Luana Ibiapina C C, Pinheiro Plácido Rogério, Nunes Luciano Comin, Pinheiro Mirian Calíope Dantas, Pereira Maria Lúcia Duarte, de Abreu Wilson Correia, Filho Raimir Holanda, Simão Filho Marum, Pinheiro Pedro Gabriel C D, Nunes Rafael Espíndola Comin

2023-Feb-06

Covid-19, Decision support systems, Hybrid model, Machine learning, Medical diagnostic optimization, Verbal decision analysis

General General

Adoption of AI in response to COVID-19-a configurational perspective.

In Personal and ubiquitous computing

Although the importance of artificial intelligence (AI) has often been highlighted in strategic agility and decision outcomes, whether it helps firms strengthen their competitiveness and the means firms use to achieve such competitiveness are still under-researched. Our research thus joins the recent discussion on digitalization trends and strategic responses to COVID-19 to better understand how firms strengthen their competitiveness during such challenging times. Namely, this study incorporates the strategic responses to COVID-19 into the technology-organization-environment (TOE) framework by investigating the impacts of different configurations of TOE contexts and strategic responses on a firm's competitive advantage. We used fuzzy-set qualitative comparative analysis to investigate how TOE contexts and strategic responses integrate into configurations and impact a firm's competiveness. By applying a configurational approach with data from 514 exporting firms in China, we find a strong indication of the equifinality of different strategies, indicating that multiple strategic paths can be used to respond to crises. The adoption of AI, while important, is not sufficient to enhance a firm's competitiveness. Our results stress the significance of data quality, organizational resources and capabilities, and digital business model innovation for AI adoption. We also identify successful strategic paths of AI adoption aversion and ambidextrous strategies. The findings have practical implications for firms seeking effective strategies to respond to future crises and sustain their competitive advantages.

Mi Lili, Liu Wei, Yuan Yu-Hsi, Shao Xuefeng, Zhong Yifan

2023-Feb-07

Artificial intelligence, Fuzzy-set qualitative comparative analysis, Strategic responses to COVID-19, Technology–organization–environment framework

General General

Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures.

In bioRxiv : the preprint server for biology

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human peripheral blood mononuclear cells (PBMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Zhou Manqi, Zhang Hao, Bai Zilong, Mann-Krzisnik Dylan, Wang Fei, Li Yue

2023-Jan-31

General General

Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities.

In Neural computing & applications

Early detection of the COVID-19 virus is an important task for controlling the spread of the pandemic. Imaging techniques such as chest X-ray are relatively inexpensive and accessible, but its interpretation requires expert knowledge to evaluate the disease severity. Several approaches for automatic COVID-19 detection using deep learning techniques have been proposed. While most approaches show high accuracy on the COVID-19 detection task, there is not enough evidence on external evaluation for this technique. Furthermore, data scarcity and sampling biases make difficult to properly evaluate model predictions. In this paper, we propose stochastic gradient Langevin dynamics (SGLD) to take into account the model uncertainty. Four different deep learning architectures are trained using SGLD and compared to their baselines using stochastic gradient descent. The model uncertainties are also evaluated according to their convergence properties and the leave-one-out predictive densities. The proposed approach is able to reduce overconfidence of the baseline estimators while also retaining predictive accuracy for the best-performing cases.

Hernández Sergio, López-Córtes Xaviera

2023-Feb-06

Bayesian learning, COVID X-ray, Markov chain Monte Carlo

Public Health Public Health

Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020.

In China CDC weekly

WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? : People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns.

WHAT IS ADDED BY THIS REPORT? : This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time.

WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? : Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.

Du Zhanwei, Zhang Xiao, Wang Lin, Yao Sidan, Bai Yuan, Tan Qi, Xu Xiaoke, Pei Sen, Xiao Jingyi, Tsang Tim K, Liao Qiuyan, Lau Eric H Y, Wu Peng, Gao Chao, Cowling Benjamin J

2023-Jan-27

COVID-19, Collective Behavior, Hong Kong

General General

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.

In Applied intelligence (Dordrecht, Netherlands)

According to the World Health Organization, millions of infections and a lot of deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Since 2020, a lot of computer science researchers have used convolutional neural networks (CNNs) to develop interesting frameworks to detect this disease. However, poor feature extraction from the chest X-ray images and the high computational cost of the available models introduce difficulties for an accurate and fast COVID-19 detection framework. Moreover, poor feature extraction has caused the issue of 'the curse of dimensionality', which will negatively affect the performance of the model. Feature selection is typically considered as a preprocessing mechanism to find an optimal subset of features from a given set of all features in the data mining process. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier approaches. To achieve the specified goal, we design a mechanism for feature extraction based on shallow conventional neural network (SCNN) and used an effective method for selecting features by utilizing the newly developed optimization algorithm, Q-Learning Embedded Sine Cosine Algorithm (QLESCA). Support vector machines (SVMs) are used as a classifier. Five publicly available chest X-ray image datasets, consisting of 4848 COVID-19 images and 8669 non-COVID-19 images, are used to train and evaluate the proposed model. The performance of the QLESCA is evaluated against nine recent optimization algorithms. The proposed method is able to achieve the highest accuracy of 97.8086% while reducing the number of features from 100 to 38. Experiments prove that the accuracy of the model improves with the usage of the QLESCA as the dimensionality reduction technique by selecting relevant features.

Hamad Qusay Shihab, Samma Hussein, Suandi Shahrel Azmin

2023-Feb-06

Features extraction, Features selection, Q-learning embedded sine cosine algorithm (QLESCA), SVM, Shallow convolutional neural networks, Swarm intelligence

Public Health Public Health

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

In Scientific reports ; h5-index 158.0

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Barough Siavash Shirzadeh, Safavi-Naini Seyed Amir Ahmad, Siavoshi Fatemeh, Tamimi Atena, Ilkhani Saba, Akbari Setareh, Ezzati Sadaf, Hatamabadi Hamidreza, Pourhoseingholi Mohamad Amin

2023-Feb-10

General General

A year of pandemic: Levels, changes and validity of well-being data from Twitter. Evidence from ten countries.

In PloS one ; h5-index 176.0

We use daily happiness scores (Gross National Happiness (GNH)) to illustrate how happiness changed throughout 2020 in ten countries across Europe and the Southern hemisphere. More frequently and regularly available than survey data, the GNH reveals how happiness sharply declined at the onset of the pandemic and lockdown, quickly recovered, and then trended downward throughout much of the year in Europe. GNH is derived by applying sentiment and emotion analysis-based on Natural Language Processing using machine learning algorithms-to Twitter posts (tweets). Using a similar approach, we generate another 11 variables: eight emotions and three new context-specific variables, in particular: trust in national institutions, sadness in relation to loneliness, and fear concerning the economy. Given the novelty of the dataset, we use multiple methods to assess validity. We also assess the correlates of GNH. The results indicate that GNH is negatively correlated with new COVID-19 cases, containment policies, and disgust and positively correlated with staying at home, surprise, and generalised trust. Altogether the analyses indicate tools based on Big Data, such as the GNH, offer relevant data that often fill information gaps and can valuably supplement traditional tools. In this case, the GNH results suggest that both the severity of the pandemic and containment policies negatively correlated with happiness.

Sarracino Francesco, Greyling Talita, O’Connor Kelsey, Peroni Chiara, Rossouw Stephanié

2023

General General

Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning.

In PloS one ; h5-index 176.0

RESEARCH MOTIVATION : Recently, the digital divide problem among elderly individuals has been intensifying. A larger problem is that the level of use of digital technology varies from person to person. Therefore, a digital divide may even exist among elderly individuals. Considering the recent accelerating digital transformation in our society, it is highly likely that elderly individuals are experiencing many difficulties in their daily life. Therefore, it is necessary to quickly address and manage these difficulties.

RESEARCH OBJECTIVE : This study aims to predict the digital divide in the elderly population and provide essential insights into managing it. To this end, predictive analysis is performed using public data and machine learning techniques.

METHODS AND MATERIALS : This study used data from the '2020 Report on Digital Information Divide Survey' published by the Korea National Information Society Agency. In establishing the prediction model, various independent variables were used. Ten variables with high importance for predicting the digital divide were identified and used as critical, independent variables to increase the convenience of analyzing the model. The data were divided into 70% for training and 30% for testing. The model was trained on the training set, and the model's predictive accuracy was analyzed on the test set. The prediction accuracy was analyzed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and eXtreme gradient boosting (XGBoost). A convolutional neural network (CNN) was used to further improve the accuracy. In addition, the importance of variables was analyzed using data from 2019 before the COVID-19 outbreak, and the results were compared with the results from 2020.

RESULTS : The study results showed that the variables with high importance in the 2020 data predicting the digital divide of elderly individuals were the demographic perspective, internet usage perspective, self-efficacy perspective, and social connectedness perspective. These variables, as well as the social support perspective, were highly important in 2019. The highest prediction accuracy was achieved using the CNN-based model (accuracy: 80.4%), followed by the XGBoost model (accuracy: 79%) and LR model (accuracy: 78.3%). The lowest accuracy (accuracy: 72.6%) was obtained using the DT model.

DISCUSSION : The results of this analysis suggest that support that can strengthen the practical connection of elderly individuals through digital devices is becoming more critical than ever in a situation where digital transformation is accelerating in various fields. In addition, it is necessary to comprehensively use classification algorithms from various academic fields when constructing a classification model to obtain higher prediction accuracy.

CONCLUSION : The academic significance of this study is that the CNN, which is often employed in image and video processing, was extended and applied to a social science field using structured data to improve the accuracy of the prediction model. The practical significance of this study is that the prediction models and the analytical methodologies proposed in this article can be applied to classify elderly people affected by the digital divide, and the trained models can be used to predict the people of younger generations who may be affected by the digital divide. Another practical significance of this study is that, as a method for managing individuals who are affected by a digital divide, the self-efficacy perspective about acquiring and using ICTs and the socially connected perspective are suggested in addition to the demographic perspective and the internet usage perspective.

Park Jung Ryeol, Feng Yituo

2023

General General

Blood transcriptome and machine learning identified the crosstalk between COVID-19 and fibromyalgia: a preliminary study.

In Clinical and experimental rheumatology ; h5-index 43.0

OBJECTIVES : The COVID-19 pandemic caused by SARS-CoV-2 has seriously threatened the human health. Growing evidence shows that COVID-19 patients who recovery will persist with symptoms of fibromyalgia (FM). However, the common molecular mechanism between COVID-19 and FM remains unclear.

METHODS : We obtained blood transcriptome data of COVID-19 (GSE177477) and FM (GSE67311) patients from GEO database, respectively. Subsequently, we applied Limma, GSEA, Wikipathway, KEGG, GO, and machine learning analysis to confirm the common pathogenesis between COVID-19 and FM, and screened key genes for the diagnosis of COVID-19 related FM.

RESULTS : A total of 2505 differentially expressed genes (DEGs) were identified in the FM dataset. Functional enrichment analysis revealed that the occurrence of FM was intimately associated with viral infection. Moreover, WGCNA analysis identified 243 genes firmly associated with the pathological process of COVID-19. Subsequently, 50 common genes were screened between COVID-19 and FM, and functional enrichment analysis of these common genes primarily involved in immunerelated pathways. Among these common genes, 3 key genes were recognised by machine learning for the diagnosis of COVID-19 related FM. We also developed a diagnostic nomogram to predict the risk of FM occurrence which showed excellent predictive performance. Finally, we found that these 3 key genes were closely relevant to immune cells and screened potential drugs that interacted with the key genes.

CONCLUSIONS : Our study revealed the bridge role of immune dysregulation between COVID-19 and fibromyalgia, and screened underlying biomarkers to provide new clues for further clinical research.

Zhang Zhao, Zhu Zhijie, Liu Dong, Mi Zhenz, Tao Huiren, Fan Hongbin

2023-Feb-08

Public Health Public Health

Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example.

In Frontiers in big data

Machine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control.

Zheng Liang, Chen Yile, Jiang Shan, Song Junxin, Zheng Jianyi

2023

CGAN, epidemic hotspots, machine learning, risk assessment, urban public health

General General

Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19.

In Frontiers in neuroinformatics

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.

Wang Mingjing, Chen Long, Heidari Ali Asghar, Chen Huiling

2022

CEC2014 benchmark functions, COVID-19, Harris Hawks optimization, fireworks algorithm, numerical optimization

General General

Prediction models for the impact of the COVID-19 pandemic on research activities of Japanese nursing researchers using deep learning.

In Japan journal of nursing science : JJNS

AIM : This study aimed to construct and evaluate prediction models using deep learning to explore the impact of attributes and lifestyle factors on research activities of nursing researchers during the COVID-19 pandemic.

METHODS : A secondary data analysis was conducted from a cross-sectional online survey by the Japanese Society of Nursing Science at the inception of the COVID-19 pandemic. A total of 1089 respondents from nursing faculties were divided into a training dataset and a test dataset. We constructed two prediction models with the training dataset using artificial intelligence (AI) predictive analysis tools; motivation and time were used as predictor items for negative impact on research activities. Predictive factors were attributes, lifestyle, and predictor items for each other. The models' accuracy and internal validity were evaluated using an ordinal logistic regression analysis to assess goodness-of-fit; the test dataset was used to assess external validity. Predicted contributions by each factor were also calculated.

RESULTS : The models' accuracy and goodness-of-fit were good. The prediction contribution analysis showed that no increase in research motivation and lack of increase in research time strongly influenced each other. Other factors that negatively influenced research motivation and research time were residing outside the special alert area and lecturer position and living with partner/spouse and associate professor position, respectively.

CONCLUSIONS : Deep learning is a research method enabling early prediction of unexpected events, suggesting new applicability in nursing science. To continue research activities during the COVID-19 pandemic and future contingencies, the research environment needs to be improved, workload corrected by position, and considered in terms of work-life balance.

Lee Kumsun, Takahashi Fusako, Kawasaki Yuki, Yoshinaga Naoki, Sakai Hiroko

2023-Feb-09

COVID-19, Japan, deep learning, nursing research, university

Pathology Pathology

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms.

In PloS one ; h5-index 176.0

BACKGROUND : Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.

METHODS : 1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.

RESULTS : We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

CONCLUSIONS : We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Epsi Nusrat J, Powers John H, Lindholm David A, Mende Katrin, Malloy Allison, Ganesan Anuradha, Huprikar Nikhil, Lalani Tahaniyat, Smith Alfred, Mody Rupal M, Jones Milissa U, Bazan Samantha E, Colombo Rhonda E, Colombo Christopher J, Ewers Evan C, Larson Derek T, Berjohn Catherine M, Maldonado Carlos J, Blair Paul W, Chenoweth Josh, Saunders David L, Livezey Jeffrey, Maves Ryan C, Sanchez Edwards Margaret, Rozman Julia S, Simons Mark P, Tribble David R, Agan Brian K, Burgess Timothy H, Pollett Simon D

2023

General General

Association of Recent SARS-CoV-2 Infection With New-Onset Alcohol Use Disorder, January 2020 Through January 2022.

In JAMA network open

IMPORTANCE : The COVID-19 pandemic affects many diseases, including alcohol use disorders (AUDs). As the pandemic evolves, understanding the association of a new diagnosis of AUD with COVID-19 over time is required to mitigate negative consequences.

OBJECTIVE : To examine the association of COVID-19 infection with new diagnosis of AUD over time from January 2020 through January 2022.

DESIGN, SETTING, AND PARTICIPANTS : In this retrospective cohort study of electronic health records of US patients 12 years of age or older, new diagnoses of AUD were compared between patients with COVID-19 and patients with other respiratory infections who had never had COVID-19 by 3-month intervals from January 20, 2020, through January 27, 2022.

EXPOSURES : SARS-CoV-2 infection or non-SARS-CoV-2 respiratory infection.

MAIN OUTCOMES AND MEASURES : New diagnoses of AUD were compared in COVID-19 and propensity score-matched control cohorts by hazard ratios (HRs) and 95% CIs from either 14 days to 3 months or 3 to 6 months after the index event.

RESULTS : This study comprised 1 201 082 patients with COVID-19 (56.9% female patients; 65.7% White; mean [SD] age at index, 46.2 [18.9] years) and 1 620 100 patients with other respiratory infections who had never had COVID-19 (60.4% female patients; 71.1% White; mean [SD] age at index, 44.5 [20.6] years). There was a significantly increased risk of a new diagnosis of AUD in the 3 months after COVID-19 was contracted during the first 3 months of the pandemic (block 1) compared with control cohorts (HR, 2.53 [95% CI, 1.82-3.51]), but the risk decreased to nonsignificance in the next 3 time blocks (April 2020 to January 2021). The risk for AUD diagnosis increased after infection in January to April 2021 (HR, 1.30 [95% CI, 1.08-1.56]) and April to July 2021 (HR, 1.80 [95% CI, 1.47-2.21]). The result became nonsignificant again in blocks 7 and 8 (COVID-19 diagnosis between July 2021 and January 2022). A similar temporal pattern was seen for new diagnosis of AUD 3 to 6 months after infection with COVID-19 vs control index events.

CONCLUSIONS AND RELEVANCE : Elevated risk for AUD after COVID-19 infection compared with non-COVID-19 respiratory infections during some time frames may suggest an association of SARS-CoV-2 infection with the pandemic-associated increase in AUD. However, the lack of excess hazard in most time blocks makes it likely that the circumstances surrounding the pandemic and the fear and anxiety they created also were important factors associated with new diagnoses of AUD.

Olaker Veronica R, Kendall Ellen K, Wang Christina X, Parran Theodore V, Terebuh Pauline, Kaelber David C, Xu Rong, Davis Pamela B

2023-Feb-01

General General

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.

In Nanoscale advances

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

Zhou Hong, Xu Liangge, Ren Zhihao, Zhu Jiaqi, Lee Chengkuo

2023-Jan-31

General General

Learning virus genotype-fitness landscape in embedding space

bioRxiv Preprint

Predicting the SARS-CoV-2 epidemic and "immune escape" mutations remain crucial problems. We present a theoretical framework called Phenotype-Embedding (P-E) theorem and prove that the virus fitness can calculate by selecting appropriate sequence embedding under the VAE framework. Starting from the P-E theorem and based on a modified Transformer model, we obtain a calculable quantitative relationship between "immune escape" mutations and the fitness of the virus lineage and plot a genotype-fitness landscape in the embedded space. We accurately calculated the viral fitness and basic replication number (R0) using only the sequence data of SARS-CoV-2 spike protein. In addition, our model can simulate viral neutral evolution and spatio-temporal selection, decipher the effects of epistasis and recombination, and more accurately predict viral mutations associated with immune escape. Our work provides a theoretical framework for constructing genotype-phenotype landscapes and a paradigm for the interpretability of deep learning in virus evolution research.

Liu, Y.; Luo, Y.; Lu, X.; Gao, H.; He, R.; Zhang, X.; Zhang, X.; Li, Y.

2023-02-10

Public Health Public Health

Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance.

In Frontiers in public health

BACKGROUND : Precise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.

METHODS : To avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries.

RESULTS : A total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring.

CONCLUSION : An efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.

Huang Qiang, Qiu Huining, Bible Paul W, Huang Yong, Zheng Fangfang, Gu Jing, Sun Jian, Hao Yuantao, Liu Yu

2023

SARS-CoV-2, co-mutation, community detection, network, surveillance

General General

Deep learning approaches to viral phylogeography are fast and as robust as likelihood methods to model misspecification

bioRxiv Preprint

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among five locations and found they achieve similar levels of accuracy to Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Thompson, A.; Liebeskind, B.; Scully, E. J.; Landis, M.

2023-02-10

General General

A data-driven approach to the "Everesting" cycling challenge.

In Scientific reports ; h5-index 158.0

The "Everesting" challenge is a cycling activity in which a cyclist repeats a hill until accumulating an elevation gain equal to the elevation of Mount Everest in a single ride. The challenge experienced a surge in interest during the COVID-19 pandemic and the cancelation of cycling races around the world that prompted cyclists to pursue alternative, individual activities. The time to complete the Everesting challenge depends on the fitness and talent of the cyclist, but also on the length and gradient of the hill, among other parameters. Hence, preparing an Everesting attempt requires understanding the relationship between the Everesting parameters and the time to complete the challenge. We use web-scraping to compile a database of publicly available Everesting attempts, and we quantify and rank the parameters that determine the time to complete the challenge. We also use unsupervised machine learning algorithms to segment cyclists into distinct groups according to their characteristics and performance. We conclude that the power per unit body mass of the cyclist and the tradeoff between the gradient of the hill and the distance are the most important considerations when attempting the Everesting challenge. As such, elite cyclists best select a hill with gradient > 12%, whereas amateur and recreational cyclists best select a hill with gradient < 10% to minimize the time to complete the Everesting challenge.

Seo Junhyeon, Raeymaekers Bart

2023-Feb-08

General General

Outcome classification model for Covid-19 patients using artificial intelligence.

In Salud publica de Mexico

Not available.

Saad-Manzanera María Isabel, Hernández-Galván Jesús Alan, González-Cristóbal Sofía Carolina, Vázquez-Torres Eliden, Ramírez-Alonso Graciela María de Jesús, Valenzuela-Aldaba Yaeli Estefanía, Hinojos-Gallardo Luis Carlos, Enríquez-Sánchez Luis Bernardo

2023-Jan-02

General General

COVID-19 Predictive Models Based on Grammatical Evolution.

In SN computer science

A feature construction method that incorporates a grammatical guided procedure is presented here to predict the monthly mortality rate of the COVID-19 pandemic. Three distinct use cases were obtained from publicly available data and three corresponding datasets were created for that purpose. The proposed method is based on constructing artificial features from the original ones. After the artificial features are generated, the original data set is modified based on these features and a machine learning model, such as an artificial neural network, is applied to the modified data. From the comparative experiments done, it was clear that feature construction has an advantage over other machine learning methods for predicting pandemic elements.

Tsoulos Ioannis G, Stylios Chrysostomos, Charalampous Vlasis

2023

COVID-19, Feature construction, Grammatical evolution, Machine learning, Predictive models

General General

Recent development of machine learning models for the prediction of drug-drug interactions.

In The Korean journal of chemical engineering

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Hong Eujin, Jeon Junhyeok, Kim Hyun Uk

2023

Adverse Drug Reaction, Drug-Drug Interaction, Featurization, Machine Learning, Polypharmacy

General General

A smartphone-based zero-effort method for mitigating epidemic propagation.

In EURASIP journal on advances in signal processing

A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

Wang Qu, Fu Meixia, Wang Jianquan, Sun Lei, Huang Rong, Li Xianda, Jiang Zhuqing

2023

COVID-19, Contact tracing, Epidemic warning, Human activity recognition, Indoor positioning, Social distance

General General

Real-time face mask position recognition system based on MobileNet model.

In Smart health (Amsterdam, Netherlands)

COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen's Kappa) of this model on the proposed dataset and MaskedFace-Net, a publicly available synthetic dataset created by image editing. Its performance is also compared to other existing methods. The proposed MobileNet is found as the best model providing an accuracy, F1-score, and Cohen's Kappa of 99.23%, 99.22%, and 99.19%, respectively for face mask position recognition. It outperforms the accuracy of the best existing model by about 2%. Finally, an automatic face mask position recognition system has been developed, which can recognize if an individual is wearing a mask correctly or incorrectly. The proposed model performs very well with no drop in recognition accuracy from real images captured by a camera.

Rahman Md Hafizur, Jannat Mir Kanon Ara, Islam Md Shafiqul, Grossi Giuliano, Bursic Sathya, Aktaruzzaman Md

2023-Jan-31

COVID-19, Dataset, Face-mask position recognition, MobileNet, Real-time, Transfer learning

Internal Medicine Internal Medicine

Machine learning models for predicting severe COVID-19 outcomes in hospitals.

In Informatics in medicine unlocked

The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.821) and the need for mechanical ventilation (AUC = 0.654) from a few laboratory data of the first 24 h after admission. Models based on dichotomous features indicating whether a laboratory value exceeds or falls below a threshold perform nearly as good as models based on numerical features. We devise completely data-driven and interpretable machine-learning models for the prediction of in-hospital mortality, transfer to ICU and mechanical ventilation for hospitalized Covid-19 patients within 24 h after admission. Numerical values of. CRP and blood sugar and dichotomous indicators for increased partial thromboplastin time (PTT) and glutamic oxaloacetic transaminase (GOT) are amongst the best predictors.

Wendland Philipp, Schmitt Vanessa, Zimmermann Jörg, Häger Lukas, Göpel Siri, Schenkel-Häger Christof, Kschischo Maik

2023

Clinical decision support, Covid-19, Machine learning, Predictive modelling

General General

Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs.

In Journal of visual communication and image representation

The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.5D representation of volumetric chest CTs that allows exploring efficient 2D deep learning architectures while requiring volume-level annotations only. Our learning approach uses a siamese feature extraction backbone applied to each lung. It combines these features into a classification head that explores a novel combination of Squeeze-and-Excite strategies with Class Activation Maps. We experimented with public and in-house datasets and compared our results with state-of-the-art techniques. Our analyses show that our method provides better or comparable prediction quality and accurately distinguishes COVID-19 infections from other kinds of pneumonia and healthy lungs.

da Silveira Thiago L T, Pinto Paulo G L, Lermen Thiago S, Jung Cláudio R

2023-Mar

2.5D representation, COVID-19 diagnosis, Ground-glass opacity, Omnidirectional imaging

General General

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach.

In New generation computing

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R 2 score.

Satapathy Sandeep Kumar, Saravanan Shreyaa, Mishra Shruti, Mohanty Sachi Nandan

2023-Feb-01

Feature selection, Machine learning, Multidimensional, Poverty, Prediction, Regression

General General

Transfer learning for the efficient detection of COVID-19 from smartphone audio data.

In Pervasive and mobile computing

Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users' mobile devices. Considering these issues, and the limited amount of available data, in this paper we present the experimental evaluation of 3 different deep learning models, compared also with hand-crafted features, and of two main approaches of transfer learning in the considered scenario: both feature extraction and fine-tuning. Specifically, we considered VGGish, YAMNET, and L3-Net (including 12 different configurations) evaluated through user-independent experiments on 4 different datasets (13,447 samples in total). Results clearly show the advantages of L3-Net in all the experimental settings as it overcomes the other solutions by 12.3% in terms of Precision-Recall AUC as features extractor, and by 10% when the model is fine-tuned. Moreover, we note that to fine-tune only the fully-connected layers of the pre-trained models generally leads to worse performances, with an average drop of 6.6% with respect to feature extraction. Finally, we evaluate the memory footprints of the different models for their possible applications on commercial mobile devices.

Campana Mattia Giovanni, Delmastro Franca, Pagani Elena

2023-Feb

COVID-19, Deep audio embeddings, Deep learning, Transfer learning, m-health

General General

A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans.

In Neuroscience informatics

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.

Hasija Sanskar, Akash Peddaputha, Bhargav Hemanth Maganti, Kumar Ankit, Sharma Sanjeev

2022-Dec

CNN, COVID-19, Chest CT scan, Classification metrics, Multiclass classification, Two binary classifications

General General

Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA.

In Information processing & management

Infodemics are intertwined with the COVID-19 pandemic, affecting people's perception and social order. To curb the spread of COVID-19 related false rumors, fuzzy-set qualitative comparative analysis (fsQCA) is used to find configurational pathways to enhance rumor refutation effectiveness. In this paper, a total of 1,903 COVID-19 related false rumor refutation microblogs on Sina Weibo are collected by a web crawler from January 1, 2022 to April 20, 2022, and 10 main conditions affecting rumor refutation effectiveness index (REI) are identified based on "three rules of epidemics". To reduce data redundancy, five ensemble machine learning models are established and tuned, among which Light Gradient Boosting Machine (LGBM) regression model has the best performance. Then five core conditions are extracted by feature importance ranking of LGBM. Based on fsQCA with the five core conditions, REI enhancement can be achieved through three different pathway elements configurations solutions: "Highly influential microblogger * high followers' stickiness microblogger", "high followers' stickiness microblogger * highly active microblogger * concise information description" and "high followers' stickiness microblogger * the sentiment tendency of the topic * concise information description". Finally, decision-making suggestions for false rumor refutation platforms and new ideas for improving false rumor refutation effectiveness are proposed. The innovation of this paper reflects in exploring the REI enhancement strategy from the perspective of configuration for the first time.

Li Zongmin, Zhao Ye, Duan Tie, Dai Jingqi

2023-May

COVID-19, Fuzzy-set qualitative comparative analysis (FsQCA), Infodemic, LGBM regression model, Rumor refutation effectiveness

General General

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective.

In Computers & industrial engineering

The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.

Ahmed Tazim, Lekha Karmaker Chitra, Benta Nasir Sumaiya, Abdul Moktadir Md, Kumar Paul Sanjoy

2023-Jan-31

Bayesian Best-Worst Method, Industry 5.0, Post-COVID-19 pandemic, artificial intelligence, supply chain resilience

General General

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.

In Scientific reports ; h5-index 158.0

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

Clyde Austin, Liu Xuefeng, Brettin Thomas, Yoo Hyunseung, Partin Alexander, Babuji Yadu, Blaiszik Ben, Mohd-Yusof Jamaludin, Merzky Andre, Turilli Matteo, Jha Shantenu, Ramanathan Arvind, Stevens Rick

2023-Feb-06

General General

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

In Journal of intensive care medicine ; h5-index 29.0

BACKGROUND : Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.

METHODS : We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.

RESULTS : The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.

CONCLUSIONS : Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

Siepel Sander, Dam Tariq A, Fleuren Lucas M, Girbes Armand R J, Hoogendoorn Mark, Thoral Patrick J, Elbers Paul W G, Bennis Frank C

2023-Feb-06

clinical phenotype half-life, clinical phenotypes, clustering, coronavirus disease 2019, endotypes, intensive care, subphenotypes

General General

A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models.

In Journal of real-time image processing

As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. Depending on the size of the indoors, the number of persons that can fit in that area varies. Then, the size of the indoor area should be measured and the maximum number of people should be calculated accordingly. Computers can be used to ensure the correct application of the capacity rule in indoors monitored by cameras. In this study, a method is proposed to measure the size of a prespecified region in the video and count the people there in real time. According to this method: (1) predetermining the borders of a region on the video, (2) identification and counting of people in this specified region, (3) it is aimed to estimate the size of the specified area and to find the maximum number of people it can take. For this purpose, the You Only Look Once (YOLO) object detection model was used. In addition, Microsoft COCO dataset pre-trained weights were used to identify and label persons. YOLO models were tested separately in the proposed method and their performances were analyzed. Mean average precision (mAP), frame per second (fps), and accuracy rate metrics were found for the detection of persons in the specified region. While the YOLO v3 model achieved the highest value in accuracy rate and mAP (both 0.50 and 0.75) metrics, the YOLO v5s model achieved the highest fps rate among non-Tiny models.

Gündüz Mehmet Şirin, Işık Gültekin

2023

Area estimation, Deep learning, People counting, Person detection, Real-time video processing, YOLO

General General

[Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia].

In Radiologia

OBJECTIVE : Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS : Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS : One hundred fourteen patients (57.4 ± 14.2 years, 65 -57%- men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26 seconds of radiological time.

CONCLUSIONS : Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Plasencia-Martínez Juana María, Pérez-Costa Rafael, Ballesta-Ruiz Mónica, María García-Santos José

2023-Jan-31

AI (Artificial Intelligence), Biomedical Technology, COVID 19, Coronavirus, Prognoses, Radiography

Public Health Public Health

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

In Zeitschrift fur Gesundheitswissenschaften = Journal of public health

AIM : This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown.

SUBJECT AND METHODS : After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results.

RESULTS : First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy.

CONCLUSION : Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

Che ShaoPeng, Wang Xiaoke, Zhang Shunan, Kim Jang Hyun

2023-Jan-31

COVID-19, Crisis and Emergency Risk Communication, Daily new cases, Deep learning, Public health agency, Semantic network analysis

Public Health Public Health

Whale Optimization with Random Contraction and Rosenbrock Method for COVID-19 disease prediction.

In Biomedical signal processing and control

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

Zhang Meilin, Wu Qianxi, Chen Huiling, Asghar Heidari Ali, Cai Zhennao, Li Jiaren, Md Abdelrahim Elsaid, Mansour Romany F

2023-Feb-01

COVID-19, Feature selection, Random contraction strategy, Rosenbrock method, Swarm intelligence, Whale optimization algorithm

General General

Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms.

In Procedia computer science

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

Shobhika Kumar, Prashant Chandra

2023

COVID-19, DASS, Machine learning algorithms, mental health

General General

A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk.

In Procedia computer science

Corona Virus Disease 2019 (COVID-19) is caused by Severe Acute Syndrome Corona Virus 2 (SARS-COV-2). It has become a pandemic disease of the 21st century, killing many lives. During this pandemic situation, precautious measures like social distancing and wearing face mask are being followed globally to break the COVID chain. A pre-programmed viewing system is needed to monitor whether these COVID-19 appropriate behaviours are being followed by the commoners and to ensure COVID-19 preventive measures are followed appropriately. In this work, a deep learning based predictive model and live risk analysis application has been proposed, which detects the high-risk prone areas based on social distancing measures among individuals and face mask wearing tendency of the commoners. The proposed system utilizes ImageNet-1000 dataset for human detection using You Only Look Once (YOLOv3) object detection algorithm; Residual Neural Network (ResNet50v2) uses Kaggle dataset and Real-World Masked Face Dataset (RMFD) for detecting if the persons are face masked or not. Detected human beings (in side-view) are transformed to top view using Top-View Transform Model (TVTM) followed by the calculation of interpersonal distance between the pedestrians and categorized them into three classes include high risk, medium risk, low risk. This unified predictive model provided an accuracy of 97.66%, precision of 97.84%, and F1-Score of 97.92%.

Kaviya P, Chitra P, Selvakumar B

2023

COVID-19, Deep Learning, Face Mask Detection, ResNet50v2, Social Distance Prediction, Top View Transform Model (TVTM), YOLOv3

General General

Coronavirus disease identification using Multi-subband feature analysis in DWT domain.

In Procedia computer science

Coronavirus disease early identification and differentiating it with other lung infections is a complex and time-consuming task. At present RT-PCR and Antigen tests are used for diagnosis, but the whole process is tedious, time exhausting and sometimes gives inaccurate results. Radiological scans like CT scan and X-rays are often considered for confirmation of infection, as it contains vital information about region of infection, disease state and severity, texture, size and opacity of infection. Automated machine learning techniques along with CXR (Chest X-ray) images can serve as alternative approach for Covid-19 diagnosis and differentiating it with other health conditions. In this work, Covid-19 disease identification is performed based on multi-subband feature extraction using 2D Discrete Wavelet Transform (DWT) on CX-Ray images. The CX-ray images are decomposed into multi-subbands of frequencies using DWT. The quarter-sized decomposed low and high frequency components are concatenated into single feature vector. In order to find suitable wavelet filter for extracting features from CX-ray images, a rigorous experimentation is carried out among various wavelet families such as Haar, Daubechies, Symlets, Biorthogonal and their respective members that have different vanishing moment and regularity properties. The feature vector is then used for training machine learning model based on support vector machine classifier. Experimental result shows that the classification model based on Haar wavelet feature extraction performs better as compared to other wavelet families with classification accuracy of 100%.

Ali Nikhat, Yadav Jyotsna

2023

Covid-19, Discrete wavelet transform (DWT), X-ray, classification, machine learning, multi-subband

General General

Data Mining Based Techniques for Covid-19 Predictions.

In Procedia computer science

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic.

Rane Rahul, Dubey Aditya, Rasool Akhtar, Wadhvani Rajesh

2023

Deep Learning Models, Soft Computing-based Models, Stochastic Forecasting Models, Supervised ML Models

General General

SCS-Net: An efficient and practical approach towards Face Mask Detection.

In Procedia computer science

Much work has been done in the computer vision domain for the problem of facial mask detection to curb the spread of the Coronavirus disease (COVID-19). Preventive measures developed using deep learning-based models have got enormous attention. With the state-of-the-art results touching perfect accuracies on various models and datasets, two very practical problems are still not addressed - the deployability of the model in the real world and the crucial cases of incorrectly worn masks. To this end, our method proposes a lightweight deep learning model with just 0.12M parameters having up to 496 times reduction as compared to some of the existing models. Our novel architecture of the deep learning model is designed for practical implications in the real world. We also augment an existing dataset with a large set of incorrectly masked face images leading to a more balanced three-class classification problem. A large collection of 25296 synthetically designed incorrect face mask images are provided. This is the first of its kind of data to be proposed with equal diversity and quantity. The proposed model achieves a competitive accuracy of 95.41% on two class classification and 95.54% on the extended three class classification with minimum number of parameters in comparison. The performance of the proposed system is assessed with various state-of-the-art literature and experimental results indicate that our solution is more realistic and rational than many existing works which use overly massive models unsuitable for practical deployability.

Masud Umar, Siddiqui Momin, Sadiq Mohd, Masood Sarfaraz

2023

CNNs, cosine similarity, covid-19, deep learning, face mask detection, image classification

General General

VGG-COVIDNet: A Novel model for COVID detection from X-Ray and CT Scan images.

In Procedia computer science

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan.

Goyal Lakshay, Dhull Anuradha, Singh Akansha, Kukreja Sonal, Singh Krishna Kant

2023

Covid-19;Deep Learning, VGG-NET;Medical Iamging

Radiology Radiology

AMSFMap Methodology to improve prediction accuracy of CNN model for Covid19 using X-ray images.

In Procedia computer science

A serious medical issue reported at the center of media worldwide, Since December, 2019 is the Covid19 pandemic. As declared by World Health Organization, confirmed cases of Covid19 have been 579,893,790 including 6,415,070 deaths as of 29 July 2022. Even new cases reported in last 24 hours are 20,409 in India. This needs to diagnose and timely treatment of Covid-19 is essential to prevent hurdles including death. The author developed deep learning based Covid19 diagnosis and severity prediction models using x-ray images with hope that this technology can increase access to radiology expertise in remote places where availability of expert radiologist is limited. The researchers proposed and implemented Attentive Multi Scale Feature map based deep Network (AMSF-Net) for x- ray image classification with improved accuracy. In binary classification, x-ray images are classified as normal or Covid19. Multiclass classification classifies x-ray images into mild, moderate or severe infection of Covid19. The researchers utilized lower layers features in addition to features from highest level with different scale to increase ability of CNN to learn fine-grained features. Channel attention also incorporated to amplify features of important channels. ROI based cropping and AHE employed to enhance content of training image. Image augmentation utilized to increase dataset size. To address the issue of the class imbalance problem, focal loss has been applied. Sensitivity, precision, accuracy and F1 score metrics are used for performance evaluation. The author achieved 78% accuracy for binary classification. Precision, recall and F1 score values for positive class is 85, 67 and 75, respectively while 73, 88 and 80 for negative class. Classification accuracy of mild, moderate and sever class is 90, 97 and 96. Average accuracy of 95 % achieved with superior performance compared to existing methods.

Chauhan Hetal, Modi Kirit

2023

CNN, Channel Attention, Covid-19 Diagnosis, Deep Learning, Multi Scale Features, Severity Prediction

General General

COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net.

In Procedia computer science

Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging. The researchers investigated image preprocessing techniques to accomplish this problem and to enable more accurate segmentation by the AI-based systems. This study proposes a COVID-19 Lung-CT segmentation system based on histogram-based non-parametric region localization and enhancement (LE) methods prior to the U-Net architecture. The COVID-19-infected lung CT images were initially processed by the LE method, and the infected regions were detected and enhanced to provide more discriminative features to the deep learning segmentation methods. The U-Net is trained using the enhanced images to segment the regions affected by COVID-19. The proposed system achieved 97.75%, 0.85, and 0.74 accuracy, dice score, and Jaccard index, respectively. The comparison results suggested that the use of LE methods as a preprocessing step in CT Lung images significantly improved the feature extraction and segmentation abilities of the U-Net model by a 0.21 dice score. The results might lead to implementing the LE method in segmenting varied medical images.

Ilhan Ahmet, Alpan Kezban, Sekeroglu Boran, Abiyev Rahib

2023

COVID-19, Enhancement, Localization, Lung CT, U-Net

General General

Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model.

In Procedia computer science

Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated Learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy.

Ambesange Sateesh, Annappa B, Koolagudi Shashidhar G

2023

Federated Learning, Federated Transfer Learning, Lung image segmentation, MRI image segmentation, Transfer Learning, U-net Architecture, X-ray Image segmentation, data privacy

General General

Impact on Air Quality Index of India Due to Lockdown.

In Procedia computer science

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40-45%), compared before the lockdown period, followed by NO2 (27-35%), Sulphur Dioxide (SO2) (2-10%), PM2.5 (35-40%), but the Ozone (O3) rises (12-25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality.

Dubey Aditya, Rasool Akhtar

2023

Deep Learning Models, Soft Computing-based Models, Stochastic Forecasting Models, Supervised ML Models

General General

DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites.

In Analytical biochemistry

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.

Jing Yuanyuan, Zhang Shengli, Wang Houqiang

2023-Feb-03

Bahdanau attention mechanism, Non-classical HLA binding sites, SENet attention mechanism, Word embedding

Public Health Public Health

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.

In Computers in biology and medicine

AIM : COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples.

METHODS : In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic explanations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19- associated biomarker candidate genes and improve the final model's interpretability.

RESULTS : For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class.

CONCLUSIONS : The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.

Yagin Fatma Hilal, Cicek İpek Balikci, Alkhateeb Abedalrhman, Yagin Burak, Colak Cemil, Azzeh Mohammad, Akbulut Sami

2023-Feb-01

COVID-19, Explainable artificial intelligence, LIME, SHAP, XGBoost

Radiology Radiology

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

In Computers in biology and medicine

BACKGROUND : The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial.

METHODS : A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP).

RESULTS : LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods.

CONCLUSIONS : The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

Wu Yanan, Qi Qianqian, Qi Shouliang, Yang Liming, Wang Hanlin, Yu Hui, Li Jianpeng, Wang Gang, Zhang Ping, Liang Zhenyu, Chen Rongchang

2023-Jan-23

COVID-19, Capsule network, Community-acquired pneumonia, Computed tomography, Maximum intensity projection

General General

[CRISPR-based molecular diagnostics: a review].

In Sheng wu gong cheng xue bao = Chinese journal of biotechnology

Rapid and accurate detection technologies are crucial for disease prevention and control. In particular, the COVID-19 pandemic has posed a great threat to our society, highlighting the importance of rapid and highly sensitive detection techniques. In recent years, CRISPR/Cas-based gene editing technique has brought revolutionary advances in biotechnology. Due to its fast, accurate, sensitive, and cost-effective characteristics, the CRISPR-based nucleic acid detection technology is revolutionizing molecular diagnosis. CRISPR-based diagnostics has been applied in many fields, such as detection of infectious diseases, genetic diseases, cancer mutation, and food safety. This review summarized the advances in CRISPR-based nucleic acid detection systems and its applications. Perspectives on intelligent diagnostics with CRISPR-based nucleic acid detection and artificial intelligence were also provided.

Sun Wenjun, Huang Xingxu, Wang Xinjie

2023-Jan-25

CRISPR, CRISPR-based detection, gene editing, molecular detection, nucleic acid detection, point-of-care testing (POCT)

General General

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model.

In Frontiers in medicine

INTRODUCTION : Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).

METHODS : In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.

RESULTS : Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.

CONCLUSION : Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Karampitsakos Theodoros, Sotiropoulou Vasilina, Katsaras Matthaios, Tsiri Panagiota, Georgakopoulou Vasiliki E, Papanikolaou Ilias C, Bibaki Eleni, Tomos Ioannis, Lambiri Irini, Papaioannou Ourania, Zarkadi Eirini, Antonakis Emmanouil, Pandi Aggeliki, Malakounidou Elli, Sampsonas Fotios, Makrodimitri Sotiria, Chrysikos Serafeim, Hillas Georgios, Dimakou Katerina, Tzanakis Nikolaos, Sipsas Nikolaos V, Antoniou Katerina, Tzouvelekis Argyris

2022

antifibrotics, interstitial lung disease, long COVID, machine learning, post-COVID-19

Ophthalmology Ophthalmology

Tele-Glaucoma Using a New Smartphone-based Tool for Visual Field Assessment.

In Journal of glaucoma

PRECIS : Covid-19 underlines the importance of telemedical diagnostics. The Sb-C is a newly developed digital application allowing visual field testing using a head-mounted device and a smartphone. It enables visual field screening remotely from a clinic.

BACKGROUND : Smartphone-based campimetry (Sb-C) is a newly developed tool for functional ophthalmic diagnosis. This study aimed to examine the comparability of the Sb-C and Octopus 900 to ensure ophthalmologic care in times of social distancing.

METHODS : 93 eyes were included in the study. After an ophthalmological examination, the visual field was tested by the Octopus program G1 and by the smartphone-based campimeter. The Sb-C was performed using VR-glasses and an iPhone 6. The software Sb-C was downloaded and installed as SmartCampiTracker app and is examining the 30° visual field with 59 test positions corresponding to the G pattern of Octopus G1. Sensitivities were recorded and saved on the app. Additionally, test-retest reliability was tested on 6 ophthalmologically healthy participants.

RESULTS : The group comprised 48 women and 45 men (mean age: 62.52±12.2 y) including 19 controls, 17 patients with ocular hypertension, 11 preperimetric glaucomas, and 46 perimetric glaucomas. The mean sensitivity (MS) of all points of G1 perimetry was 23.13 dB (95% CI: 22.08-24.18). The MS of the Sb-C was 21.23 dB (95% CI: 20.37-22.08). The correlation between the mean MS measured by G1 perimetry and the Sb-C was strong (r=0.815, P<0.05). The test-retest reliability showed a correlation of r=0.591 (P<0.05.

CONCLUSION : With some technical adjustments, the Sb-C shows promise for screening glaucoma and monitoring disease progression remotely from an ophthalmologic clinic.

Grau Elisabeth, Andrae Stefan, Horn Folkert, Hohberger Bettina, Ring Matthias, Michelson Georg

2022-Nov-29

General General

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.

In JMIR mental health

BACKGROUND : Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE : This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS : A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS : A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS : These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Tornero-Costa Roberto, Martinez-Millana Antonio, Azzopardi-Muscat Natasha, Lazeri Ledia, Traver Vicente, Novillo-Ortiz David

2023-Feb-02

artificial intelligence, health research, mental health, research methodology, research quality, review methodology, systematic review, trial methodology

Pathology Pathology

Host immunological responses facilitate development of SARS-CoV-2 mutations in patients receiving monoclonal antibody treatments.

In The Journal of clinical investigation ; h5-index 129.0

BACKGROUND : The role of host immunity in emergence of evasive SARS-CoV-2 Spike mutations under therapeutic monoclonal antibody (mAb) pressure remains to be explored.

METHODS : In a prospective, observational, monocentric ORCHESTRA cohort study, conducted between March 2021 and November 2022, mild-to-moderately ill COVID-19 patients (n=204) receiving bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, or sotrovimab were longitudinally studied over 28 days for viral loads, de novo Spike mutations, mAb kinetics, seroneutralization against infecting variants of concern, and T-cell immunity. Additionally, a machine learning-based circulating immune-related (CIB) biomarker profile predictive of evasive Spike mutations was constructed and confirmed in an independent dataset (n=19) that included patients receiving sotrovimab or tixagevimab/cilgavimab.

RESULTS : Patients treated with various mAbs developed evasive Spike mutations with remarkable speed and high specificity to the targeted mAb-binding sites. Immunocompromised patients receiving mAb therapy not only continued to display significantly higher viral loads, but also showed higher likelihood of developing de novo Spike mutations. Development of escape mutants also strongly correlated with neutralizing capacity of the therapeutic mAbs and T-cell immunity, suggesting immune pressure as an important driver of escape mutations. Lastly, we showed that an anti-inflammatory and healing-promoting host milieu facilitates Spike mutations, where 4 CIBs identified patients at high risk of developing escape mutations against therapeutic mAbs with high accuracy.

CONCLUSIONS : Our data demonstrate that host-driven immune and non-immune responses are essential for development of mutant SARS-CoV-2. These data also support point-of-care decision-making in reducing the risk of mAb treatment failure and improving mitigation strategies for possible dissemination of escape SARS-CoV-2 mutants.

Gupta Akshita, Konnova Angelina, Smet Mathias, Berkell Matilda, Savoldi Alessia, Morra Matteo, Van Averbeke Vincent, De Winter Fien Hr, Peserico Denise, Danese Elisa, Hotterbeekx An, Righi Elda, De Nardo Pasquale, Tacconelli Evelina, Malhotra-Kumar Surbhi, Kumar-Singh Samir

2023-Feb-09

COVID-19, Cellular immune response

General General

Improving effectiveness of online learning for higher education students during the COVID-19 pandemic.

In Frontiers in psychology ; h5-index 92.0

During the COVID-19 pandemic, online learning has become one of the important ways of higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has become the focus of education research, and the role of the "online learning community" cannot be ignored. In the context of the Internet of Things (IoT), we try to build up a new online learning community model: (1) First, we introduce the Kolb learning style theory to identify different online learning styles; (2) Second, we use a clustering algorithm to identify the nature of different learning style groups; and (3) Third, we introduce the group dynamics theory to design the dimensions of the questionnaire and combine the Analytic Hierarchy Process (AHP) method to identify the key influencing factors of the online learning community. We take business administration majors and students in universities as an example. The results show that (1) as a machine learning method, the clustering algorithm method is superior to the random construction method in identifying different learning style groups, and (2) our method can well judge the importance of each factor based on hierarchical analysis and clarify the different roles of factors in the process of knowledge transfer. This study can provide a useful reference for the sustainable development of online learning in higher education.

Li Xuelan, Pei Zhiqiang

2022

COVID-19, analytic hierarchy process, cluster analysis, group dynamics theory, online learning community

General General

Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : Within the technological development path, chatbots are considered an important tool for economic and social entities to become more efficient and to develop customer-centric experiences that mimic human behavior. Although artificial intelligence is increasingly used, there is a lack of empirical studies that aim to understand consumers' experience with chatbots. Moreover, in a context characterized by constant population aging and an increased life-expectancy, the way aging adults perceive technology becomes of great interest. However, based on the digital divide (unequal access to technology, knowledge, and resources), and since young adults (aged between 18 and 34 years old) are considered to have greater affinity for technology, most of the research is dedicated to their perception. The present paper investigates the way chatbots are perceived by middle-aged and aging adults in Romania.

METHODS : An online opinion survey has been conducted. The age-range of the subjects is 40-78 years old, a convenience sampling technique being used (N = 235). The timeframe of the study is May-June 2021. Thus, the COVID-19 pandemic is the core context of the research. A covariance-based structural equation modelling (CB-SEM) has been used to test the theoretical assumptions as it is a procedure used for complex conceptual models and theory testing.

RESULTS : The results show that while perceived ease of use is explained by the effort, the competence, and the perceive external control in interacting with chatbots, perceived usefulness is supported by the perceived ease of use and subjective norms. Furthermore, individuals are likely to further use chatbots (behavioral intention) if they consider this interaction useful and if the others' opinion is in favor of using it. Gender and age seem to have no effect on behavioral intention. As studies on chatbots and aging adults are few and are mainly investigating reactions in the healthcare domain, this research is one of the first attempts to better understand the way chatbots in a not domain-specific context are perceived later in life. Likewise, judging from a business perspective, the results can help economic and social organizations to improve and adapt AI-based interaction for the aging customers.

Iancu Ioana, Iancu Bogdan

2022

behavioral intention, chatbots, middle-aged and aging adults, perceived ease of use, perceived usefulness, technology acceptance model

Public Health Public Health

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

In Journal of exposure science & environmental epidemiology ; h5-index 34.0

BACKGROUND : Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales.

OBJECTIVE : To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches.

METHODS : We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches.

RESULTS : We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models.

SIGNIFICANCE : The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

Ren Xiang, Mi Zhongyuan, Georgopoulos Panos G

2023-Feb-01

Bayesian geospatial modeling, COVID-19, Explainable machine learning, Exposome and socioexposome, Social/environmental health disparities

General General

Future trajectory of respiratory infections following the COVID-19 pandemic in Hong Kong.

In Chaos (Woodbury, N.Y.)

The accumulation of susceptible populations for respiratory infectious diseases (RIDs) when COVID-19-targeted non-pharmaceutical interventions (NPIs) were in place might pose a greater risk of future RID outbreaks. We examined the timing and magnitude of RID resurgence after lifting COVID-19-targeted NPIs and assessed the burdens on the health system. We proposed the Threshold-based Control Method (TCM) to identify data-driven solutions to maintain the resilience of the health system by re-introducing NPIs when the number of severe infections reaches a threshold. There will be outbreaks of all RIDs with staggered peak times after lifting COVID-19-targeted NPIs. Such a large-scale resurgence of RID patients will impose a significant risk of overwhelming the health system. With a strict NPI strategy, a TCM-initiated threshold of 600 severe infections can ensure a sufficient supply of hospital beds for all hospitalized severely infected patients. The proposed TCM identifies effective dynamic NPIs, which facilitate future NPI relaxation policymaking.

Cheng Weibin, Zhou Hanchu, Ye Yang, Chen Yifan, Jing Fengshi, Cao Zhidong, Zeng Daniel Dajun, Zhang Qingpeng

2023-Jan

General General

Recent Issues in Medical Journal Publishing and Editing Policies: Adoption of Artificial Intelligence, Preprints, Open Peer Review, Model Text Recycling Policies, Best Practice in Scholarly Publishing 4th Version, and Country Names in Titles.

In Neurointervention

In Korea, many editors of medical journal are also publishers; therefore, they need to not only manage peer review, but also understand current trends and policies in journal publishing and editing. This article aims to highlight some of these policies with examples. First, the use of artificial intelligence tools in journal publishing has increased, including for manuscript editing and plagiarism detection. Second, preprint publications, which have not been peer-reviewed, are becoming more common. During the COVID-19 pandemic, medical journals have been more willing to accept preprints to adjust rapidly changing pandemic health issues, leading to a significant increase in their use. Third, open peer review with reviewer comments is becoming more widespread, including the mandatory publication of peer-reviewed manuscripts with comments. Fourth, model text recycling policies provide guidelines for researchers and editors on how to appropriately recycle text, for example, in the background section of the Introduction or the Methods section. Fifth, journals should take into account the recently updated 4th version of the Principles of Transparency and Best Practice in Scholarly Publishing, released in 2022. This version includes more detailed guidelines on journal websites, peer review processes, advisory boards, and author fees. Finally, it recommends that titles of human studies include country names to clarify the cultural context of the research. Each editor must decide whether to adopt these six policies for their journals. Editor-publishers of society journals are encouraged to familiarize themselves with these policies so that they can implement them in their journals as appropriate.

Huh Sun

2023-Feb-01

Artificial intelligence, Culture, Peer review, Policy, Scholarly communication

Public Health Public Health

Voice assistants' responses to questions about the COVID-19 vaccine: a national cross-sectional study.

In JMIR formative research

BACKGROUND : Artificial intelligence (AI)-powered voice assistants (VAs) - like Apple Siri, Google Assistant, and Amazon Alexa - interact with users in natural language and are capable of responding to simple commands, searching the internet, and answering questions. Despite being an increasingly popular way for the public to access health information, VAs could be a source of ambiguous or potentially biased information.

OBJECTIVE : In response to the ongoing prevalence of vaccine misinformation and disinformation, this study aims to evaluate how smartphone VAs respond to information- and recommendation-seeking inquiries regarding the COVID-19 vaccine.

METHODS : A national cross-sectional survey of English-speaking adults who owned a smartphone with a VA installed, conducted online from April 22-28, 2021. The primary outcomes were the VAs' responses to two questions: "Should I get the COVID vaccine?" and "Is the COVID vaccine safe?". Directed content analysis was used to assign a negative, neutral, or positive connotation to each response and website title provided by the VAs. Statistical significance was assessed using the t test (parametric) or Mann-Whitney U (nonparametric) test for continuous variables and the χ2 or Fisher exact test for categorical variables.

RESULTS : Of the 466 survey respondents included in the final analysis, 404 (86.7%) used Apple Siri, 53 (11.4%) used Google Assistant, and 9 (1.9%) used Amazon Alexa. In response to the question "Is the COVID vaccine safe?" 89.9% of users received a direct response, of which 97.3% had a positive connotation encouraging users to get vaccinated. Of the websites presented, only 5.3% had a positive connotation and 94.7% had a neutral connotation. In response to the question "Should I get the COVID vaccine?" 93.1% of users received a list of websites, of which 91.5% had a neutral connotation. For both COVID-19 vaccine-related questions, there was no association between the connotation of a response and the age, gender, zip code, race/ethnicity, or education level of the respondent.

CONCLUSIONS : Our study found that VAs were much more likely to respond directly with positive connotations to the question, "Is the COVID vaccine safe?" but not respond directly and provide a list of websites with neutral connotations to the question, "Should I get the COVID vaccine?" To our knowledge, this is the first study to evaluate how VAs respond to both information- and recommendation-seeking inquiries regarding the COVID-19 vaccine. These findings add to our growing understanding of both the opportunities and pitfalls of VAs in supporting public health information dissemination.

Sossenheimer Philip, Hong Grace, Devon-Sand Anna, Lin Steven

2023-Jan-29

General General

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.

In Medical & biological engineering & computing ; h5-index 32.0

A long-standing challenge in pneumonia diagnosis is recognizing the pathological lung texture, especially the ground-glass appearance pathological texture. One main difficulty lies in precisely extracting and recognizing the pathological features. The patients, especially those with mild symptoms, show very little difference in lung texture, neither conventional computer vision methods nor convolutional neural networks perform well on pneumonia diagnosis based on chest X-ray (CXR) images. In the meanwhile, the Coronavirus Disease 2019 (COVID-19) pandemic continues wreaking havoc around the world, where quick and accurate diagnosis backed by CXR images is in high demand. Rather than simply recognizing the patterns, extracting feature maps from the original CXR image is what we need in the classification process. Thus, we propose a Vision Transformer (VIT)-based model called PneuNet to make an accurate diagnosis backed by channel-based attention through X-ray images of the lung, where multi-head attention is applied on channel patches rather than feature patches. The techniques presented in this paper are oriented toward the medical application of deep neural networks and VIT. Extensive experiment results show that our method can reach 94.96% accuracy in the three-categories classification problem on the test set, which outperforms previous deep learning models.

Wang Tianmu, Nie Zhenguo, Wang Ruijing, Xu Qingfeng, Huang Hongshi, Xu Handing, Xie Fugui, Liu Xin-Jun

2023-Jan-31

COVID-19, Deep learning, Multi-head attention, Pneumonia diagnosis, Vision Transformer

General General

Data-driven analysis and predictive modeling on COVID-19.

In Concurrency and computation : practice & experience

The coronavirus (COVID-19) started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID-19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data-driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID-19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches.

Sharma Sonam, Alsmadi Izzat, Alkhawaldeh Rami S, Al-Ahmad Bilal

2022-Dec-25

COVID‐19, gender of patients, global growth rate, predictive modeling, social distancing

General General

A face mask detection system: An approach to fight with COVID-19 scenario.

In Concurrency and computation : practice & experience

A new coronavirus has caused a pandemic crisis around the globe. According to the WHO, this is an infectious illness that spreads from person to person. Therefore, the only way to avoid this infection is to take precautions. Wearing a mask is the most critical COVID-19 protection method because it prevents the virus from spreading from an infected person to a healthy one. This study reflects a deep learning method to create a system for detecting Face Masks. The paper proposes a unique FMDRT (Face Mask Dataset in Real-Time) dataset to determine whether a person is wearing a mask or not. The RFMD and Face Mask datasets are also taken from the internet to evaluate the performance of the proposed method. The CLAHE preprocessing method is employed to enhance the image quality, then resizing and Image augmentation techniques are used to convert it into a standard format and increase the size of the dataset, respectively. The pretrained Caffe face detector model is used to detect the faces, and then the lightweight transfer learning-based Xception model is applied for the feature extraction process. This paper recommended a novel model that is, CL-SSDXcept to distinguish the Face Mask or no mask images. However, accession with the MobileNetV2, VGG16, VGG19, and InceptionV3 models with different hyperparameter settings has been tested on the FMDRT dataset. We have also compared the results of the synthesized dataset FMDRT to the existing Face Mask datasets. The experimental results attained 98% test accuracy on the suggested dataset 'FMDRT' using the CL-SSDXcept method. The empirical findings have been reported at 50 iterations with tuned hyperparameter values with an average accuracy 98% and a loss of 0.05.

Jayaswal Ruchi, Dixit Manish

2022-Dec-25

3D‐face masks, CL‐SSDXcept, COVID‐19, DNN models, face mask detection, hyperparameters, optimizers

General General

Rationing scarce healthcare capacity: A study of the ventilator allocation guidelines during the COVID-19 pandemic.

In Production and operations management

In the United States, even though national guidelines for allocating scarce healthcare resources are lacking, 26 states have specific ventilator allocation guidelines to be invoked in case of a shortage. While several states developed their guidelines in response to the recent COVID-19 pandemic, New York State developed these guidelines in 2015 as "pandemic influenza is a foreseeable threat, one that we cannot ignore." The primary objective of this study is to assess the existing procedures and priority rules in place for allocating/rationing scarce ventilator capacity and propose alternative (and improved) priority schemes. We first build machine learning models using inpatient records of COVID-19 patients admitted to New York-Presbyterian/Columbia University Irving Medical Center and an affiliated community health center to predict survival probabilities as well as ventilator length-of-use. Then, we use the resulting point estimators and their uncertainties as inputs for a multiclass priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P (Sequential Organ Failure Assessment based prioritization), which most closely mimics the existing practice by prioritizing patients with sufficiently low SOFA scores; (ii) ISP (incremental survival probability), which assigns priority based on patient-level survival predictions; and (iii) ISP-LU (incremental survival probability per length-of-use), which takes into account survival predictions and resource use duration. Our findings highlight that our proposed priority scheme, ISP-LU, achieves a demonstrable improvement over the other two alternatives. Specifically, the expected number of survivals increases and death risk while waiting for ventilator use decreases. We also show that ISP-LU is a robust priority scheme whose implementation yields a Pareto-improvement over both SOFA-P and ISP in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.

Anderson David R, Aydinliyim Tolga, Bjarnadóttir Margrét V, Çil Eren B, Anderson Michaela R

2023-Jan-22

COVID‐19, fairness, machine learning, multiclass queueing with abandonments, priority scheduling, resource allocation, scarce ventilator capacity

General General

Federated learning based Covid-19 detection.

In Expert systems

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Chowdhury Deepraj, Banerjee Soham, Sannigrahi Madhushree, Chakraborty Arka, Das Anik, Dey Ajoy, Dwivedi Ashutosh Dhar

2022-Nov-02

COVID‐19, CXR images, Internet of Medical Things (IoMT), Xception, cybersecurity, federated learning, privacy, transfer learning

General General

The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic

ArXiv Preprint

Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world. To gain further insight into this matter, we introduce the Real World Worry Waves Dataset (RW3D). The dataset combines rich open-ended free-text responses with survey data on emotions, significant life events, and psychological stressors in a repeated-measures design in the UK over three years (2020: n=2441, 2021: n=1716 and 2022: n=1152). This paper provides background information on the data collection procedure, the recorded variables, participants' demographics, and higher-order psychological and text-based derived variables that emerged from the data. The RW3D is a unique primary data resource that could inspire new research questions on the psychological impact of the pandemic, especially those that connect modalities (here: text data, psychological survey variables and demographics) over time.

Isabelle van der Vegt, Bennett Kleinberg

2023-02-01

General General

Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.

In Scientific reports ; h5-index 158.0

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Talkar Tanya, Low Daniel M, Simpkin Andrew J, Ghosh Satrajit, O’Keeffe Derek T, Quatieri Thomas F

2023-Jan-28

Radiology Radiology

Longitudinal changes in global structural brain connectivity and cognitive performance in former hospitalized COVID-19 survivors: an exploratory study.

In Experimental brain research

BACKGROUND : Long-term sequelae of COVID-19 can result in reduced functionality of the central nervous system and substandard quality of life. Gaining insight into the recovery trajectory of admitted COVID-19 patients on their cognitive performance and global structural brain connectivity may allow a better understanding of the diseases' relevance.

OBJECTIVES : To assess whole-brain structural connectivity in former non-intensive-care unit (ICU)- and ICU-admitted COVID-19 survivors over 2 months following hospital discharge and correlate structural connectivity measures to cognitive performance.

METHODS : Participants underwent Magnetic Resonance Imaging brain scans and a cognitive test battery after hospital discharge to evaluate structural connectivity and cognitive performance. Multilevel models were constructed for each graph measure and cognitive test, assessing the groups' influence, time since discharge, and interactions. Linear regression models estimated whether the graph measurements affected cognitive measures and whether they differed between ICU and non-ICU patients.

RESULTS : Six former ICU and six non-ICU patients completed the study. Across the various graph measures, the characteristic path length decreased over time (β = 0.97, p = 0.006). We detected no group-level effects (β = 1.07, p = 0.442) nor interaction effects (β = 1.02, p = 0.220). Cognitive performance improved for both non-ICU and ICU COVID-19 survivors on four out of seven cognitive tests 2 months later (p < 0.05).

CONCLUSION : Adverse effects of COVID-19 on brain functioning and structure abate over time. These results should be supported by future research including larger sample sizes, matched control groups of healthy non-infected individuals, and more extended follow-up periods.

Tassignon B, Radwan A, Blommaert J, Stas L, Allard S D, De Ridder F, De Waele E, Bulnes L C, Hoornaert N, Lacor P, Lathouwers E, Mertens R, Naeyaert M, Raeymaekers H, Seyler L, Van Binst A M, Van Imschoot L, Van Liedekerke L, Van Schependom J, Van Schuerbeek P, Vandekerckhove M, Meeusen R, Sunaert S, Nagels G, De Mey J, De Pauw K

2023-Jan-28

Magnetic resonance imaging, Recovery, SARS-CoV-2

Surgery Surgery

Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface.

In Scientific reports ; h5-index 158.0

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

Pirolli Davide, Righino Benedetta, Camponeschi Chiara, Ria Francesco, Di Sante Gabriele, De Rosa Maria Cristina

2023-Jan-27

General General

A survey of machine learning-based methods for COVID-19 medical image analysis.

In Medical & biological engineering & computing ; h5-index 32.0

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.

Sailunaz Kashfia, Özyer Tansel, Rokne Jon, Alhajj Reda

2023-Jan-28

COVID-19, Computer tomography, Deep learning, Machine learning, Medical image analysis, Transfer learning

Public Health Public Health

Development of an Artificial Intelligence-Guided Citizen-Centric Predictive Model for the Uptake of Maternal Health Services Among Pregnant Women Living in Urban Slum Settings in India: Protocol for a Cross-sectional Study With a Mixed Methods Design.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Pregnant women are considered a "high-risk" group with limited access to health facilities in urban slums in India. Barriers to using health services appropriately may lead to maternal and child mortality, morbidity, low birth weight, and children with stunted growth. With the increase in the use of artificial intelligence (AI) and machine learning in the health sector, we plan to develop a predictive model that can enable substantial uptake of maternal health services and improvements in adverse pregnancy health care outcomes from early diagnostics to treatment in urban slum settings.

OBJECTIVE : The objective of our study is to develop and evaluate the AI-guided citizen-centric platform that will support the uptake of maternal health services among pregnant women seeking antenatal care living in urban slum settings.

METHODS : We will conduct a cross-sectional study using a mixed methods approach to enroll 225 pregnant women aged 18-44 years, living in the urban slums of Delhi for more than 6 months, seeking antenatal care, and who have smartphones. Quantitative and qualitative data will be collected using an Open Data Kit Android-based tool. Variables gathered will include sociodemographics, clinical history, pregnancy history, dietary history, COVID-19 history, health care facility data, socioeconomic status, and pregnancy outcomes. All data gathered will be aggregated into a common database. We will use AI to predict the early at-risk pregnancy outcomes (in terms of the type of delivery method, term, and related complications) depending on the needs of the beneficiaries translating into effective service-delivery improvements in enhancing the use of maternal health services among pregnant women seeking antenatal care. The proposed research will help policy makers to prioritize resource planning, resource allocation, and the development of programs and policies to enhance maternal health outcomes. The academic research study has received ethical approval from the University Research Ethics Committee of Dehradun Institute of Technology (DIT) University, Dehradun, India.

RESULTS : The study was approved by the University Research Ethics Committee of DIT University, Dehradun, on July 4, 2021. Enrollment of the eligible participants will begin by April 2022 followed by the development of the predictive model by October 2022 till January 2023. The proposed AI-guided citizen-centric tool will be designed, developed, implemented, and evaluated using principles of human-centered design that will help to predict early at-risk pregnancy outcomes.

CONCLUSIONS : The proposed internet-enabled AI-guided prediction model will help identify the potential risk associated with pregnancies and enhance the uptake of maternal health services among those seeking antenatal care for safer deliveries. We will explore the scalability of the proposed platform up to different geographic locations for adoption for similar and other health conditions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : PRR1-10.2196/35452.

Shrivastava Rahul, Singhal Manmohan, Gupta Mansi, Joshi Ashish

2023-Jan-27

artificial intelligence, citizen centric, development, evaluation, informatics, machine learning, maternal health, predictive model

Public Health Public Health

Application of artificial intelligence to the public health education.

In Frontiers in public health

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Wang Xueyan, He Xiujing, Wei Jiawei, Liu Jianping, Li Yuanxi, Liu Xiaowei

2022

algorithm, artificial intelligence, big data, curriculum, education, public health

Public Health Public Health

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.

OBJECTIVE : This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.

METHODS : The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.

RESULTS : The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.

CONCLUSIONS : A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.

Raza Shaina, Schwartz Brian

2023-Jan-26

Artificial intelligence, COVID-19, Data cohort, Named entity, Natural language processing, Relation extraction, Transfer learning

Public Health Public Health

Insights in paediatric virology during the COVID-19 era (Review).

In Medicine international

The present article provides an overview of the key messages of the topics discussed at the '7th Workshop on Paediatric Virology', which was organised virtually on December 20, 2021 by the Institute of Paediatric Virology, located on the Island of Euboea in Greece. The workshop's plenary lectures were on: i) viral pandemics and epidemics in the ancient Mediterranean; ii) the impact of obesity on the outcome of viral infections in children and adolescents; and iii) COVID-19 and artificial intelligence. Despite the scarcity of evidence from fossils and remnants, viruses have been recognised as significant causes of several epidemics in the ancient Mediterranean. Paediatric obesity, a modifiable critical health risk factor, has been shown to impact on the development, progression and severity of viral infections. Thus, the prevention of paediatric obesity should be included in formulating public health policies and decision-making strategies against emerging global viral threats. During the current COVID-19 pandemic, artificial intelligence has been used to facilitate the identification, monitoring and prevention of SARS-CoV-2. In the future, it will play a fundamental role in the surveillance of epidemic-prone infectious diseases, in the repurposing of older therapies and in the design of novel therapeutic agents against viral infections. The collaboration between different medical specialties and other diverse scientific fields, including archaeology, history, epidemiology, nutritional technologies, mathematics, computer technology, engineering, medical law and ethics is essential for the successful management of paediatric viral infections. The current COVID-19 pandemic has underscored this need, which should be further encouraged in modern medical education.

Mammas Ioannis N, Liston Maria, Koletsi Patra, Vitoratou Dimitra-Irinna, Koutsaftiki Chryssie, Papatheodoropoulou Alexia, Kornarou Helen, Theodoridou Maria, Kramvis Anna, Drysdale Simon B, Spandidos Demetrios A

2022

Institute of Paediatric Virology, ancient Mediterranean, artificial intelligence, coronavirus disease 2019, obesity, paediatric virology, severe acute respiratory syndrome coronavirus 2, viral infections, viral pandemics

General General

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

In Journal of translational medicine

BACKGROUND : Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS : We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS : To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS : All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Ren Zhong-Hao, You Zhu-Hong, Zou Quan, Yu Chang-Qing, Ma Yan-Fang, Guan Yong-Jian, You Hai-Ru, Wang Xin-Fei, Pan Jie

2023-Jan-25

Drug–protein interactions, Joint learning, Meta-path, Multi-modal, Natural language processing, Sequence analysis

General General

An open-source molecular builder and free energy preparation workflow.

In Communications chemistry

Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow , along with a tutorial.

Bieniek Mateusz K, Cree Ben, Pirie Rachael, Horton Joshua T, Tatum Natalie J, Cole Daniel J

2022-Oct-27

General General

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

In Briefings in bioinformatics

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

Zhou Haiwei, Tan Wenxi, Shi Shaoping

2023-Jan-24

Gaussian prior, gated attention mechanism, methylation, residual network, weighted joint loss function

General General

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19.

In Biology methods & protocols

Artificial intelligence (AI) as a suite of technologies can complement systematic review and meta-analysis studies and answer questions that cannot be typically answered using traditional review protocols and reporting methods. The purpose of this protocol is to introduce a new protocol to complete systematic review and meta-analysis studies. In this work, systematic review, meta-analysis, and meta-analysis network based on selected AI technique, and for P < 0.05 are followed, with a view to responding to questions and challenges that the global population is facing in light of the COVID-19 pandemic. Finally, it is expected that conducting reviews by following the proposed protocol can provide suitable answers to some of the research questions raised due to COVID-19.

Salari Nader, Shohaimi Shamarina, Kiaei Aliakbar, Hosseinian-Far Amin, Mansouri Kamran, Ahmadi Arash, Mohammadi Masoud

2023

COVID-19, artificial intelligence, meta-analysis, network meta-analysis, protocol, systematic review

Public Health Public Health

Conspiracy beliefs and COVID-19 guideline adherence in adolescent psychiatric outpatients: the predictive role of adverse childhood experiences.

In Child and adolescent psychiatry and mental health

BACKGROUND : Conspiracy beliefs have become widespread throughout the COVID-19 pandemic. Previous studies have shown that endorsing conspiracy beliefs leads to lower protective guideline adherence (i.e., wearing face masks), posing a threat to public health measures. The current study expands this research across the lifespan, i.e., in a sample of adolescents with mental health problems. Here, we investigated the association between conspiracy beliefs and guideline adherence while also exploring the predictors of conspiracy beliefs.

METHODS : N = 93 adolescent psychiatric outpatients (57% female, mean age: 15.8) were assessed using anonymous paper-pencil questionnaires. Endorsement of generic and COVID-19 conspiracy beliefs was assessed, in addition to items measuring adherence to protective guidelines and mental health (stress, depressive symptoms, emotional/behavioral problems, and adverse childhood experiences). Multiple regressions and supervised machine learning (conditional random forests) were used for analyses.

RESULTS : Fourteen percent of our sample fully endorsed at least one COVID-19 conspiracy theory, while protective guidelines adherence was relatively high (M = 4.92, on a scale from 1 to 7). The endorsement of COVID-19 conspiracy beliefs-but not of generic conspiracy beliefs-was associated with lower guideline adherence (β = - 0.32, 95% CI - 0.53 to - 0.11, p < .001). Conditional random forests suggested that adverse childhood experiences and peer and conduct problems were relevant predictors of both conspiracy belief categories.

CONCLUSION : While a significant proportion of our sample of adolescents in psychiatric treatment endorsed conspiracy beliefs, the majority did not. Furthermore, and to some degree, contrary to public perception, we found that adolescents show relatively good adherence to public health measures-even while experiencing a high degree of mental distress. The predictive value of adverse childhood experiences and peer/conduct problems for conspiracy beliefs might be explained by compensatory mechanisms to ensure the safety, structure, and inclusion that conspiracies provide.

Goreis Andreas, Pfeffer Bettina, Zesch Heidi Elisabeth, Klinger Diana, Reiner Tamara, Bock Mercedes M, Ohmann Susanne, Sackl-Pammer Petra, Werneck-Rohrer Sonja, Eder Harald, Skala Katrin, Czernin Klara, Mairhofer Dunja, Rohringer Bernhard, Bedus Carolin, Lipp Ronja, Vesely Christine, Plener Paul L, Kothgassner Oswald D

2023-Jan-24

Adolescents, Adverse childhood experiences, COVID-19, Childhood Trauma, Conspiracy beliefs, Guideline adherence, Mental health

Public Health Public Health

Generating simple classification rules to predict local surges in COVID-19 hospitalizations.

In Health care management science

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Yaesoubi Reza, You Shiying, Xi Qin, Menzies Nicolas A, Tuite Ashleigh, Grad Yonatan H, Salomon Joshua A

2023-Jan-24

COVID-19, Decision tree, Machine learning, Prediction, Simulation, Surveillance

General General

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.

In Genetic epidemiology

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Liu Zihuan, Dai Wei, Wang Shiying, Yao Yisha, Zhang Heping

2023-Jan-24

COVID-19, SARS-CoV-2, TAS2R1, UK Biobank, deep learning

General General

Deep learning approach to security enforcement in cloud workflow orchestration.

In Journal of cloud computing (Heidelberg, Germany)

Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients' data managed by a workflow deployed on the cloud need to be protected, and communication of such data across multiple stakeholders should also be secured. In general, security threats in cloud environments have been studied extensively. Such threats include data breaches, data loss, denial of service, service rejection, and malicious insiders generated from issues such as multi-tenancy, loss of control over data and trust. Supporting the security of a cloud workflow deployed and executed over a dynamic environment, across different platforms, involving different stakeholders, and dynamic data is a difficult task and is the sole responsibility of cloud providers. Therefore, in this paper, we propose an architecture and a formal model for security enforcement in cloud workflow orchestration. The proposed architecture emphasizes monitoring cloud resources, workflow tasks, and the data to detect and predict anomalies in cloud workflow orchestration using a multi-modal approach that combines deep learning, one class classification, and clustering. It also features an adaptation scheme to cope with anomalies and mitigate their effect on the workflow cloud performance. Our prediction model captures unsupervised static and dynamic features as well as reduces the data dimensionality, which leads to better characterization of various cloud workflow tasks, and thus provides better prediction of potential attacks. We conduct a set of experiments to evaluate the proposed anomaly detection, prediction, and adaptation schemes using a real COVID-19 dataset of patient health records. The results of the training and prediction experiments show high anomaly prediction accuracy in terms of precision, recall, and F1 scores. Other experimental results maintained a high execution performance of the cloud workflow after applying adaptation strategy to respond to some detected anomalies. The experiments demonstrate how the proposed architecture prevents unnecessary wastage of resources due to anomaly detection and prediction.

El-Kassabi Hadeel T, Serhani Mohamed Adel, Masud Mohammad M, Shuaib Khaled, Khalil Khaled

2023

Anomaly detection, Cloud, Cloud workflow, Covid-19, Deep learning, Prediction, Security enforcement

General General

Protein-ligand binding affinity prediction with edge awareness and supervised attention.

In iScience

Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization.

Gu Yuliang, Zhang Xiangzhou, Xu Anqi, Chen Weiqi, Liu Kang, Wu Lijuan, Mo Shenglong, Hu Yong, Liu Mei, Luo Qichao

2023-Jan-20

Biocomputational method, Classification of proteins, Molecular interaction

General General

Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods.

In Heliyon

The article focuses on analyzing the robustness of Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in unemployment rate estimation. In this context, a stochastic trend in the unemployment rate was determined by using monthly data in Turkey. The oil price, real exchange rate, interest rate and unemployment rate variables are imported into the ARIMA and ANN models with 176 data samples for the period of 01.01.2008-31.08.2022. The results of the conventional linear ARIMA and nonlinear ANN regressor models are compared. The comparison results show that the ARMA (2,1) model is the most suitable model for the unemployment rate estimation. This conclusion was reached based on ARMA (2,1) and ANN's RMSE, MAE, MAPE and R2 parameters. From the results of the specified criteria, it was found that both models gave results close to the actual unemployment rate however ARMA (2,1) was the more appropriate model for the current data set. The actual unemployment data and the estimated values are also given verifying the better modeling of the developed ARMA (2,1) model. In addition, there are meaningful relationships between month variables and the employment rate. This result supports that the unemployment possesses chronic reasons in Turkey. On the other side, the unemployment rate forecasting error of the ARMA (2,1) is higher than the ANN model for the 2020-2021 period during the intense pandemic. This result is important because it shows that during the times of the economic uncertainty caused by the Covid-19 pandemic, forecasts employing the neural network model is observed to have lower errors than the results of autoregressive moving average model. Therefore, under an economic uncertainty, it is shown that modeling the unemployment rate using artificial neural network provides novel insights for economic forecasting.

Yamacli Dilek Surekci, Yamacli Serhan

2023-Jan

ANN, ARIMA, Estimation, Unemployment rate

Internal Medicine Internal Medicine

Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort.

In Open forum infectious diseases

BACKGROUND : The mortality rates of coronavirus disease 2019 (COVID-19) have been changed across the epidemiological waves. The aim was to investigate the differences in mortality rates of COVID-19 patients in Japan across the 6 epidemiological waves stratified by age group and Coronavirus Clinical Characterisation Consortium (4C) mortality score risk group.

METHODS : A total of 56 986 COVID-19 patients in the COVID-19 Registry Japan from 2 March 2020 to 1 February 2022 were enrolled. These patients were categorized into 4 risk groups based on their 4C mortality score. Mortality rates of each risk group were calculated separately for different age groups: 18-64, 65-74, 75-89, and ≥90 years. In addition, mortality rates across the wave periods were calculated separately in 2 age groups: <75 and ≥75 years. All calculated mortality rates were compared with reported data from the United Kingdom (UK) during the early epidemic.

RESULTS : The mortality rates of patients in Japan were significantly lower than in the UK across the board, with the exception of patients aged ≥90 years at very high risk. The mortality rates of patients aged ≥75 years at very high risk in the fourth and fifth wave periods showed no significant differences from those in the UK, whereas those in the sixth wave period were significantly lower in all age groups and in all risk groups.

CONCLUSIONS : The present analysis showed that COVID-19 patients had a lower mortality rate in the most recent sixth wave period, even among patients ≥75 years old at very high risk.

Baba Hiroaki, Ikumi Saori, Aoyama Shotaro, Ishikawa Tetsuo, Asai Yusuke, Matsunaga Nobuaki, Ohmagari Norio, Kanamori Hajime, Tokuda Koichi, Ueda Takuya, Kawakami Eiryo

2023-Jan

4C mortality score, COVID-19, Japan, elderly, epidemic wave

General General

CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19.

In Informatics in medicine unlocked

Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensive hyperparameter adjustment and are unsuitable for small datasets. By leveraging the data in a comparatively small dataset, few-shot learning algorithms aim to reduce the requirement of large datasets. This inspired us to develop a few-shot learning model for early detection of COVID-19 to reduce the post-effect of this dangerous disease. The proposed architecture combines few-shot learning with an ensemble of pre-trained convolutional neural networks to extract feature vectors from CT scan images for similarity learning. The proposed Triplet Siamese Network as the few-shot learning model classified CT scan images into Normal, COVID-19, and Community-Acquired Pneumonia. The suggested model achieved an overall accuracy of 98.719%, a specificity of 99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT scans per category for training data.

Ornob Tareque Rahman, Roy Gourab, Hassan Enamul

2023

COVID-19 diagnosis, CT scan images, Ensemble CNN, Few-shot learning, Triplet siamese network

General General

ELUCNN for explainable COVID-19 diagnosis.

In Soft computing

COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client-server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of 94.41 ± 0.98 , a specificity of 94.84 ± 1.21 , an accuracy of 94.62 ± 0.96 , and an F1 score of 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

Wang Shui-Hua, Satapathy Suresh Chandra, Xie Man-Xia, Zhang Yu-Dong

2023-Jan-13

COVID-19, Cloud computing, Convolutional neural network, Cross validation, Deep learning, Exponential linear unit, Mobile app, Multiple-way data augmentation, SARS-CoV-2

General General

Assessment of the digital competencies of university instructors through use of the machine learning method.

In SN social sciences

The explosion of COVID-19 has brought new challenges to the education industry, especially higher education. Digital competency is becoming an essential competency for higher education instructors, and how to assess instructors' digital competency is attracting increasing attention in higher education. However, most studies have used self-report questionnaires or manual reviews to assess digital competencies, which are time-consuming and potentially biased, and there is a current need for valid and effective assessment methods. To address this issue, this study uses machine learning to analyze syllabi to assess the extent to which university instructors have incorporated digital competency into their courses. The results show that not only is the proposed method feasible, but the results of the assessment using machine learning are highly consistent with those of the human assessment. This approach contributes to the assessment of digital competency in higher education institutions and provides evidence that can be used as a reference for future research on the development of digital competency in higher education institutions.

Yang Tzu-Chi

2023

Digital competence, Higher education, Machine learning, Syllabus analysis

General General

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.

In Archives of computational methods in engineering : state of the art reviews

The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.

Vinod Dasari Naga, Prabaharan S R S

2023-Jan-17

General General

Development of a Fast Fourier Transform-based Analytical Method for COVID-19 Diagnosis from Chest X-Ray Images Using GNU Octave.

In Journal of medical physics

PURPOSE : Many artificial intelligence-based computational procedures are developed to diagnose COVID-19 infection from chest X-ray (CXR) images, as diagnosis by CXR imaging is less time consuming and economically cheap compared to other detection procedures. Due to unavailability of skilled computer professionals and high computer architectural resource, majority of the employed methods are difficult to implement in rural and poor economic settings. Majority of such reports are devoid of codes and ignores related diseases (pneumonia). The absence of codes makes limitation in applying them widely. Hence, validation testing followed by evidence-based medical practice is difficult. The present work was aimed to develop a simple method that requires a less computational expertise and minimal level of computer resource, but with statistical inference.

MATERIALS AND METHODS : A Fast Fourier Transform-based (FFT) method was developed with GNU Octave, a free and open-source platform. This was employed to the images of CXR for further analysis. For statistical inference, two variables, i.e., the highest peak and number of peaks in the FFT distribution plot were considered.

RESULTS : The comparison of mean values among different groups (normal, COVID-19, viral, and bacterial pneumonia [BP]) showed statistical significance, especially when compared to normal, except between viral and BP groups.

CONCLUSION : Parametric statistical inference from our result showed high level of significance (P < 0.001). This is comparable to the available artificial intelligence-based methods (where accuracy is about 94%). Developed method is easy, availability with codes, and requires a minimal level of computer resource and can be tested with a small sample size in different demography, and hence, be implemented in a poor socioeconomic setting.

Majumder Durjoy

2022

COVID-19, Chest X-ray image, Fourier analysis, image analysis, pneumonia

Surgery Surgery

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients.

In Frontiers in cardiovascular medicine

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Li Hongyu, Sun Xinti, Li Zesheng, Zhao Ruiping, Li Meng, Hu Taohong

2022

acute myocardial infarction, bioinformatics, immune infiltration, machine learning, prognosis

General General

Analysis of individual characteristics influencing user polarization in COVID-19 vaccine hesitancy.

In Computers in human behavior ; h5-index 125.0

During the COVID-19 pandemic, vaccine hesitancy proved to be a major obstacle in efforts to control and mitigate the negative consequences of COVID-19. This study centered on the degree of polarization on social media about vaccine use and contributing factors to vaccine hesitancy among social media users. Examining the discussion about COVID-19 vaccine on the Weibo platform, a relatively comprehensive system of user features was constructed based on psychological theories and models such as the curiosity-drive theory and the big five model of personality. Then machine learning methods were used to explore the paramount impacting factors that led users into polarization. Findings revealed that factors reflecting the activity and effectiveness of social media use promoted user polarization. In contrast, features reflecting users' information processing ability and personal qualities had a negative impact on polarization. This study hopes to help healthcare organizations and governments understand and curb social media polarization around vaccine development in the face of future surges of pandemics.

Xie Lei, Wang Dandan, Ma Feicheng

2023-Jan-17

Big five model of personality, COVID-19 pandemic, Curiosity-drive theory, User polarization, Vaccine hesitancy

General General

Safety, Tolerability and Pharmacokinetics of Half-Life Extended SARS-CoV-2 Neutralizing Monoclonal Antibodies AZD7442 (Tixagevimab/Cilgavimab) in Healthy Adults.

In The Journal of infectious diseases ; h5-index 82.0

BACKGROUND : AZD7442 is a combination of extended half-life, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific neutralizing monoclonal antibodies (tixagevimab/cilgavimab).

METHODS : This phase 1, first-in-human, randomized, double-blind, placebo-controlled, dose-escalation study evaluated AZD7442 administered intramuscularly (300 mg) or intravenously (300, 1000, 3000 mg) in healthy adults (aged 18-55 years). The primary endpoint was safety and tolerability. Secondary endpoints included pharmacokinetics and anti-drug antibodies.

RESULTS : Between August 18-October 16, 2020, 60 participants enrolled; 50 received AZD7442 and 10 received placebo. Adverse events (all of mild/moderate intensity) occurred in 26 (52.0%) and 8 (80.0%) participants (AZD7442 and placebo groups, respectively). No infusion- or injection-site, or hypersensitivity reactions occurred. Tixagevimab and cilgavimab had mean half-lives of approximately 90 days (range: 87.0-95.3 [tixagevimab], 79.8--91.1 [cilgavimab]) and similar pharmacokinetic profiles over the 361-day study period. SARS-CoV-2-specific neutralizing antibody titers provided by AZD7442 were maintained above those in plasma from convalescent coronavirus disease-19 (COVID-19) patients.

CONCLUSIONS : AZD7442 was well tolerated in healthy adults, showing a favorable safety profile across all doses. Depending on the SARS-CoV-2 variant, pharmacokinetic analyses suggest AZD7442 could offer protection for at least 6 months against symptomatic COVID-19 following a single 300 mg intramuscular administration.

CLINICAL TRIALS REGISTRATION : NCT04507256 (https://clinicaltrials.gov/ct2/show/NCT04507256).

Forte-Soto Pablo, Albayaty Muna, Brooks Dennis, Arends Rosalinda H, Tillinghast John, Aksyuk Anastasia A, Bouquet Jerome, Chen Cecil, Gebre Asfiha, Kubiak Robert J, Pilla Reddy Venkatesh, Seegobin Seth, Streicher Katie, Templeton Alison, Esser Mark T

2023-Jan-23

COVID-19, SARS-CoV-2, monoclonal antibody, pharmacokinetics, phase 1, safety, tolerability

Surgery Surgery

Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations.

In Translational behavioral medicine

Smartphone weight loss apps are constantly being developed but the essential elements needed by a multi-ethnic population with overweight and obesity remains unclear. Purpose: To explore the perceptions of an Asian multi-ethnic population with overweight and obesity on the essential elements of weight loss apps. Twenty two participants were purposively sampled from a specialist weight management clinic in Singapore from 13 April to 30 April 2021. Recorded interviews were conducted using face-to-face and videoconferencing modalities. Data saturation was reached at the 18th participant. Data analysis was performed using inductive content analysis with constant comparison between and within transcripts. Findings: Three themes and eight subthemes on the essential app components emerged-(a) comprehensive and flexible calorie counters; (b) holistic, gradual and individualized behavior change recommendations tailored for people with overweight and obesity, and (c) just-in-time reminders of future consequences. There was a need to incorporate flexible options for food logging; break down general recommendations into small steps towards sustainable changes; tailor app contents for people with overweight and obesity; and evoke one's considerations of future consequences. Future weight loss apps should be designed to meet the needs of those with overweight and obesity, the very population that needs assistance with weight loss. Future apps could consider leveraging the capacity of artificial intelligence to provide personalized weight management in terms of sustaining self-regulation behaviors, optimizing goal-setting and providing personalized and timely recommendations for weight loss.

Chew Han Shi Jocelyn, Lim Su Lin, Kim Guowei, Kayambu Geetha, So Bok Yan Jimmy, Shabbir Asim, Gao Yujia

2023-Jan-23

App, BMI, Behavior, Perceptions, Weight management, mHealth

Dermatology Dermatology

The role of mobile teledermoscopy in skin cancer triage and management during the COVID-19 pandemic.

In Indian journal of dermatology, venereology and leprology

The unprecedented onset of the COVID-19 crisis poses a significant challenge to all fields of medicine, including dermatology. Since the start of the coronavirus outbreak, a stark decline in new skin cancer diagnoses has been reported by countries worldwide. One of the greatest challenges during the pandemic has been the reduced access to face-to-face dermatologic evaluation and non-urgent procedures, such as biopsies or surgical excisions. Teledermatology is a well-integrated alternative when face-to-face dermatological assistance is not available. Teledermoscopy, an extension of teledermatology, comprises consulting dermoscopic images to improve the remote assessment of pigmented and non-pigmented lesions when direct visualisation of lesions is difficult. One of teledermoscopy's greatest strengths may be its utility as a triage and monitoring tool, which is critical in the early detection of skin cancer, as it can reduce the number of unnecessary referrals, wait times, and the cost of providing and receiving dermatological care. Mobile teledermoscopy may act as a communication tool between medical practitioners and patients. By using their smartphone (mobile phone) patients can monitor a suspicious skin lesion identified by their medical practitioner, or alternatively self-detect concerning lesions and forward valuable dermoscopic images for remote medical evaluation. Several mobile applications that allow users to photograph suspicious lesions with their smartphones and have them evaluated using artificial intelligence technology have recently emerged. With the growing popularity of mobile apps and consumer-involved healthcare, this will likely be a key component of skin cancer screening in the years to come. However, most of these applications apply artificial intelligence technology to assess clinical images rather than dermoscopic images, which may lead to lower diagnostic accuracy. Incorporating the direct-to-consumer mobile dermoscopy model in combination with mole-scanning artificial intelligence as a mobile app may be the future of skin cancer detection.

Lee Claudia, Witkowski Alexander, Żychowska Magdalena, Ludzik Joanna

2022-Dec-08

COVID-19, Dermoscopy, melanoma, skin cancer, smartphone, teledermoscopy

General General

A framework for designing AI systems that support community wellbeing.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : Designing artificial intelligence (AI) to support health and wellbeing is an important and broad challenge for technologists, designers, and policymakers. Drawing upon theories of AI and cybernetics, this article offers a design framework for designing intelligent systems to optimize human wellbeing. We focus on the production of wellbeing information feedback loops in complex community settings, and discuss the case study of My Wellness Check, an intelligent system designed to support the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic.

METHODS : The basis for our discussion is the community-led design of My Wellness Check, an intelligent system that supported the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic. Our system was designed to create an intelligent feedback loop to assess community wellbeing needs and to inform community action. This article provides an overview of our longitudinal assessment of students and staff wellbeing (n = 20,311) across two years of the COVID-19 pandemic.

RESULTS : We further share the results of a controlled experiment (n = 1,719) demonstrating the enhanced sensitivity and user experience of our context-sensitive wellbeing assessment.

DISCUSSION : Our approach to designing "AI for community wellbeing," may generalize to the systematic improvement of human wellbeing in other human-computer systems for large-scale governance (e.g., schools, businesses, NGOs, platforms). The two main contributions are: 1) showcasing a simple way to draw from AI theory to produce more intelligent human systems, and 2) introducing a human-centered, community-led approach that may be beneficial to the field of AI.

van der Maden Willem, Lomas Derek, Hekkert Paul

2022

artificial intelligence, community wellbeing, cybernetics, feedback loop, human values, human-centered design, wellbeing economy

General General

An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.

In Applied soft computing

Coronavirus Disease-2019 (COVID-19) causes Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and has opened several challenges for research concerning diagnosis and treatment. Chest X-rays and computed tomography (CT) scans are effective and fast alternatives to detect and assess the damage that COVID causes to the lungs at different stages of the disease. Although the CT scan is an accurate exam, the chest X-ray is still helpful due to the cheaper, faster, lower radiation exposure, and is available in low-incoming countries. Computer-aided diagnostic systems based on Artificial Intelligence (AI) and computer vision are an alternative to extract features from X-ray images, providing an accurate COVID-19 diagnosis. However, specialized and expensive computational resources come across as challenging. Also, it needs to be better understood how low-cost devices and smartphones can hold AI models to predict diseases timely. Even using deep learning to support image-based medical diagnosis, challenges still need to be addressed once the known techniques use centralized intelligence on high-performance servers, making it difficult to embed these models in low-cost devices. This paper sheds light on these questions by proposing the Artificial Intelligence as a Service Architecture (AIaaS), a hybrid AI support operation, both centralized and distributed, with the purpose of enabling the embedding of already-trained models on low-cost devices or smartphones. We demonstrated the suitability of our architecture through a case study of COVID-19 diagnosis using a low-cost device. Among the main findings of this paper, we point out the performance evaluation of low-cost devices to handle COVID-19 predicting tasks timely and accurately and the quantitative performance evaluation of CNN models embodiment on low-cost devices.

Rodrigues Moreira Larissa Ferreira, Moreira Rodrigo, Travençolo Bruno Augusto Nassif, Backes André Ricardo

2023-Feb

Artificial intelligence, COVID-19, Convolutional neural network, Embedded, Low-cost device

General General

Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus.

In Biochip journal

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.

Lee Young Suh, Choi Ji Wook, Kang Taewook, Chung Bong Geun

2023-Jan-17

Deep learning, GMM clustering, Image processing, Mask R-CNN, ddPCR

General General

Machine learning sentiment analysis, COVID-19 news and stock market reactions.

In Research in international business and finance

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.

Costola Michele, Hinz Oliver, Nofer Michael, Pelizzon Loriana

2023-Jan

COVID-19 news, Sentiment analysis, Stock markets

General General

A review of advanced technologies available to improve the healthcare performance during COVID-19 pandemic.

In Procedia computer science

Information technology (IT) has enabled the initiation of an innovative healthcare system. An innovative healthcare system integrates new technologies such as cloud computing, the internet of things, and artificial intelligence (AI), to transform the healthcare to be more efficient, more convenient and more personalized. This review aims to identify the key technologies that will help to support an innovative healthcare system. A case study approach was used in this research analysis to enable a researcher to closely analyze the data in a particular context. It presents a case study of the coronavirus (COVID-19) as a means of exploring the use of advanced technologies in an innovative healthcare system to help address a worldwide health crisis. An innovative healthcare system can help to promote better patient self-management, reduce costs, relieve staff pressures, help with resource and knowledge management, and improve the patient experience. An innovative healthcare system can reduce the expense and time for research, and increase the overall efficacy of the research. Overall, this research identifies how innovative technologies can improve the performance of the healthcare system. Advanced technologies can assist with pandemic control and can help in the recognition of the virus, clinical treatment, medical protection, intelligent diagnosis, and outbreak analysis. The review provides an analysis of the future prospects of an innovative healthcare system.

Ali Omar, AlAhmad Ahmad, Kahtan Hasan

2023

Artificial Intelligence, COVID-19, Cloud Computing, Healthcare, Informatization, Internet of Things

General General

Towards precision medicine: Omics approach for COVID-19.

In Biosafety and health

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Cen Xiaoping, Wang Fengao, Huang Xinhe, Jovic Dragomirka, Dubee Fred, Yang Huanming, Li Yixue

2023-Jan-18

COVID-19, artificial intelligence, multi-omics, precision medicine

General General

Speech phoneme and spectral smearing based non-invasive COVID-19 detection.

In Frontiers in artificial intelligence

COVID-19 is a deadly viral infection that mainly affects the nasopharyngeal and oropharyngeal cavities before the lung in the human body. Early detection followed by immediate treatment can potentially reduce lung invasion and decrease fatality. Recently, several COVID-19 detections methods have been proposed using cough and breath sounds. However, very little study has been done on the use of phoneme analysis and the smearing of the audio signal in COVID-19 detection. In this paper, this problem has been addressed and the classification of speech samples has been carried out in COVID-19-positive and healthy audio samples. Additionally, the grouping of the phonemes based on reference classification accuracies have been proposed for effectiveness and faster detection of the disease at a primary stage. The Mel and Gammatone Cepstral coefficients and their derivatives are used as the features for five standard machine learning-based classifiers. It is observed that the generalized additive model provides the highest accuracy of 97.22% for the phoneme grouping "/t//r//n//g//l/." This smearing-based phoneme classification technique can also be used in the future to classify other speech-related disease detections.

Mishra Soumya, Dash Tusar Kanti, Panda Ganapati

2022

COVID-19, COVID-19 detection, machine learning, phoneme analysis, spectral smearing

General General

Curious thing, an artificial intelligence (AI)-based conversational agent for COVID-19 patient management.

In Australian journal of primary health ; h5-index 18.0

There are no clear guidelines or validated models for artificial intelligence (AI)-based approaches in the monitoring of coronavirus disease 2019 (COVID-19) patients who were isolated in the community, in order to identify early deterioration of their health symptoms. Developed in partnership with Curious Thing (CT), a Sydney-based AI conversational technology, a new care robot technology was introduced in South Western Sydney (SWS) in September 2021 to manage the large numbers of low-to-medium risk patients with a COVID-19 diagnosis and who were isolating at home. The CT interface made contact with patients via their mobile phone, following a locally produced script to obtain information recording physical condition, wellness and support. The care robot has engaged over 6323 patients between 2 September to 14 December 2021. The AI-assisted phone calls effectively identified the patients requiring further support, saved clinician time by monitoring less ailing patients remotely, and enabled them to spend more time on critically ill patients, thus ensuring that service and supply resources could be directed to those at greatest need. Engagement strategies had ensured stakeholders support of this technology to meet clinical and welfare needs of the identified patient group. Feedback from both the patients and healthcare staff was positive and had informed the ongoing formulation of a more patient-centred model of virtual care.

Chow Josephine Sau Fan, Blight Victoria, Brown Marian, Glynn Vanessa, Lane Brian, Larkin Amanda, Marshall Sonia, Matthews Prue, Rowles Mick, Warner Bradley

2023-Jan-23

Cardiology Cardiology

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients.

In Clinics and research in hepatology and gastroenterology

INTRODUCTION : Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses.

METHODS : We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset.

RESULTS : 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified.

CONCLUSIONS : We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.

Ho Kai Man Alexander, Rosenfeld Avi, Hogan Áine, McBain Hazel, Duku Margaret, Wolfson Paul Bd, Wilson Ashley, Cheung Sharon My, Hennelly Laura, Macabodbod Lester, Graham David G, Sehgal Vinay, Banerjee Amitava, Lovat Laurence B

2023-Jan-17

General General

Mosaic RBD nanoparticles induce intergenus cross-reactive antibodies and protect against SARS-CoV-2 challenge.

In Proceedings of the National Academy of Sciences of the United States of America

Recurrent spillovers of α- and β-coronaviruses (CoV) such as severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome-CoV, SARS-CoV-2, and possibly human CoV have caused serious morbidity and mortality worldwide. In this study, six receptor-binding domains (RBDs) derived from α- and β-CoV that are considered to have originated from animals and cross-infected humans were linked to a heterotrimeric scaffold, proliferating cell nuclear antigen (PCNA) subunits, PCNA1, PCNA2, and PCNA3. They assemble to create a stable mosaic multivalent nanoparticle, 6RBD-np, displaying a ring-shaped disk with six protruding antigens, like jewels in a crown. Prime-boost immunizations with 6RBD-np in mice induced significantly high Ab titers against RBD antigens derived from α- and β-CoV and increased interferon (IFN-γ) production, with full protection against the SARS-CoV-2 wild type and Delta challenges. The mosaic 6RBD-np has the potential to induce intergenus cross-reactivity and to be developed as a pan-CoV vaccine against future CoV spillovers.

Lee Dan Bi, Kim Hyojin, Jeong Ju Hwan, Jang Ui Soon, Jang Yuyeon, Roh Seokbeom, Jeon Hyunbum, Kim Eun Jeong, Han Su Yeon, Maeng Jin Young, Magez Stefan, Radwanska Magdalena, Mun Ji Young, Jun Hyun Sik, Lee Gyudo, Song Min-Suk, Lee Hye-Ra, Chung Mi Sook, Baek Yun Hee, Kim Kyung Hyun

2023-Jan-24

SARS-CoV-2, immune response, mosaic multivalent antigens, receptor-binding domain, spike

General General

Multidimensional machine learning on 2173 COVID-19 patients in Vietnam: Retro-prospective Validation Study.

In JMIR formative research

BACKGROUND : Machine learning (ML) is a part of the Artificial Intelligence strategy. Its algorithms are imputed on Big Data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address the problem. New diseases like Sars-Cov2 are important data stores for machine learning. Therefore, all relevant parameters should be explicitly quantified and modeled.

OBJECTIVE : The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients.

METHODS : The machine learning study population consisted of 2173 patients (1587 mild and non-symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. Two correlation test, relative risk and risk ration were used to eliminate the unbalance parameter and select also the most remarkable ones. HCA, K-means are two independent methods to classify the parameters following their R scores. Finally, Network analysis step give the view in three dimension, more complete of the results above.

RESULTS : The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their significant risk ratio (P<.00001) from the meta-analysis, respectively, are: 4.19 [3.58-4.95], 3.29 [2.76-3.92,] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (P<.00001), 0.34 [0.2743;0.4210]. The significant inversion of correlation between the group of severity shows the important remark. ALT - Leucocytes show the strong negative link (R=-1, P<.00001) in the mild group to the significant positive correlation in the moderate group (R=1, P<.00001). Transferrin-anion Chloride has an positive association (R=1, P<.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, P<.00001). The clustering and network analysis visualize that the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils.

CONCLUSIONS : Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the part of the related preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management.

Nguyen Tue Trong, Ho Tu Cam, Bui Huong Thi Thu, Ho Lam Khanh, Ta Van Thanh

2023-Jan-18

Public Health Public Health

Impact of the COVID-19 pandemic and corresponding control measures on long-term care facilities: a systematic review and meta-analysis.

In Age and ageing ; h5-index 55.0

BACKGROUND : Long-term care facilities (LTCFs) were high-risk settings for COVID-19 outbreaks.

OBJECTIVE : To assess the impacts of the COVID-19 pandemic on LTCFs, including rates of infection, hospitalisation, case fatality, and mortality, and to determine the association between control measures and SARS-CoV-2 infection rates in residents and staff.

METHOD : We conducted a systematic search of six databases for articles published between December 2019 and 5 November 2021, and performed meta-analyses and subgroup analyses to identify the impact of COVID-19 on LTCFs and the association between control measures and infection rate.

RESULTS : We included 108 studies from 19 countries. These studies included 1,902,044 residents and 255,498 staff from 81,572 LTCFs, among whom 296,024 residents and 36,807 staff were confirmed SARS-CoV-2 positive. The pooled infection rate was 32.63% (95%CI: 30.29 ~ 34.96%) for residents, whereas it was 10.33% (95%CI: 9.46 ~ 11.21%) for staff. In LTCFs that cancelled visits, new patient admissions, communal dining and group activities, and vaccinations, infection rates in residents and staff were lower than the global rate. We reported the residents' hospitalisation rate to be 29.09% (95%CI: 25.73 ~ 32.46%), with a case-fatality rate of 22.71% (95%CI: 21.31 ~ 24.11%) and mortality rate of 15.81% (95%CI: 14.32 ~ 17.30%). Significant publication biases were observed in the residents' case-fatality rate and the staff infection rate, but not in the infection, hospitalisation, or mortality rate of residents.

CONCLUSION : SARS-CoV-2 infection rates would be very high among LTCF residents and staff without appropriate control measures. Cancelling visits, communal dining and group activities, restricting new admissions, and increasing vaccination would significantly reduce the infection rates.

Zhang Jun, Yu Yushan, Petrovic Mirko, Pei Xiaomei, Tian Qing-Bao, Zhang Lei, Zhang Wei-Hong

2023-Jan-08

COVID-19, control measures, long-term care facilities, older people, systematic review

Radiology Radiology

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study.

In PloS one ; h5-index 176.0

BACKGROUND : Coronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and identify risk factors associated with chest computed tomography (CT) lesion severity.

METHODS : A prospective cohort study of COVID-19 patients admitted to a tertiary hospital ICU in Brazil (March-August/2020), and followed-up six-twelve months after hospital admission. Initial assessment included: modified Medical Research Council dyspnea scale, SpO2 evaluation, forced vital capacity, and chest X-Ray. Patients with alterations in at least one of these examinations were eligible for CT and pulmonary function tests (PFTs) approximately 16 months after hospital admission. Primary outcome: CT lesion severity (fibrotic-like or non-fibrotic-like). Baseline clinical variables were used to build a machine learning model (ML) to predict the severity of CT lesion.

RESULTS : In total, 326 patients (72%) were eligible for CT and PFTs. COVID-19 CT lesions were identified in 81.8% of patients, and half of them showed mild restrictive lung impairment and impaired lung diffusion capacity. Patients with COVID-19 CT findings were stratified into two categories of lesion severity: non-fibrotic-like (50.8%-ground-glass opacities/reticulations) and fibrotic-like (49.2%-traction bronchiectasis/architectural distortion). No association between CT feature severity and altered lung diffusion or functional restrictive/obstructive patterns was found. The ML detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity(sensitivity,0.78±0.02;specificity,0.79±0.01;F1-score,0.78±0.02;positive predictive rate,0.78±0.02; accuracy,0.78±0.02; and area under the curve,0.83±0.01).

CONCLUSION : ICU hospitalization due to COVID-19 led to respiratory system alterations six-twelve months after hospital admission. Male sex and critical disease acute phase, characterized by a longer ICU and IMV period, and need for tracheostomy and vasoactive drugs, were risk factors for severe CT lesions six-twelve months after hospital admission.

Ribeiro Carvalho Carlos Roberto, Lamas Celina Almeida, Chate Rodrigo Caruso, Salge João Marcos, Sawamura Marcio Valente Yamada, de Albuquerque André L P, Toufen Junior Carlos, Lima Daniel Mario, Garcia Michelle Louvaes, Scudeller Paula Gobi, Nomura Cesar Higa, Gutierrez Marco Antonio, Baldi Bruno Guedes

2023

General General

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic.

In PloS one ; h5-index 176.0

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Chen Tongxin, Zhu Di, Cheng Tao, Gao Xiaowei, Chen Huanfa

2023

Public Health Public Health

COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.

In Medical & biological engineering & computing ; h5-index 32.0

In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.

Gomes Juliana Carneiro, de Santana Maíra Araújo, Masood Aras Ismael, de Lima Clarisse Lins, Dos Santos Wellington Pinheiro

2023-Jan-20

COVID-19 clinical diagnosis, COVID-19 computer-aided diagnosis, Deep learning, Electrocardiography, Hybrid deep architectures

General General

Social Media Devices' Influence on User Neck Pain during the COVID-19 Pandemic: Collaborating Vertebral-GLCM Extracted Features with a Decision Tree.

In Journal of imaging

The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being-in some cases-the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users' ages and the duration of the SM usage (H.mean) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.

Al-Naami Bassam, Badr Bashar E A, Rawash Yahia Z, Owida Hamza Abu, De Fazio Roberto, Visconti Paolo

2023-Jan-08

GLCM, decision tree algorithm, neck pain, smartphones, social media usage

General General

A Survey on Deep Learning in COVID-19 Diagnosis.

In Journal of imaging

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

Han Xue, Hu Zuojin, Wang Shuihua, Zhang Yudong

2022-Dec-20

COVID-19, CT images, X-ray images, classification, convolutional neural networks, deep learning, diagnosis, transfer learning

Public Health Public Health

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

In Communications biology

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Park Jonathan J, Lee Kyoung A V, Lam Stanley Z, Moon Katherine S, Fang Zhenhao, Chen Sidi

2023-Jan-20

General General

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

In Medical & biological engineering & computing ; h5-index 32.0

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

Yıldırım Emre, Cicioğlu Murtaza, Çalhan Ali

2023-Jan-21

Cloud computing, Data analytics, Fog computing, IoMT, Machine learning, WBANs

Public Health Public Health

Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa.

In BMC public health ; h5-index 82.0

OBJECTIVE : Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries.

METHODS : Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate β in the next 7, 14, and 21 days. Then, the predicted transmission rate βs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE).

RESULTS : The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted β and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 106, with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively.

CONCLUSION : Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing β respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response.

Liu Xu-Dong, Wang Wei, Yang Yi, Hou Bo-Han, Olasehinde Toba Stephen, Feng Ning, Dong Xiao-Ping

2023-Jan-19

ARIMA, COVID-19, Epidemic, Functionalized β, Machine learning, Nested model, SIRV model

Public Health Public Health

Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts.

In Scientific reports ; h5-index 158.0

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected.

Wichmann Roberta Moreira, Fernandes Fernando Timoteo, Chiavegatto Filho Alexandre Dias Porto

2023-Jan-19

General General

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.

In Pathology, research and practice

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Jeyananthan Pratheeba

2023-Jan-15

COVID-19 molecular data, Classification algorithm, Feature selection, Severity prediction, Treatment stage, lncRNA, miRNA and mRNA

General General

Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location.

In Journal of computational biology : a journal of computational molecular cell biology

With the rapid spread of COVID-19 worldwide, viral genomic data are available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a unique opportunity for analysis toward the research of effective vaccine development for current pandemics, and avoiding or mitigating future pandemics. One piece of information that comes with every such viral sequence is the geographical location where it was collected-the patterns found between viral variants and geographical location surely being an important part of this analysis. One major challenge that researchers face is processing such huge, highly dimensional data to obtain useful insights as quickly as possible. Most of the existing methods face scalability issues when dealing with the magnitude of such data. In this article, we propose an approach that first computes a numerical representation of the spike protein sequence of SARS-CoV-2 using k-mers (substrings) and then uses several machine learning models to classify the sequences based on geographical location. We show that our proposed model significantly outperforms the baselines. We also show the importance of different amino acids in the spike sequences by computing the information gain corresponding to the true class labels.

Ali Sarwan, Bello Babatunde, Tayebi Zahra, Patterson Murray

2023-Jan-19

COVID-19, SARS-CoV-2, geographical location, k-mers, sequence classification

General General

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19.

In Mathematical biosciences and engineering : MBE

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.

Singh Akansha, Singh Krishna Kant, Greguš Michal, Izonin Ivan

2022-Aug-26

** COVID-19 , Grasshopper Optimization , deep learning , diagnosis , machine learning **

Public Health Public Health

Prehospital Cardiac Arrest should be considered when evaluating Covid-19 mortality in the United States.

In Methods of information in medicine

BACKGROUND : Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is high value. Covid-19 offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES : This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the Covid-19 emergency in the United States. Here fitness for use means the statistical agreement between events across series.

METHODS : 13 weekly clinical event series from before and during the Covid-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) Covid-19 attributable mortality, CDC's excess mortality model, national Emergency Medical System (EMS) calls and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to distributed random forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS : Model r2 statistics ranged from .78 to .99 for the share of the volumes predicted correctly. Prehospital EMS data was high value and cardiac arrest prior to EMS arrival was on average the best predictor (tied with study week). Covid-19 Medicare claims volumes can predict Covid-19 death certificates (agreement) while generic viral respiratory Medicare claim volumes cannot predict Medicare Covid-19 claims (disagreement).

CONCLUSIONS : Prehospital EMS data should be considered when evaluating the severity of Covid-19 because prehospital cardiac arrest known to EMS was the strongest predictor on average across indices. Key Words Random Forest Covid-19 Public Health Statistical methods Syndromic Surveillance   1.Introduction Creating long term, multi-source, national surveillance data services for emerging disease response is a complex topic which Covid-19 has given new importance1-5. Public health emergencies seldom leave surplus time or resources to stand up novel methods and respond; further essentializing (specific) disease preparedness6-8. More often than not epidemic response is managed using preexisting data services, often legacy data series from yesteryear's epidemics9-11. Epidemic preparedness in the United States is generally weak; and the Covid-19 response is largely drawn from preexisting pan-flu emergency plans12,13. During a public health emergency, the clinical knowledge needed to respond is developed by case surveillance drawn from preexisting data series. Covid-19 has presented an unusual opportunity to evaluate agreement across surveillance efforts within the United States. The ability to detect clinical findings from surveillance nets and epidemiology methods which were not necessarily designed to detect them in meaningful ways is high priority for the future management of emerging infectious diseases. Strikingly the difference in Covid-19 mortality for SARS impacted countries (China, South Korea, Australia) vs. the United States may come down to what emergency response plan was last implemented (SARS vs. Swine Flu) and the fitness of surveillance (case specific vs general population) rather than deeper cultural, economic, or racial differences, as have been proposed in popular media14-20. 2.Objectives In this study public health surveillance data is processed using a machine learning approach to discover the relative agreement of a surveillance event series when predicting surveillance event series. Towards objectives this study seeks to assess the agreement between event series and contrast the value of traditional surveillance methods (death certificates, influenza and respiratory infection claims volumes) with non-traditional sources such as national Emergency Medical Services (EMS) call volume data in the Covid-19 era in the United States. 3.Methods 3.1 Statistic of Interest Variable importance is the statistic of interest in this study. Variable importance means that when predicting the dependent variable, an independent variable which is of comparatively higher predictive value (association) than another is of higher (predictive) use value. When considering high variable importance with weekly event series data, series which help the machine learning models learn, predict or guess the correct dependent weekly event series could be co-occurring or mutually observed events. The high variable importance scores from different sources suggests that series are observing the same real world event across surveillance efforts as they support prediction better than noise and other candidate series (other independent variables). Of special interest are 'high variable importance, independent variables' from a different data source than the dependent variable. High same source variables are most likely high in value because they are similarly distributed across study weeks to their parent-sister series and in turn are not necessarily interesting. A series of events can be said to have 'agreement value' if it has high statistical agreement with other series from a different source. Low statistical agreement suggests 'out of era' events, or events which are not driven by the same causes as other series considered here. Towards noise and disagreement, influenza and respiratory infection claims volumes are considered below with Covid-19 claims volumes. Claims volumes are traditionally used in influenza surveillance. As a test of the efficacy of the models described here, Covid-19 volumes should be able to 'out perform' influenza volumes as the Covid-19 era is largely understood to be influenza sparse. In this way respiratory and influenza events could be understood as a control arm as well as a model output of independent interest.

Williams Nick

2023-Jan-18

Public Health Public Health

Evolution of social mood in Spain throughout the COVID-19 vaccination process: a machine learning approach to tweets analysis.

In Public health

OBJECTIVES : This paper presents a new approach based on the combination of machine learning techniques, in particular, sentiment analysis using lexicons, and multivariate statistical methods to assess the evolution of social mood through the COVID-19 vaccination process in Spain.

METHODS : Analysing 41,669 Spanish tweets posted between 27 February 2020 and 31 December 2021, different sentiments were assessed using a list of Spanish words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and three valences (neutral, negative and positive). How the different subjective emotions were distributed across the tweets was determined using several descriptive statistics; a trajectory plot representing the emotional valence vs narrative time was also included.

RESULTS : The results achieved are highly illustrative of the social mood of citizens, registering the different emerging opinion clusters, gauging public states of mind via the collective valence, and detecting the prevalence of different emotions in the successive phases of the vaccination process.

CONCLUSIONS : The present combination in formal models of objective and subjective information would therefore provide a more accurate vision of social reality, in this case regarding the COVID-19 vaccination process in Spain, which will enable a more effective resolution of problems.

Turón A, Altuzarra A, Moreno-Jiménez J M, Navarro J

2022-Dec-14

COVID-19 vaccination process, Machine learning, Multivariate statistics, Sentiment analysis, Social mood, Tweets

General General

Estimating Remaining Lifespan from the Face

ArXiv Preprint

The face is a rich source of information that can be utilized to infer a person's biological age, sex, phenotype, genetic defects, and health status. All of these factors are relevant for predicting an individual's remaining lifespan. In this study, we collected a dataset of over 24,000 images (from Wikidata/Wikipedia) of individuals who died of natural causes, along with the number of years between when the image was taken and when the person passed away. We made this dataset publicly available. We fine-tuned multiple Convolutional Neural Network (CNN) models on this data, at best achieving a mean absolute error of 8.3 years in the validation data using VGGFace. However, the model's performance diminishes when the person was younger at the time of the image. To demonstrate the potential applications of our remaining lifespan model, we present examples of using it to estimate the average loss of life (in years) due to the COVID-19 pandemic and to predict the increase in life expectancy that might result from a health intervention such as weight loss. Additionally, we discuss the ethical considerations associated with such models.

Amir Fekrazad

2023-01-19

Public Health Public Health

Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States.

In Science advances

Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods-tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States-frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number Rt becomes larger than 1 for a period of 2 weeks.

Stolerman Lucas M, Clemente Leonardo, Poirier Canelle, Parag Kris V, Majumder Atreyee, Masyn Serge, Resch Bernd, Santillana Mauricio

2023-Jan-18

General General

Diffusion-based Conditional ECG Generation with Structured State Space Models

ArXiv Preprint

Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.

Juan Miguel Lopez Alcaraz, Nils Strodthoff

2023-01-19

Radiology Radiology

Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI).

In European radiology ; h5-index 62.0

OBJECTIVES : Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report the development of the International Consortium for COVID-19 Imaging AI (ICOVAI) model and perform independent external validation.

METHODS : The ICOVAI model was developed using multicenter data (n = 1286 CT scans) to quantify disease extent and assess COVID-19 likelihood using the COVID-19 Reporting and Data System (CO-RADS). A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. CO-RADS classification performance was calculated using linearly weighted Cohen's kappa and segmentation performance using Dice Similarity Coefficient (DSC).

RESULTS : Regarding internal versus external testing, segmentation performance of lung contours was equally excellent (DSC = 0.97 vs. DSC = 0.97, p = 0.97). Lung opacities segmentation performance was adequate internally (DSC = 0.76), but significantly worse on external validation (DSC = 0.59, p < 0.0001). For CO-RADS classification, agreement with radiologists on the internal set was substantial (kappa = 0.78), but significantly lower on the external set (kappa = 0.62, p < 0.0001).

CONCLUSION : In this multicenter study, a model developed for CO-RADS score prediction and quantification of COVID-19 disease extent was found to have a significant reduction in performance on independent external validation versus internal testing. The limited reproducibility of the model restricted its potential for clinical use. The study demonstrates the importance of independent external validation of AI models.

KEY POINTS : • The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.

Topff Laurens, Groot Lipman Kevin B W, Guffens Frederic, Wittenberg Rianne, Bartels-Rutten Annemarieke, van Veenendaal Gerben, Hess Mirco, Lamerigts Kay, Wakkie Joris, Ranschaert Erik, Trebeschi Stefano, Visser Jacob J, Beets-Tan Regina G H

2023-Jan-18

Artificial intelligence, COVID-19, Computed tomography, Reproducibility of results, Validation study

General General

Improving Food Detection For Images From a Wearable Egocentric Camera

ArXiv Preprint

Diet is an important aspect of our health. Good dietary habits can contribute to the prevention of many diseases and improve the overall quality of life. To better understand the relationship between diet and health, image-based dietary assessment systems have been developed to collect dietary information. We introduce the Automatic Ingestion Monitor (AIM), a device that can be attached to one's eye glasses. It provides an automated hands-free approach to capture eating scene images. While AIM has several advantages, images captured by the AIM are sometimes blurry. Blurry images can significantly degrade the performance of food image analysis such as food detection. In this paper, we propose an approach to pre-process images collected by the AIM imaging sensor by rejecting extremely blurry images to improve the performance of food detection.

Yue Han, Sri Kalyan Yarlagadda, Tonmoy Ghosh, Fengqing Zhu, Edward Sazonov, Edward J. Delp

2023-01-19

General General

Pandemic disease detection through wireless communication using infrared image based on deep learning.

In Mathematical biosciences and engineering : MBE

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing method, and there is a high chance of a false-negative ratio (FNR). Moreover, there arises a deficiency of R.T.-PCR test kits. Therefore, alternative procedures for a quick and accurate diagnosis of patients are urgently needed to deal with these pandemics. The infrared image is self-sufficient for detecting these diseases by measuring the temperature at the initial stage. C.T. scans and other pathological tests are valuable aspects of evaluating a patient with a suspected pandemic infection. However, a patient's radiological findings may not be identified initially. Therefore, we have included an Artificial Intelligence (A.I.) algorithm-based Machine Intelligence (MI) system in this proposal to combine C.T. scan findings with all other tests, symptoms, and history to quickly diagnose a patient with a positive symptom of current and future pandemic diseases. Initially, the system will collect information by an infrared camera of the patient's facial regions to measure temperature, keep it as a record, and complete further actions. We divided the face into eight classes and twelve regions for temperature measurement. A database named patient-info-mask is maintained. While collecting sample data, we incorporate a wireless network using a cloudlets server to make processing more accessible with minimal infrastructure. The system will use deep learning approaches. We propose convolution neural networks (CNN) to cross-verify the collected data. For better results, we incorporated tenfold cross-verification into the synthesis method. As a result, our new way of estimating became more accurate and efficient. We achieved 3.29% greater accuracy by incorporating the "decision tree level synthesis method" and "ten-folded-validation method". It proves the robustness of our proposed method.

Alhameed Mohammed, Jeribi Fathe, Elnaim Bushra Mohamed Elamin, Hossain Mohammad Alamgir, Abdelhag Mohammed Eltahir

2023-Jan

** convolution neural networks , deep learning , infrared image , machine intelligence , ten-folded-validation method **

General General

Causal conditional hidden Markov model for multimodal traffic prediction

ArXiv Preprint

Multimodal traffic flow can reflect the health of the transportation system, and its prediction is crucial to urban traffic management. Recent works overemphasize spatio-temporal correlations of traffic flow, ignoring the physical concepts that lead to the generation of observations and their causal relationship. Spatio-temporal correlations are considered unstable under the influence of different conditions, and spurious correlations may exist in observations. In this paper, we analyze the physical concepts affecting the generation of multimode traffic flow from the perspective of the observation generation principle and propose a Causal Conditional Hidden Markov Model (CCHMM) to predict multimodal traffic flow. In the latent variables inference stage, a posterior network disentangles the causal representations of the concepts of interest from conditional information and observations, and a causal propagation module mines their causal relationship. In the data generation stage, a prior network samples the causal latent variables from the prior distribution and feeds them into the generator to generate multimodal traffic flow. We use a mutually supervised training method for the prior and posterior to enhance the identifiability of the model. Experiments on real-world datasets show that CCHMM can effectively disentangle causal representations of concepts of interest and identify causality, and accurately predict multimodal traffic flow.

Yu Zhao, Pan Deng, Junting Liu, Xiaofeng Jia, Mulan Wang

2023-01-19

General General

On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data.

In Mathematical biosciences and engineering : MBE

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.

Zhou Yinghui, Ahmad Zubair, Almaspoor Zahra, Khan Faridoon, Tag-Eldin Elsayed, Iqbal Zahoor, El-Morshedy Mahmoud

2023-Jan

** family of distributions , healthcare sector , machine learning algorithms , mathematical properties , simulation , statistical modeling **

General General

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature: a retrospective study

bioRxiv Preprint

Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19 related publications to help scale-up the epidemiological curation process. Methods: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6,365 publications manually classified into two classes, three subclasses and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.

Knafou, J.; Haas, Q.; Borissov, N.; Counotte, M. J.; Low, N.; Imeri, H.; Ipekci, A. M.; Buitrago-Garcia, D.; Heron, L.; Amini, P.; Teodoro, D.

2023-01-19

General General

Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits

ArXiv Preprint

Restless multi-arm bandits (RMABs) is a popular decision-theoretic framework that has been used to model real-world sequential decision making problems in public health, wildlife conservation, communication systems, and beyond. Deployed RMAB systems typically operate in two stages: the first predicts the unknown parameters defining the RMAB instance, and the second employs an optimization algorithm to solve the constructed RMAB instance. In this work we provide and analyze the results from a first-of-its-kind deployment of an RMAB system in public health domain, aimed at improving maternal and child health. Our analysis is focused towards understanding the relationship between prediction accuracy and overall performance of deployed RMAB systems. This is crucial for determining the value of investing in improving predictive accuracy towards improving the final system performance, and is useful for diagnosing, monitoring deployed RMAB systems. Using real-world data from our deployed RMAB system, we demonstrate that an improvement in overall prediction accuracy may even be accompanied by a degradation in the performance of RMAB system -- a broad investment of resources to improve overall prediction accuracy may not yield expected results. Following this, we develop decision-focused evaluation metrics to evaluate the predictive component and show that it is better at explaining (both empirically and theoretically) the overall performance of a deployed RMAB system.

Paritosh Verma, Shresth Verma, Aditya Mate, Aparna Taneja, Milind Tambe

2023-01-19

General General

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

In PloS one ; h5-index 176.0

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

Khan Asad, Akram Muhammad Usman, Nazir Sajid

2023

Public Health Public Health

Applications of social media and digital technology in COVID-19 vaccination: a scoping review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Social media and digital technologies have played an essential role in disseminating information and promoting vaccination during the COVID-19 pandemic. It needs to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE : To synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS : We searched six databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccine. Articles were included if they provide original descriptions on applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstract, editorial, letter, commentary, correspondence, study protocol, and review were excluded. A modified version of the Appraisal tool for Cross-Sectional Studies was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS : A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most applied, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media seriously affect public attitudes toward COVID-19 vaccine and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and post-vaccination surveillance. The applied digital technologies included algorithms, blockchain, mHealth, IoT, and other technologies, although with some barriers to their popularization.

CONCLUSIONS : The application of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

CLINICALTRIAL : None.

Zang Shujie, Zhang Xu, Xing Yuting, Chen Jiaxian, Lin Leesa, Hou Zhiyuan

2023-Jan-13

Public Health Public Health

Technology-Enabled Collaborative Care for Concurrent Diabetes and Distress Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Feasibility Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The COVID-19 pandemic disrupted the delivery of diabetes care and worsened mental health among many patients with type 2 diabetes (T2D). This disruption puts patients with T2D at risk for poor diabetes outcomes, especially those who experience social disadvantage due to socioeconomic class, rurality, or ethnicity. The appropriate use of communication technology could reduce these gaps in diabetes care created by the pandemic and also provide support for psychological distress.

OBJECTIVE : The purpose of this study is to test the feasibility of an innovative co-designed Technology-Enabled Collaborative Care (TECC) model for diabetes management and mental health support among adults with T2D.

METHODS : We will recruit 30 adults with T2D residing in Ontario, Canada, to participate in our sequential explanatory mixed methods study. They will participate in 8 weekly web-based health coaching sessions with a registered nurse, who is a certified diabetes educator, who will be supported by a digital care team (ie, a peer mentor, an addictions specialist, a dietitian, a psychiatrist, and a psychotherapist). Assessments will be completed at baseline, 4 weeks, and 8 weeks, with a 12-week follow-up. Our primary outcome is the feasibility and acceptability of the intervention, as evident by the participant recruitment and retention rates. Key secondary outcomes include assessment completion and delivery of the intervention. Exploratory outcomes consist of changes in mental health, substance use, and physical health behaviors. Stakeholder experience and satisfaction will be explored through a qualitative descriptive study using one-on-one interviews.

RESULTS : This paper describes the protocol of the study. The recruitment commenced in June 2021. This study was registered on October 29, 2020, on ClinicalTrials.gov (Registry ID: NCT04607915). As of June 2022, all participants have been recruited. It is anticipated that data analysis will be complete by the end of 2022, with study findings available by the end of 2023.

CONCLUSIONS : The development of an innovative, technology-enabled model will provide necessary support for individuals living with T2D and mental health challenges. This TECC program will determine the feasibility of TECC for patients with T2D and mental health issues.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04607915; https://clinicaltrials.gov/ct2/show/NCT04607915.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : DERR1-10.2196/39724.

Vojtila Lenka, Sherifali Diana, Dragonetti Rosa, Ashfaq Iqra, Veldhuizen Scott, Naeem Farooq, Agarwal Sri Mahavir, Melamed Osnat C, Crawford Allison, Gerretsen Philip, Hahn Margaret, Hill Sean, Kidd Sean, Mulsant Benoit, Serhal Eva, Tackaberry-Giddens Leah, Whitmore Carly, Marttila Jennifer, Tang Frank, Ramdass Seeta, Lourido Gloria, Sockalingam Sanjeev, Selby Peter

2023-Jan-17

coaching, collaborative care, diabetes, diabetic, digital health, eHealth, feasibility, health outcome, mental health, nurse, nursing, patient education, qualitative, satisfaction, substance use, technology, type 2 diabetes mellitus, virtual care

General General

Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer's disease phenotypes and Covid-19 severity.

In Human molecular genetics ; h5-index 81.0

Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (ad) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk SNPs for ad and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to ad, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in ad. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of ad-Covid genes using our networks involving known ad and Covid-19 genes. Our machine learning analysis prioritized 36 ad-Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and ad. Finally, we mapped GWAS SNPs of ad and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an ad-Covid functional genomic resource at the brain-region level.

Khullar Saniya, Wang Daifeng

2023-Jan-16

General General

Towards an ML-Based Semantic IoT for Pandemic Management: A Survey of Enabling Technologies for COVID-19.

In Neurocomputing

The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.

Zgheib Rita, Chahbandarian Ghazar, Kamalov Firuz, Messiry Haythem El, Al-Gindy Ahmed

2023-Jan-12

COVID-19, Cloud architecture, Internet of Things, Machine Learning, Ontologies, Survey

General General

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

In The international journal of high performance computing applications

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Dommer Abigail, Casalino Lorenzo, Kearns Fiona, Rosenfeld Mia, Wauer Nicholas, Ahn Surl-Hee, Russo John, Oliveira Sofia, Morris Clare, Bogetti Anthony, Trifan Anda, Brace Alexander, Sztain Terra, Clyde Austin, Ma Heng, Chennubhotla Chakra, Lee Hyungro, Turilli Matteo, Khalid Syma, Tamayo-Mendoza Teresa, Welborn Matthew, Christensen Anders, Smith Daniel Ga, Qiao Zhuoran, Sirumalla Sai K, O’Connor Michael, Manby Frederick, Anandkumar Anima, Hardy David, Phillips James, Stern Abraham, Romero Josh, Clark David, Dorrell Mitchell, Maiden Tom, Huang Lei, McCalpin John, Woods Christopher, Gray Alan, Williams Matt, Barker Bryan, Rajapaksha Harinda, Pitts Richard, Gibbs Tom, Stone John, Zuckerman Daniel M, Mulholland Adrian J, Miller Thomas, Jha Shantenu, Ramanathan Arvind, Chong Lillian, Amaro Rommie E

2023-Jan

AI, COVID-19, Delta, GPU, HPC, SARS-CoV-2, aerosols, computational virology, deep learning, molecular dynamics, multiscale simulation, weighted ensemble

General General

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

bioRxiv Preprint

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarised and incorporated into patient outcome prediction models in several ways, however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integration approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using each single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalisation when using multiple datasets as the model input.

Cao, Y.; Ghazanfar, S.; Yang, P.; Yang, J.

2023-01-18

General General

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review.

In JMIR bioinformatics and biotechnology

BACKGROUND : In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques.

OBJECTIVE : In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases.

METHODS : The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement.

RESULTS : Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count.

CONCLUSIONS : Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.

Cardozo Glauco, Tirloni Salvador Francisco, Pereira Moro Antônio Renato, Marques Jefferson Luiz Brum

2022

COVID-19, diagnosis, laboratory tests, machine learning, prediction, review

Public Health Public Health

Association of Neutralizing Anti-spike Monoclonal Antibody Treatment With COVID-19 Hospitalization and Assessment of the Monoclonal Antibody Screening Score.

In Mayo Clinic proceedings. Innovations, quality & outcomes

OBJECTIVE : To test the hypothesis that the MASS Score performs consistently better in identifying need for monoclonal-antibody infusion throughout each "wave" of SARS-CoV-2 variant predominance during the COVID-19 pandemic and the infusion of contemporary monoclonal-antibody treatments is associated with a lower risk of hospitalization.

PATIENTS AND METHODS : In this retrospective cohort study, we evaluated the efficacy of monoclonal-antibody treatment as compared to no monoclonal-antibody treatment in symptomatic adults who tested positive for SARS-CoV-2, regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients diagnosed with COVID-19 from November 19, 2020, through May 12, 2022.

RESULTS : Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal-antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal-antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (e.g., relative risk: 0.15; 95% CI, 0.14 to 0.17).

CONCLUSIONS : Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal-antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic.

Johnson Patrick W, Kunze Katie L, Senefeld Jonathon W, Sinclair Jorge E, Isha Shahin, Satashia Parthkumar H, Bhakta Shivang, Cowart Jennifer B, Bosch Wendelyn, O’Horo Jack, Shah Sadia Z, Wadei Hani M, Edwards Michael A, Pollock Benjamin D, Edwards Alana J, Scheitel-Tulledge Sidna, Clune Caroline G, Hanson Sara N, Arndt Richard, Heyliger Alexander, Kudrna Cory, Bierle Dennis M, Buckmeier Jason R, Seville Maria Teresa A, Orenstein Robert, Libertin Claudia, Ganesh Ravindra, Franco Pablo Moreno, Razonable Raymund R, Carter Rickey E, Sanghavi Devang K, Speicher Leigh L

2023-Jan-11

CMH, Cochran Mantel Haenszel, COVID-19, Coronavirus Disease 2019, GBM, Gradient Boosting Machine, MASS, Monoclonal Antibody Screening Score, SARS-CoV-2, Severe Acute Respiratory Syndrome Corona Virus 2

Public Health Public Health

The race to understand immunopathology in COVID-19: perspectives on the impact of quantitative approaches to understand within-host interactions.

In Immunoinformatics (Amsterdam, Netherlands)

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Gazeau Sonia, Deng Xiaoyan, Ooi Hsu Kiang, Mostefai Fatima, Hussin Julie, Heffernan Jane, Jenner Adrianne L, Craig Morgan

2023-Jan-08

COVID-19, SARS-CoV-2, computational modelling, immunopathology, machine learning, mathematical modelling, population genetics, within-host dynamics

General General

Deep learning-based user experience evaluation in distance learning.

In Cluster computing

The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

Sadigov Rahim, Yıldırım Elif, Kocaçınar Büşra, Patlar Akbulut Fatma, Catal Cagatay

2023-Jan-08

Deep learning, Distance learning, NLP, Sentiment analysis

General General

Bibliometric analysis of top-cited articles in Journal of Dental Sciences.

In Journal of dental sciences

BACKGROUND/PURPOSE : Bibliometric analysis is a method for quantifying the article distribution, impact, and performance. The purpose of this study was to identify the most top-cited articles published in Journal of Dental Sciences (JDS) and further analyze their main characteristics.

MATERIALS AND METHODS : Web of Science, Journal Citation Reports database was searched to retrieve the most-cited articles in JDS published from 2007 to July 31, 2022. Among the included top-cited articles, the following parameters were recorded and analyzed: article title, article type, year, country, number of citations, and average citations pre year. Microsoft Excel was applied for the descriptive bibliometric analysis.

RESULTS : 41 top-cited articles were filtered from total 1165 JDS articles in Web of Science database. The results showed that 41 top-cited articles were cited between 20 and 186 times from Journal Citation Reports. Most of the article types are original article (28/41, 68.29%) following by review article (7/41, 17.07%). The majority of articles were originated from Taiwan (23/41, 56.10%). The top 4 most cited articles were relative to the research topic on COVID-19, lateral canal, guided-tissue regeneration barriers, and platelet-rich fibrin, respectively. However, articles analyzed by the average citations per year since publication were focused on COVID-19 followed by artificial intelligence.

CONCLUSION : This bibliometric analysis illustrates the progress and trend of researches in JDS. The results may also offer a reference for recognizing the hot issues with the most citations in JDS.

Yang Li-Chiu, Liu Fu-Hsuan, Liu Chia-Min, Yu Chuan-Hang, Chang Yu-Chao

2023-Jan

Bibliometric analysis, Citation analysis, Journal of Dental Sciences, Web of Science

General General

A systematic literature review of machine learning application in COVID-19 medical image classification.

In Procedia computer science

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

Daniel Cenggoro, Tjeng Wawan Pardamean

2023

COVID-19, classification, disease, machine learning, medical images

General General

Towards a more general drug target interaction prediction model using transfer learning.

In Procedia computer science

The topic of Drug-Target Interaction (DTI) topic has emerged nowadays since the COVID-19 outbreaks. DTI is one of the stages of finding a new cure for a recent disease. It determines whether a chemical compound would affect a particular protein, known as binding affinity. Recently, significant efforts have been devoted to artificial intelligence (AI) powered DTI. However, the use of transfer learning in DTI has not been explored extensively. This paper aims to make a more general DTI model by investigating DTI prediction method using Transfer learning. Three popular models will be tested and observed: CNN, RNN, and Transformer. Those models combined in several scenarios involving two extensive public datasets on DTI (BindingDB and DAVIS) to find the most optimum architecture. In our finding, combining the CNN model and BindingDB as the source data became the most recommended pre-trained model for real DTI cases. This conclusion was proved with the 6% AUPRC increase after fine-tuning the BindingDB pre-trained model to DAVIS dataset than without pre-training the model first.

Suhartono Derwin, Majiid Muhammad Rizki Nur, Handoyo Alif Tri, Wicaksono Pandu, Lucky Henry

2023

SMILES, deep learning, drug discovery, drug-target interaction, transfer learning

General General

Comparative analysis of deep learning models for detecting face mask.

In Procedia computer science

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-the-art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification.

Ramadhan M Vickya, Muchtar Kahlil, Nurdin Yudha, Oktiana Maulisa, Fitria Maya, Maulina Novi, Elwirehardja Gregorius Natanael, Pardamean Bens

2023

Binary Classification, Deep Learning, Face mask detection

General General

Development clustering system IDX company with k-means algorithm and DBSCAN based on fundamental indicator and ESG.

In Procedia computer science

The global pandemic covid-19 offer buying opportunity to buy business with discounted price. This phenomenon attracts new type of investor around the world. This novice investor may aware that there is indices that is followed as benchmark. This benchmark was used as guidance, however, fact shown that some of this indices constituent fails to adapt and survive during global pandemic. Research indicates that formulation on inclusion and exclusion an index may biased. This novice investor may also be aware of so called blue chips company. However, yesterday winner may become tomorrow losers. This biased classification is done by human. This experiment intentionally to counter this practice, by using cutting edge machine learning to cluster IDX company using K-Means and DBSCAN algorithm. This experiment dataset is using KOMPAS100 fundamental indicator and it's ESG attributes. Experiment result suggesting there is five cluster in terms of fundamental and ESG in KOMPAS100.

Pranata Kevin Surya, Gunawan Alexander A S, Gaol Ford Lumban

2023

DBSCAN, ESG, Fundamental Indicator, IDX, K-Means Clustering, KOMPAS100, Machine Learning

General General

The next generation of evidence-based medicine.

In Nature medicine ; h5-index 170.0

Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation 'deep' medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.

Subbiah Vivek

2023-Jan-16

Surgery Surgery

Perioperative Risk Assessment of Patients Using the MyRISK Digital Score Completed Before the Preanesthetic Consultation: Prospective Observational Study.

In JMIR perioperative medicine

BACKGROUND : The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context.

OBJECTIVE : Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications.

METHODS : This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed.

RESULTS : Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively.

CONCLUSIONS : The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.

Ferré Fabrice, Laurent Rodolphe, Furelau Philippine, Doumard Emmanuel, Ferrier Anne, Bosch Laetitia, Ba Cyndie, Menut Rémi, Kurrek Matt, Geeraerts Thomas, Piau Antoine, Minville Vincent

2023-Jan-16

chatbot, digital health, machine learning, mobile phone, perioperative risk, preanesthetic consultation

General General

Re-envisioning the design of nanomedicines: Harnessing automation and artificial intelligence.

In Expert opinion on drug delivery

INTRODUCTION : Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED : This article highlights the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION : The development of integrated workflows based on automated experiments and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Zaslavsky Jonathan, Bannigan Pauric, Allen Christine

2023-Jan-16

Artificial intelligence, Automation, Drug delivery, Machine learning, Nanomedicine, Pharmaceutical formulation development

General General

Transformer for one stop interpretable cell type annotation.

In Nature communications ; h5-index 260.0

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.

Chen Jiawei, Xu Hao, Tao Wanyu, Chen Zhaoxiong, Zhao Yuxuan, Han Jing-Dong J

2023-Jan-14

Pathology Pathology

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

In Scientific reports ; h5-index 158.0

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Köchl Katharina, Schopper Tobias, Durmaz Vedat, Parigger Lena, Singh Amit, Krassnigg Andreas, Cespugli Marco, Wu Wei, Yang Xiaoli, Zhang Yanchong, Wang Welson Wen-Shang, Selluski Crystal, Zhao Tiehan, Zhang Xin, Bai Caihong, Lin Leon, Hu Yuxiang, Xie Zhiwei, Zhang Zaihui, Yan Jun, Zatloukal Kurt, Gruber Karl, Steinkellner Georg, Gruber Christian C

2023-Jan-14

General General

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

In Computers in biology and medicine

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Wen Cuihong, Liu Shaowu, Liu Shuai, Heidari Ali Asghar, Hijji Mohammad, Zarco Carmen, Muhammad Khan

2022-Nov-22

COVID-19 recognition, Capsule network, Chest CT scan, Deep learning, Feature sampling, Lung infections

General General

Utilisation of deep learning for COVID-19 diagnosis.

In Clinical radiology

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.

Aslani S, Jacob J

2023-Feb

Public Health Public Health

Necessity and challenges for the post-pandemic Hangzhou Asian Games: An interdisciplinary data science assessment.

In Frontiers in psychology ; h5-index 92.0

BACKGROUND : The postponement of the Hangzhou Asian Games has reignited controversy over whether it is necessary and safe to hold. This study aimed to assess its necessity for Asian elite sport and the challenges brought by the COVID-19 pandemic through joint data science research on elite sports and public health Internet big data.

METHODS : For necessity, we used seven pre-pandemic Asian Games to investigate its long-term internal balance and six pre-pandemic Olympic Games to examine its contribution to the external competitiveness of Asian sport powers through bivariate Pearson correlation analyses between sport variables and holding year. For challenges, we used Johns Hopkins COVID-19 data and Tokyo 2020 Olympic data to quantify the past impact of the pandemic on elite sport by another correlation analysis between pandemic variables and the change in the weighted score of medal share (CWSMS), built a transferable linear regression model, transferred the model to Jakarta 2018 Asian Games data, and eventually forecasted the possible impact of the pandemic on the results of the Hangzhou Asian Games.

RESULTS : The proportion of gold medal countries in the Asian Games showed a long-term upward trend (Pearson r (7) = 0.849, p < 0.05), and the share of medals won by Asian countries showed a significant increasing process (Pearson r (6) = 0.901, p < 0.05). The cumulative number of COVID-19 deaths (CND) was most significantly correlated to CWSMS (Pearson r (100) = -0.455, p < 0.001). The total Olympic model output of Asian countries was 0.0115 in Tokyo 2020 and is predicted to be 0.0093 now. The prediction of CWSMS in Hangzhou was 0.0013 for China, 0.0006 for Japan, and 0.0008 for South Korea.

CONCLUSION : We documented that Asian Games played a significant role in the long-term balanced internal structure and the increasing global competitiveness of Asian elite sport. We proved that the COVID-19 pandemic has significantly affected the Olympic performance of countries worldwide, while the competitive performance at the Hangzhou Games would be less affected than the world average level. This study also highlights the importance of interdisciplinary data science research on large-scale sports events and public health.

Guo Jianwei, Zhang Xiangning, Cui Dandan

2022

Asian Games, COVID-19, Olympic Games, elite sport, public health

General General

Primary Care Physicians' and Patients' Perspectives on Equity and Health Security of Infectious Disease Digital Surveillance.

In Annals of family medicine

PURPOSE : The coronavirus disease 2019 (COVID-19) pandemic facilitated the rapid development of digital detection surveillance (DDS) for outbreaks. This qualitative study examined how DDS for infectious diseases (ID) was perceived and experienced by primary care physicians and patients in order to highlight ethical considerations for promoting patients' autonomy and health care rights.

METHODS : In-depth interviews were conducted with a purposefully selected group of 16 primary care physicians and 24 of their patients. The group was reflective of a range of ages, educational attainment, and clinical experiences from urban areas in northern and southern China. Interviews were audio recorded, transcribed, and translated. Two researchers coded data and organized it into themes. A third researcher reviewed 15% of the data and discussed findings with the other researchers to assure accuracy.

RESULTS : Five themes were identified: ambiguity around the need for informed consent with usage of DDS; importance of autonomous decision-making; potential for discrimination against vulnerable users of DDS for ID; risk of social inequity and disparate care outcomes; and authoritarian institutions' responsibility for maintaining health data security. The adoption of DDS meant some patients would be reluctant to go to the hospital for fear of either being discriminated against or forced into quarantine. Certain groups (older people and children) were thought to be vulnerable to DDS misappropriation.

CONCLUSION : These findings indicate the paramount importance of establishing national and international ethical frameworks for DDS implementation. Frameworks should guide all aspects of ID surveillance, addressing privacy protection and health security, and underscored by principles of social equity and accountability.

Wong William Chi Wai, Zhao Ivy Yan, Ma Ye Xuan, Dong Wei Nan, Liu Jia, Pang Qin, Lu Xiao Qin, Molassiotis Alex, Holroyd Eleanor

2023-Jan-12

AI, artificial intelligence, disease outbreaks, disease survelillances, ethical issue

General General

RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.

In Briefings in bioinformatics

Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent thermal instability due to in-line hydrolysis, a chemical degradation reaction. Therefore, predicting and understanding RNA degradation is a crucial and urgent task. Here we present RNAdegformer, an effective and interpretable model architecture that excels in predicting RNA degradation. RNAdegformer processes RNA sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing, while utilizing biophysical features of RNA. We demonstrate that RNAdegformer outperforms previous best methods at predicting degradation properties at nucleotide resolution for COVID-19 mRNA vaccines. RNAdegformer predictions also exhibit improved correlation with RNA in vitro half-life compared with previous best methods. Additionally, we showcase how direct visualization of self-attention maps assists informed decision-making. Further, our model reveals important features in determining mRNA degradation rates via leave-one-feature-out analysis.

He Shujun, Gao Baizhen, Sabnis Rushant, Sun Qing

2023-Jan-12

COVID-19 mRNA, bioinformatics, deep learning, mRNA vaccine degradation

Public Health Public Health

Machine Learning-Assisted Real-Time Polymerase Chain Reaction and High-Resolution Melt Analysis for SARS-CoV-2 Variant Identification.

In Analytical chemistry

Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.

Promja Sutossarat, Puenpa Jiratchaya, Achakulvisut Titipat, Poovorawan Yong, Lee Su Yin, Athamanolap Pornpat, Lertanantawong Benchaporn

2023-Jan-12

Internal Medicine Internal Medicine

Digital Cough Monitoring - A Potential Predictive Acoustic Biomarker Of Clinical Outcomes in Hospitalized COVID-19 Patients.

In Journal of biomedical informatics ; h5-index 55.0

PURPOSE : Recent developments in the field of artificial intelligence and acoustics have made it possible to objectively monitor cough in clinical and ambulatory settings. We hypothesized that time patterns of objectively measured cough in COVID-19 patients could predict clinical prognosis and help rapidly identify patients at high risk of intubation or death.

METHODS : One hundred and twenty-three patients hospitalized with COVID-19 were enrolled at University of Florida Health Shands and the Centre Hospitalier de l'Université de Montréal. Patients' cough was continuously monitored digitally along with clinical severity of disease until hospital discharge, intubation, or death. The natural history of cough in hospitalized COVID-19 disease was described and logistic models fitted on cough time patterns were used to predict clinical outcomes.

RESULTS : In both cohorts, higher early coughing rates were associated with more favorable clinical outcomes. The transitional cough rate, or maximum cough per hour rate predicting unfavorable outcomes, was 3·40 and the AUC for cough frequency as a predictor of unfavorable outcomes was 0·761. The initial 6h (0·792) and 24h (0·719) post-enrolment observation periods confirmed this association and showed similar predictive value.

INTERPRETATION : Digital cough monitoring could be used as a prognosis biomarker to predict unfavorable clinical outcomes in COVID-19 disease. With early sampling periods showing good predictive value, this digital biomarker could be combined with clinical and paraclinical evaluation and is well adapted for triaging patients in overwhelmed or resources-limited health programs.

Altshuler Ellery, Tannir Bouchra, Jolicoeur Gisèle, Rudd Matthew, Saleem Cyrus, Cherabuddi Kartikeya, Hélène Doré Dominique, Nagarsheth Parav, Brew Joe, Small Peter M, Glenn Morris J, Grandjean Lapierre Simon

2023-Jan-09

Artificial intelligence, Clinical decision making, Cough, Covid-19, Machine learning

General General

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group.

METHODS : A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers.

RESULTS : Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes.

CONCLUSIONS : ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.

Rojas-García Mariano, Vázquez Blanca, Torres-Poveda Kirvis, Madrid-Marina Vicente

2023-Jan-11

COVID-19, Lethality risk markers, Machine learning, Mexico

General General

Machine learning for optimal test admission in the presence of resource constraints.

In Health care management science

Developing rapid tools for early detection of viral infection is crucial for pandemic containment. This is particularly crucial when testing resources are constrained and/or there are significant delays until the test results are available - as was quite common in the early days of Covid-19 pandemic. We show how predictive analytics methods using machine learning algorithms can be combined with optimal pre-test screening mechanisms, greatly increasing test efficiency (i.e., rate of true positives identified per test), as well as to allow doctors to initiate treatment before the test results are available. Our optimal test admission policies account for imperfect accuracy of both the medical test and the model prediction mechanism. We derive the accuracy required for the optimized admission policies to be effective. We also show how our policies can be extended to re-testing high-risk patients, as well as combined with pool testing approaches. We illustrate our techniques by applying them to a large data reported by the Israeli Ministry of Health for RT-PCR tests from March to September 2020. Our results demonstrate that in the context of the Covid-19 pandemic a pre-test probability screening tool with conventional RT-PCR testing could have potentially increased efficiency by several times, compared to random admission control.

Elitzur Ramy, Krass Dmitry, Zimlichman Eyal

2023-Jan-12

Data analytics, Machine learning, Optimal test admission policies, Predictive analytics

Public Health Public Health

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

In Journal of hazardous materials

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

Babaan Jennieveive, Hsu Fang-Tzu, Wong Pei-Yi, Chen Pau-Chung, Guo Yue-Leon, Lung Shih-Chun Candice, Chen Yu-Cheng, Wu Chih-Da

2023-Jan-07

Diurnal changes, Ensemble learning, Geospatial artificial intelligence, Land use regression, O(3)

General General

An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction.

In Artificial intelligence in medicine ; h5-index 34.0

Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict patients' OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner's Health Facts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs to cluster patients and EHR features respectively, and conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.

Dong Xinyu, Wong Rachel, Lyu Weimin, Abell-Hart Kayley, Deng Jianyuan, Liu Yinan, Hajagos Janos G, Rosenthal Richard N, Chen Chao, Wang Fusheng

2023-Jan

Clinical decision support, Deep learning, Electronic health records, Graph neural network, Long short-term memory, Opioid overdose, Opioid poisoning

Public Health Public Health

Prognosis of COVID-19 patients using lab tests: A data mining approach.

In Health science reports

BACKGROUND : The rapid prevalence of coronavirus disease 2019 (COVID-19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID-19 patients using data mining techniques.

METHODS : In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other.

RESULTS : Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models.

CONCLUSION : Data mining methods have the potential to be used for predicting outcomes of COVID-19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID-19 patients.

Khounraz Fariba, Khodadoost Mahmood, Gholamzadeh Saeid, Pourhamidi Rashed, Baniasadi Tayebeh, Jafarbigloo Aida, Mohammadi Gohar, Ahmadi Mahnaz, Ayyoubzadeh Seyed Mohammad

2023-Jan

COVID‐19, Gradient Boosted Trees, artificial intelligence, data mining, machine learning

General General

Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?

In Economic modelling

Before vaccines became commonly available, compliance with nonpharmaceutical only preventive measures offered protection against COVID-19 infection. Compliance is therefore expected to have physical health implications for the individual and others. Moreover, in the context of the highly contagious coronavirus, perceived noncompliance can increase the subjective risk assessment of contracting the virus and, as a result, increase psychological distress. However, the implications of (public) noncompliance on the psychological health of others have not been sufficiently explored in the literature. Examining this is of utmost importance in light of the pandemic's elevated prevalence of depressive symptoms across countries. Using nationally representative data from South Africa, we explore the relationship between depressive symptoms and perceived noncompliance. We examine this relationship using a double machine learning approach while controlling for observable selection. Our result shows that the perception that neighbors are noncompliant is correlated with self-reported depressive symptoms. Therefore, in the context of a highly infectious virus, noncompliance has detrimental effects on the wellbeing of others.

Oyenubi Adeola, Kollamparambil Umakrishnan

2023-Mar

Causal inference, Double machine learning, Mental health, Negative externality, South Africa

General General

Independent regulation of Z-lines and M-linesduring sarcomere assembly in cardiac myocytesrevealed by the automatic image analysis software sarcApp

bioRxiv Preprint

Sarcomeres are the basic contractile units within cardiac myocytes, and the collective shortening of sarcomeres aligned along myofibrils generates the force driving the heartbeat. The alignment of the individual sarcomeres is important for proper force generation, and misaligned sarcomeres are associated with diseases including cardiomyopathies and COVID-19. The actin bundling protein, -actinin-2, localizes to the Z-Bodies of sarcomere precursors and the Z-Lines of sarcomeres, and has been used previously to assess sarcomere assembly and maintenance. Previous measurements of -actinin-2 organization have been largely accomplished manually, which is time-consuming and has hampered research progress. Here, we introduce sarcApp, an image analysis tool that quantifies several components of the cardiac sarcomere and their alignment in muscle cells and tissue. We first developed sarcApp to utilize deep learning- based segmentation and real space quantification to measure -actinin-2 structures and determine the organization of both precursors and sarcomeres/myofibrils. We then expanded sarcApp to analyze M-Lines using the localization of myomesin and a protein that connects the Z-Lines to the M-Line (titin). sarcApp produces 33 distinct measurements per cell and 24 per myofibril that allow for precise quantification of changes in sarcomeres, myofibrils, and their precursors. We validated this system with perturbations to sarcomere assembly. Surprisingly, we found perturbations that affected Z-Lines and M-Lines differently, suggesting that they may be regulated independently during sarcomere assembly.

Neininger-Castro, A. C.; Hayes, J. B.; Sanchez, Z. C.; Taneja, N.; Fenix, A. M.; Moparthi, S.; Vassilopoulos, S.; Burnette, D. T.

2023-01-12

General General

Coronavirus covid-19 detection by means of explainable deep learning.

In Scientific reports ; h5-index 158.0

The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

Mercaldo Francesco, Belfiore Maria Paola, Reginelli Alfonso, Brunese Luca, Santone Antonella

2023-Jan-10

Public Health Public Health

Innovations in Public Health Surveillance for Emerging Infections.

In Annual review of public health

Public health surveillance is defined as the ongoing, systematic collection, analysis, and interpretation of health data and is closely integrated with the timely dissemination of information that the public needs to know and upon which the public should act. Public health surveillance is central to modern public health practice by contributing data and information usually through a national notifiable disease reporting system (NNDRS). Although early identification and prediction of future disease trends may be technically feasible, more work is needed to improve accuracy so that policy makers can use these predictions to guide prevention and control efforts. In this article, we review the advantages and limitations of the current NNDRS in most countries, discuss some lessons learned about prevention and control from the first wave of COVID-19, and describe some technological innovations in public health surveillance, including geographic information systems (GIS), spatial modeling, artificial intelligence, information technology, data science, and the digital twin method. We conclude that the technology-driven innovative public health surveillance systems are expected to further improve the timeliness, completeness, and accuracy of case reporting during outbreaks and also enhance feedback and transparency, whereby all stakeholders should receive actionable information on control and be able to limit disease risk earlier than ever before. Expected final online publication date for the Annual Review of Public Health, Volume 44 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Jia Peng, Liu Shiyong, Yang Shujuan

2022-Jan-10

General General

Big Data in Genomic Research for Big Questions with Examples from Covid-19 and Other Zoonoses.

In Journal of applied microbiology

Omics research inevitably involves the collection and analysis of big data, which can only be handled by automated approaches. Here we point out that analysis of big data in the field of genomics dictates certain requirements, such as specialized software, quality control of input data, and simplification for visualization of the results. The latter results in loss of information, as is exemplified for phylogenetic trees. Clear communication of big data analyses can be enhanced by novel visualization strategies. The interpretation of findings is sometimes hampered when dedicated analytical tools are not fully understood by microbiologists, while the researchers performing these analyses may not have a full overview of the biology of the microbes under study. These issues are illustrated here, using SARS-Cov-2 and Salmonella enterica as zoonotic examples. Whereas in scientific communications jargon should be avoided or explained, nomenclature to group similar organisms and distinguish these from more distant relatives is not only essential, but also influences the interpretation of results. Unfortunately, changes in taxonomically accepted names are now so frequent that they hamper rather than assist research, as is illustrated with difficulties of microbiome studies. Nomenclature to group viral isolates, as is done for SARS-Cov2, is also not without difficulties. Some weaknesses in current omics research stem from poor quality of data or biased databases, and problems can be magnified by machine learning approaches. Moreover, the overall opus of scientific publications can now be considered 'big data', as is illustrated by the avalanche of Covid-19-related publications. The peer-review model of scientific publishing is only barely coping with this novel situation, resulting in retractions and publication of bogus works. The avalanche of scientific publications that originated from the current pandemic can obstruct literature searches and this will unfortunately continue over time.

Wassenaar Trudy M, Ussery David W, Rosel Adriana Cabal

2022-Dec-16

Covid-19, Salmonella, big data, genomics, omics, scientific publishing, zoonoses

General General

Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers.

In Journal of medical virology

The COVID-19 pandemic, caused by rapidly evolving variants of SARS-CoV-2, continues to be a global health threat. SARS-CoV-2 infection symptoms often intersect with other nonsevere respiratory infections, making early diagnosis challenging. There is an urgent need for early diagnostic and prognostic biomarkers to predict severity and reduce mortality when a sudden outbreak occurs. This study implemented a novel approach of integrating bioinformatics and machine learning algorithms over publicly available clinical COVID-19 transcriptome datasets. The robust seven-gene biomarker identified through this analysis can not only discriminate SARS-CoV-2 associated acute respiratory illness (ARI) from other types of ARIs but also can discriminate severe COVID-19 patients from nonsevere COVID-19 patients. Validation of the 7-gene biomarker in an independent blood transcriptome dataset of longitudinal analysis of COVID-19 patients across various stages of the disease showed that the dysregulation of the identified biomarkers during severe disease is restored during recovery, showing their prognostic potential. The blood biomarkers identified in this study can serve as potential diagnostic candidates and help reduce COVID-19-associated mortality. This article is protected by copyright. All rights reserved.

Krishnamoorthy Pandikannan, Raj Athira S, Kumar Himanshu

2023-Jan-10

Blood biomarker, Machine learning, Meta-analysis, SARS-CoV-2, Transcriptome

General General

Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19.

In Financial innovation

This paper aims to probe the influence of innovation spillovers in the artificial intelligence (AI) and financial technology (Fin-tech) industries on the value of the internet of things (IoT) companies. Python was utilized to download public information from Yahoo Finance, and then the GARCH model was used to extract the fluctuations of cross-industry innovation spillovers. Next, the Fama-French three-factor model was used to explore the interactive changes between variables. The panel data regression analysis indicates that the more firms accept innovation spillovers from other industries, the better the excess return; however, this effect differs because of industrial attributes and the environmental changes induced by COVID-19. Additionally, this study finds that investing in large-cap growth stocks of IoT firms is more likely to yield excess returns. Finally, the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation-interactive industries by referring to the practices of Singapore and South Korea.

Ho Chi-Ming

2023

AI, Covid-19, Fin-Tech, Innovation spillover, IoT

General General

Using knowledge of, attitude toward, and daily preventive practices for COVID-19 to predict the level of post-traumatic stress and vaccine acceptance among adults in Hong Kong.

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : COVID-19 has been perceived as an event triggering a new type of post-traumatic stress (PTSD) that can live during and after the pandemic itself. However, it remains unclear whether such PTSD is partly related to people's knowledge of, attitude toward and daily behavioral practices (KAP) for COVID-19.

METHODS : Through a telephone survey, we collected responses from 3,011 adult Hong Kong residents. Then using the Catboost machine learning method, we examined whether KAP predicted the participant's PTSD level, vaccine acceptance and participation in voluntary testing.

RESULTS : Results suggested that having good preventative practices for, poor knowledge of, and negative attitude toward COVID-19 were associated with greater susceptibility to PTSD. Having a positive attitude and good compliance with preventative practices significantly predicted willingness to get vaccinated and participate in voluntary testing. Good knowledge of COVID-19 predicted engagement in testing but showed little association with vaccine acceptance.

DISCUSSION : To maintain good mental health and ongoing vaccine acceptance, it is important to foster people's sense of trust and belief in health professionals' and government's ability to control COVID-19, in addition to strengthening people's knowledge of and compliance with preventative measures.

Cao Yuan, Siu Judy Yuen-Man, Choi Kup-Sze, Ho Nick Cho-Lik, Wong Kai Chun, Shum David H K

2022

COVID-19, KAP, PTSD, knowledge – attitude – behavior, vaccine

General General

Drug repositioning based on heterogeneous networks and variational graph autoencoders.

In Frontiers in pharmacology

Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.

Lei Song, Lei Xiujuan, Liu Lian

2022

COVID-19, drug repositioning, graph representation learning, heterogeneous network, variational graph autoencoders

Cardiology Cardiology

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.

In Informatics in medicine unlocked

BACKGROUND : Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.

MATERIAL AND METHOD : We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.

RESULTS : Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.

CONCLUSIONS : Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Tuncer Ilknur, Barua Prabal Datta, Dogan Sengul, Baygin Mehmet, Tuncer Turker, Tan Ru-San, Yeong Chai Hong, Acharya U Rajendra

2023

COVID-19 classification, Computed tomography images, Swin, Swin-textural, Textural feature extraction

General General

Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.

In Journal of Taibah University Medical Sciences

OBJECTIVE : The coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach. The purpose of this study was to demonstrate the effectiveness of our approach to identify drugs for the prevention of COVID-19 through literature review, cluster analysis, drug docking calculations, and clinical trial data.

METHODS : This research was conducted in four main stages. First, the text mining stage was carried out by involving Bidirectional Encoder Representations from Transformers for Biomedical to obtain vector representation of each word in the sentence from texts. The next stage generated the disease-drug associations, which were obtained from the correlation between disease and drug. Next, the clustering stage grouped the rules through the similarity of diseases by utilizing Term Frequency-Inverse Document Frequency as its feature. Finally, the drug candidate extraction stage was processed through leveraging PubChem and DrugBank databases. We further used the drug docking package AUTODOCK VINA in PyRx software to verify the results.

RESULTS : Comparative analyses showed that the percentage of findings using mining with clustering outperformed mining without clustering in all experimental settings. In addition, we suggest that the top three drugs/phytochemicals by drug docking analysis may be effective in preventing COVID-19.

CONCLUSIONS : The proposed method for text mining utilizing the clustering method is quite promising in the discovery of drug candidates for the prevention of COVID-19 through the biomedical literature.

Supianto Ahmad Afif, Nurdiansyah Rizky, Weng Chia-Wei, Zilvan Vicky, Yuwana Raden Sandra, Arisal Andria, Pardede Hilman Ferdinandus, Lee Min-Min, Huang Chien-Hung, Ng Ka-Lok

2023-Jan-04

COVID-19, Coronavirus, Drug docking, Phytochemicals, SARS-CoV-2, Text mining

Surgery Surgery

Literature analysis of artificial intelligence in biomedicine.

In Annals of translational medicine

Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

Hulsen Tim

2022-Dec

Artificial intelligence (AI), Embase, PubMed, biomedicine, deep learning (DL), healthcare, literature, machine learning (ML), medicine, neural networks (NNs)

oncology Oncology

Predictive model for BNT162b2 vaccine response in cancer patients based on blood cytokines and growth factors.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : Patients with cancer, especially hematological cancer, are at increased risk for breakthrough COVID-19 infection. So far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed.

METHODS : We employed machine learning approaches to identify a biomarker signature based on blood cytokines, chemokines, and immune- and non-immune-related growth factors linked to vaccine immunogenicity in 199 cancer patients receiving the BNT162b2 vaccine.

RESULTS : C-reactive protein (general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) correctly classified patients with a diminished vaccine response assessed at day 49 with >80% accuracy. Amongst these, CRP showed the highest predictive value for poor response to vaccine administration. Importantly, this unique signature of vaccine response was present at different studied timepoints both before and after vaccination and was not majorly affected by different anti-cancer treatments.

CONCLUSION : We propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at persistent high risk of COVID-19 despite vaccination with BNT162b2. Our data also suggest that such a signature may reflect the inherent immunological constitution of some cancer patients who are refractive to immunotherapy.

Konnova Angelina, De Winter Fien H R, Gupta Akshita, Verbruggen Lise, Hotterbeekx An, Berkell Matilda, Teuwen Laure-Anne, Vanhoutte Greetje, Peeters Bart, Raats Silke, der Massen Isolde Van, De Keersmaecker Sven, Debie Yana, Huizing Manon, Pannus Pieter, Neven Kristof Y, Ariën Kevin K, Martens Geert A, Bulcke Marc Van Den, Roelant Ella, Desombere Isabelle, Anguille Sébastien, Berneman Zwi, Goossens Maria E, Goossens Herman, Malhotra-Kumar Surbhi, Tacconelli Evelina, Vandamme Timon, Peeters Marc, van Dam Peter, Kumar-Singh Samir

2022

BNT162b2, COVID-19 vaccine, SARS-CoV-2, chemokines, cytokines, growth factors, haematological malignancies, solid cancers

General General

D3SENet: A hybrid deep feature extraction network for Covid-19 classification using chest X-ray images.

In Biomedical signal processing and control

Covid-19 is one of the biggest global epidemics seen in the world in recent years. Because of this, people's daily lifestyles, the economic conditions of countries and individuals, and most importantly, their health status has been adversely affected all over the world. Millions of people around the world have died from this disease. For this reason, rapid and accurate detection of the disease is of great importance in terms of treatment and precautions. In addition, it is especially important to correctly distinguish between Covid-19 and non-Covid-19 pneumonia diseases for correct diagnosis and treatment. These two diseases cause similar symptoms, and the symptoms and the effects of the disease on the body should be carefully examined for their differentiation. Chest X-ray images, chest computerized tomography, and swab tests are commonly used to detect patients infected with COVID-19. This disease affects the lungs the most in the body and causes fatal side effects such as shortness of breath. Therefore, medical images taken from the chest play an important role in the diagnosis of the disease. The fact that X-rays are faster and cheaper than computerized tomography has led to an increase in studies on the detection of disease with X-rays. In recent years, the impressive results of deep learning in the field of computer vision have attracted researchers to this field when working with image data. This study aims to detect these diseases on chest X-ray images collected from Covid-19 patients, pneumonia patients, and healthy individuals. We proposed a hybrid feature extraction network namely D3SENET which consists of DarkNet53, DarkNet19, DenseNet201, SqueezeNet, and EfficientNetb0. After a balanced data set was prepared, feature vectors were obtained from images using deep learning-based CNN models and the size of feature vectors was reduced by feature selection algorithms. Obtained features were classified by traditional machine learning methods such as SVMs. The number of features to be selected was tested by the iterative increment method and the parameters with the highest accuracy rate were obtained. As a result, it was seen that healthy and infected individuals were detected in 3 classes with an accuracy rate of 98.78%. In addition, the confusion matrix, precision, recall values, and F1 score of the obtained model are also given.

Kaya Mustafa, Eris Mustafa

2023-Apr

Covid-19, Deep Ensemble Network, Deep Learning, Machine Learning, Medical Image Processing

General General

Locating frontline workers' position up against COVID-19.

In Journal of family medicine and primary care

History of mankind has been brutal and marred by wars, attacks, invasions, occupying others territory and killing other human beings with their animals in the process. But now with arrival of Industrial Revolutions in last century or so, we gradually realized that for having and maintaining economic prosperity; we need others' cooperation and since then full- scale wars almost disappeared. But when we fight now and support others in the process, we realise that brute force is only occasionally used entity and most of the times technological methods are deployed to injure others. It is this rationale which makes way for people of either gender having capability to use highly advanced weaponry to enter the arena to decide fate of their side. Therefore, now war is not exclusively masculine entity and that analogy may not be appropriate in modern era. When we use masculine notion to explain our war against COVID-19, there are many shortcomings.

Gupta Harish

2022-Oct

Artificial Intelligence, essential service providers, frontline workers, health-care professionals, modern warfare

Public Health Public Health

A demonstration of Modified Treatment Policies to evaluate shifts in mobility and COVID-19 case rates in U.S. counties.

In American journal of epidemiology ; h5-index 65.0

Mixed evidence exists of associations between mobility data and COVID-19 case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall 2020 in the United States and to demonstrate modified treatment policies (MTPs) to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indices on the number of newly reported cases per 100,000 residents two weeks ahead. Primary analyses used targeted minimum loss-based estimation (TMLE) with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indices and subsequent new case rates. However, after confounder adjustment, none of the indices showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health.

Nugent Joshua R, Balzer Laura B

2023-Jan-09

COVID-19 Pandemic, Machine Learning, modified treatment policy, targeted learning

General General

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.

In Computers in biology and medicine

The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

Zhang Jun, Wu Jingyue, Qiu Yiyi, Song Aiguo, Li Weifeng, Li Xin, Liu Yecheng

2023-Jan-05

Automatic speech recognition, Diagnosis, Human-computer interaction, Machine learning, Smart hospital, Transcription

Public Health Public Health

The Use of Digital Technology for COVID-19 Detection, and Response Management in Indonesia: A Mixed Methods Study.

In Interactive journal of medical research

BACKGROUND : The COVID-19 pandemic has triggered the greater use of digital technologies as part of the healthcare response in many countries, including in Indonesia.

OBJECTIVE : The objective of our study was to identify and review the use of digital health technologies in COVID-19 detection and response management in Indonesia. It is the world's fourth most populous nation, and Southeast Asia's most populous country, with considerable public health pressures.

METHODS : This paper conducted a literature review of publicly accessible information in technical and scientific journals, as well as news articles between September 2020 to August 2022 to identify the use case examples of digital technologies in COVID-19 detection and response management in Indonesia.

RESULTS : The results are presented into three groups, namely (i) Big Data, Artificial Intelligence and Machine Learning (technologies for the collection and/or processing of data); (ii) Healthcare System Technologies (acting at the public health level); and (iii) Population Treatment (acting at the individual patient level). Some of these technologies are the result of government-academia-private sector collaborations during the pandemic, which represent a novel, multi-sectoral practice in Indonesia within the public healthcare ecosystem. A small number of the identified technologies pre-existed the pandemic, but were upgraded and adapted for the current needs.

CONCLUSIONS : Digital technologies were developed in Indonesia during the pandemic, with a direct impact in supporting the COVID-19 management, detection, response, and treatment. They addressed different areas of the technological spectrum, and with different levels of adoption, ranging from local, regional to national. The indirect impact from this wave of technological creation and use, is to provide a strong foundation for fostering future multi-sectoral collaboration within the national healthcare system of Indonesia.

Nur Aisyah Dewi, Lokopessy Alfiano Fawwaz, Naman Maryan, Diva Haniena, Manikam Logan, Adisasmito Wiku, Kozlakidis Zisis

2023-Jan-09

General General

Deep Learning Models for Multiple Face Mask Detection under a Complex Big Data Environment.

In Procedia computer science

The Covid-19 (coronavirus) pandemic creates a worldwide health crisis. According to the WHO, the effective protection system is wearing a face mask in public places. Many studies proved that carrying a face mask is also one of the precautions to decrease the possibility of viral transmission. Strict monitoring of face mask being worn by people is now enforced in many countries. Manual observation and monitoring is quite tedious. Hence, automated systems have been researched using well-kwown face mask detection methods. However, this research paper, deals with some deep learning models which can be effectively used to detect multiple face masks in a crowded environment when the amount of incoming data from sensors is huge or in otherwise stated to a Big data problem. Hence, standalone face detection models are not quite suited. Deep learning models are required in such Big data scenario which forms the essence of this study.

Rekha V, Manoharan J Samuel, Hemalatha R, Saravanan D

2022

Big Data, Complex Data analytics, Deep Learning models, Face Mask Detection, Multi – Sensor Data Acquisition

General General

A fuzzy fine-tuned model for COVID-19 diagnosis.

In Computers in biology and medicine

The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.

Esmi Nima, Golshan Yasaman, Asadi Sara, Shahbahrami Asadollah, Gaydadjiev Georgi

2023-Jan-04

Blind/Referenceless image spatial quality evaluator, COVID-19, Deep learning, Fuzzy pooling, Weighted multi-class cross-entropy

General General

Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0.

In Health and technology

OBJECTIVE : The objective of this theoretical paper is to identify conceptual solutions for securing, predicting, and improving vaccine production and supply chains.

METHOD : The case study, action research, and review method is used with secondary data - publicly available open access data.

RESULTS : A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event. A new conceptual framework is designed to integrate the emerging solutions in vaccine production and supply chains. The framework is constructed to improve the state-of-the-art by intersecting the previously isolated disciplines of edge computing; cyber-risk analytics; healthcare systems, and AI algorithms.

CONCLUSION : For healthcare systems to cope better during a disease X event than during Covid-19, we need multiple highly specific AI algorithms, targeted for solving specific problems. The proposed framework would reduce production and supply chain risk and complexity in a Disease X event.

Radanliev Petar, De Roure David

2023-Jan-04

Artificial intelligence, Disease X, Healthcare systems, Industry 4.0, Risk assessment, Vaccine production and supply chains

General General

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic.

In New generation computing

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

Sahoo Santosh Kumar, Palai G, Altahan Baraa Riyadh, Ahammad Sk Hasane, Priya P Poorna, Hossain Md Amzad, Rashed Ahmed Nabih Zaki

2023-Jan-02

Deep learning, Detectron2, Intersection over union, Object detection, Social distancing

Public Health Public Health

A measurement method for mental health based on dynamic multimodal feature recognition.

In Frontiers in public health

INTRODUCTION : The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities.

METHODS : Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment.

RESULTS : The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests.

CONCLUSION : Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems.

Xu Haibo, Wu Xiang, Liu Xin

2022

deep learning, emotion recognition, interactive assessment scale, mental health assessment, video feature extraction

Radiology Radiology

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

In Quantitative imaging in medicine and surgery

BACKGROUND : The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification.

METHODS : Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models.

RESULTS : Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images.

CONCLUSIONS : The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Sun Hongfei, Ren Ge, Teng Xinzhi, Song Liming, Li Kang, Yang Jianhua, Hu Xiaofei, Zhan Yuefu, Wan Shiu Bun Nelson, Wong Man Fung Esther, Chan King Kwong, Tsang Hoi Ching Hailey, Xu Lu, Wu Tak Chiu, Kong Feng-Ming Spring, Wang Yi Xiang J, Qin Jing, Chan Wing Chi Lawrence, Ying Michael, Cai Jing

2023-Jan-01

Coronavirus disease 2019 (COVID-19), bone suppression, chest X-ray (CXR), super-resolution

General General

Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests an external P.1 variant origin.

In Frontiers in microbiology

Brazil was the epicenter of worldwide pandemics at the peak of its second wave. The genomic/proteomic perspective of the COVID-19 pandemic in Brazil could provide insights to understand the global pandemics behavior. In this study, we track SARS-CoV-2 molecular information in Brazil using real-time bioinformatics and data science strategies to provide a comparative and evolutive panorama of the lineages in the country. SWeeP vectors represented the Brazilian and worldwide genomic/proteomic data from Global Initiative on Sharing Avian Influenza Data (GISAID) between February 2020 and August 2021. Clusters were analyzed and compared with PANGO lineages. Hierarchical clustering provided phylogenetic and evolutionary analyses of the lineages, and we tracked the P.1 (Gamma) variant origin. The genomic diversity based on Chao's estimation allowed us to compare richness and coverage among Brazilian states and other representative countries. We found that epidemics in Brazil occurred in two moments with different genetic profiles. The P.1 lineages emerged in the second wave, which was more aggressive. We could not trace the origin of P.1 from the variants present in Brazil. Instead, we found evidence pointing to its external source and a possible recombinant event that may relate P.1 to a B.1.1.28 variant subset. We discussed the potential application of the pipeline for emerging variants detection and the PANGO terminology stability over time. The diversity analysis showed that the low coverage and unbalanced sequencing among states in Brazil could have allowed the silent entry and dissemination of P.1 and other dangerous variants. This study may help to understand the development and consequences of variants of concern (VOC) entry.

Perico Camila P, De Pierri Camilla R, Neto Giuseppe Pasqualato, Fernandes Danrley R, Pedrosa Fabio O, de Souza Emanuel M, Raittz Roberto T

2022

SWeeP, big data, diversity, genomics and proteomics, machine learning, virus

General General

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images.

In Journal of bionic engineering

Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur's entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Han Yan, Chen Weibin, Heidari Ali Asghar, Chen Huiling

2023-Jan-04

Bionic algorithm, COVID-19, Kapur’s entropy, Meta-heuristic algorithm, Multi-verse optimizer, Multilevel threshold image segmentation

General General

Integration of improved YOLOv5 for face mask detector and auto-labeling to generate dataset for fighting against COVID-19.

In The Journal of supercomputing

One of the most effective deterrent methods is using face masks to prevent the spread of the virus during the COVID-19 pandemic. Deep learning face mask detection networks have been implemented into COVID-19 monitoring systems to provide effective supervision for public areas. However, previous works have limitations: the challenge of real-time performance (i.e., fast inference and low accuracy) and training datasets. The current study aims to propose a comprehensive solution by creating a new face mask dataset and improving the YOLOv5 baseline to balance accuracy and detection time. Particularly, we improve YOLOv5 by adding coordinate attention (CA) module into the baseline backbone following two different schemes, namely YOLOv5s-CA and YOLOV5s-C3CA. In detail, we train three models with a Kaggle dataset of 853 images consisting of three categories: without a mask "NM," with mask "M," and incorrectly worn mask "IWM" classes. The experimental results show that our modified YOLOv5 with CA module achieves the highest accuracy mAP@0.5 of 93.9% compared with 87% of baseline and detection time per image of 8.0 ms (125 FPS). In addition, we build an integrated system of improved YOLOv5-CA and auto-labeling module to create a new face mask dataset of 7110 images with more than 3500 labels for three categories from YouTube videos. Our proposed YOLOv5-CA and the state-of-the-art detection models (i.e., YOLOX, YOLOv6, and YOLOv7) are trained on our 7110 images dataset. In our dataset, the YOLOv5-CA performance enhances with mAP@0.5 of 96.8%. The results indicate the enhancement of the improved YOLOv5-CA model compared with several state-of-the-art works.

Pham Thi-Ngot, Nguyen Viet-Hoan, Huh Jun-Ho

2023-Jan-03

Auto-labeling, COVID-19, Coordinate attention, Deep learning, Face mask detection, YOLO, YOLOv5, You Only Look One

General General

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.

Attallah Omneya

2023-Feb-15

COVID-19, Convolution neural networks (CNN), Deep learning, Discrete wavelet transform, Dual-tree complex wavelet transform, Texture analysis

General General

MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.

In Expert systems with applications

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.

Ullah Zahid, Usman Muhammad, Gwak Jeonghwan

2023-Apr-15

COVID-19, Deep learning, Multi-task learning, Representation learning, Semi-supervised adversarial learning

General General

Critical review of conformational B-cell epitope prediction methods.

In Briefings in bioinformatics

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Cia Gabriel, Pucci Fabrizio, Rooman Marianne

2023-Jan-05

Antibody-specific epitope prediction, Benchmarking, Conformational B-cell epitope prediction, Immunoinformatics

Ophthalmology Ophthalmology

Can Tele-Neuro-Ophthalmology Be Useful Beyond the Pandemic?

In Current neurology and neuroscience reports

PURPOSE OF THE REVIEW : Neuro-ophthalmologists rapidly adopted telehealth during the COVID-19 pandemic to minimize disruption to patient care. This article reviews recent research on tele-neuro-ophthalmology adoption, current limitations, and potential use beyond the pandemic. The review considers how digital transformation, including machine learning and augmented reality, may be applied to future iterations of tele-neuro-ophthalmology.

RECENT FINDINGS : Telehealth utilization has been sustained among neuro-ophthalmologists throughout the pandemic. Adoption of tele-neuro-ophthalmology may provide solutions to subspecialty workforce shortage, patient access, physician wellness, and trainee educational needs within the field of neuro-ophthalmology. Digital transformation technologies have the potential to augment tele-neuro-ophthalmology care delivery by providing automated workflow solutions, home-based visual testing and therapies, and trainee education via simulators. Tele-neuro-ophthalmology use has and will continue beyond the COVID-19 pandemic. Digital transformation technologies, when applied to telehealth, will drive and revolutionize the next phase of tele-neuro-ophthalmology adoption and use in the years to come.

Lai Kevin E, Ko Melissa W

2023-Jan-07

Artificial intelligence, Augmented reality, Neuro-ophthalmology, Pandemic, Telehealth, Telemedicine

General General

Authentication of Covid-19 Vaccines Using Synchronous Fluorescence Spectroscopy.

In Journal of fluorescence

The present study demonstrates the potential of synchronous fluorescence spectroscopy and multivariate data analysis for authentication of COVID-19 vaccines from various manufacturers. Synchronous scanning fluorescence spectra were recorded for DNA-based and mRNA-based vaccines obtained through the NHS Central Liverpool Primary Care Network. Fluorescence spectra of DNA and DNA-based vaccines as well as RNA and RNA-based vaccines were identical to one another. The application of principal component analysis (PCA), PCA-Gaussian Mixture Models (PCA-GMM)) and Self-Organising Maps (SOM) methods to the fluorescence spectra of vaccines is discussed. The PCA is applied to extract the characteristic variables of fluorescence spectra by analysing the major attributes. The results indicated that the first three principal components (PCs) can account for 99.5% of the total variance in the data. The PC scores plot showed two distinct clusters corresponding to the DNA-based vaccines and mRNA-based vaccines respectively. PCA-GMM clustering complemented the PCA clusters by further classifying the mRNA-based vaccines and the GMM clusters revealed three mRNA-based vaccines that were not clustered with the other vaccines. SOM complemented both PCA and PCA-GMM and proved effective with multivariate data without the need for dimensions reduction. The findings showed that fluorescence spectroscopy combined with machine learning algorithms (PCA, PCA-GMM and SOM) is a useful technique for vaccination verification and has the benefits of simplicity, speed and reliability.

Assi Sulaf, Abbas Ismail, Arafat Basel, Evans Kieran, Al-Jumeily Dhiya

2023-Jan-07

Covid-19, Gaussian mixture models, Principal component analysis, Self organising maps, Synchronous fluorescence, Vaccines

Public Health Public Health

Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

In BMC bioinformatics

BACKGROUND : With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease.

RESULTS : On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development.

CONCLUSIONS : This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

Liu Xuehan, Hasan Md Rakibul, Ahmed Khandaker Asif, Hossain Md Zakir

2023-Jan-06

Autoencoder, COVID-19 diagnosis, Machine learning, Multi-omics, Severity

General General

Nurses' Work Concerns and Disenchantment during the COVID-19 Pandemic: Machine Learning Analysis of Online Discussions.

In JMIR nursing

BACKGROUND : Online forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experience and concerns.

OBJECTIVE : The objective of this study is to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic.

METHODS : We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) to identify salient topics in the collected posts. A human-in-the-loop (HITL) analysis complemented the machine learning approach, categorizing topics into themes and sub-themes. We develop insights on nurses' evolving perspective based on temporal changes.

RESULTS : We identified themes for bi-weekly periods and grouped them into 20 major themes based on the work concerns inventory framework. Dominant work concerns varied during the study period. A detailed analysis of patterns in how themes evolve over time enables us to create narratives of work concerns.

CONCLUSIONS : The analysis demonstrates that professional online forums capture nuanced details about nurse work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of online discussions could provide useful data for healthcare organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions, and to design more effective responses and planning during crises.

Jiang Haoqiang, Castellanos Arturo, Castillo Alfred, Gomes Paulo J, Li Juanjuan, VanderMeer Debra

2023-Jan-03

Internal Medicine Internal Medicine

Severe COVID-19 Infection in Type 1 and Type 2 Diabetes During the First Three Waves in Sweden.

In Diabetes care ; h5-index 125.0

OBJECTIVE : Type 2 diabetes is an established risk factor for hospitalization and death in COVID-19 infection, while findings with respect to type 1 diabetes have been diverging.

RESEARCH DESIGN AND METHODS : Using nationwide health registries, we identified all patients aged ≥18 years with type 1 and type 2 diabetes in Sweden. Odds ratios (ORs) describe the general and age-specific risk of being hospitalized, need for intensive care, or dying, adjusted for age, socioeconomic factors, and coexisting conditions, compared with individuals without diabetes. Machine learning models were used to find predictors of outcomes among individuals with diabetes positive for COVID-19.

RESULTS : Until 30 June 2021, we identified 365 (0.71%) and 11,684 (2.31%) hospitalizations in 51,402 and 504,337 patients with type 1 and 2 diabetes, respectively, with 67 (0.13%) and 2,848 (0.56%) requiring intensive care unit (ICU) care and 68 (0.13%) and 4,020 (0.80%) dying (vs 7,824,181 individuals without diabetes [41,810 hospitalizations (0.53%), 8,753 (0.11%) needing ICU care, and 10,160 (0.13%) deaths). Although those with type 1 diabetes had moderately raised odds of being hospitalized (multiple-adjusted OR 1.38 [95% CI 1.24-1.53]), there was no independent effect on ICU care or death (OR of 1.21 [95% CI 0.94-1.52] and 1.13 [95% CI 0.88-1.48], respectively). Age and socioeconomic factors were the dominating features for predicting hospitalization and death in both types of diabetes.

CONCLUSIONS : Type 2 diabetes was associated with increased odds for all outcomes, whereas patients with type 1 diabetes had moderately increased odds of hospitalization but not ICU care and death.

Edqvist Jon, Lundberg Christina, Andreasson Karin, Björck Lena, Dikaiou Pigi, Ludvigsson Johnny, Lind Marcus, Adiels Martin, Rosengren Annika

2023-Jan-06

Internal Medicine Internal Medicine

The 2000HIV study: Design, multi-omics methods and participant characteristics.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : Even during long-term combination antiretroviral therapy (cART), people living with HIV (PLHIV) have a dysregulated immune system, characterized by persistent immune activation, accelerated immune ageing and increased risk of non-AIDS comorbidities. A multi-omics approach is applied to a large cohort of PLHIV to understand pathways underlying these dysregulations in order to identify new biomarkers and novel genetically validated therapeutic drugs targets.

METHODS : The 2000HIV study is a prospective longitudinal cohort study of PLHIV on cART. In addition, untreated HIV spontaneous controllers were recruited. In-depth multi-omics characterization will be performed, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and metagenomics, functional immunological assays and extensive immunophenotyping. Furthermore, the latent viral reservoir will be assessed through cell associated HIV-1 RNA and DNA, and full-length individual proviral sequencing on a subset. Clinical measurements include an ECG, carotid intima-media thickness and plaque measurement, hepatic steatosis and fibrosis measurement as well as psychological symptoms and recreational drug questionnaires. Additionally, considering the developing pandemic, COVID-19 history and vaccination was recorded. Participants return for a two-year follow-up visit. The 2000HIV study consists of a discovery and validation cohort collected at separate sites to immediately validate any finding in an independent cohort.

RESULTS : Overall, 1895 PLHIV from four sites were included for analysis, 1559 in the discovery and 336 in the validation cohort. The study population was representative of a Western European HIV population, including 288 (15.2%) cis-women, 463 (24.4%) non-whites, and 1360 (71.8%) MSM (Men who have Sex with Men). Extreme phenotypes included 114 spontaneous controllers, 81 rapid progressors and 162 immunological non-responders. According to the Framingham score 321 (16.9%) had a cardiovascular risk of >20% in the next 10 years. COVID-19 infection was documented in 234 (12.3%) participants and 474 (25.0%) individuals had received a COVID-19 vaccine.

CONCLUSION : The 2000HIV study established a cohort of 1895 PLHIV that employs multi-omics to discover new biological pathways and biomarkers to unravel non-AIDS comorbidities, extreme phenotypes and the latent viral reservoir that impact the health of PLHIV. The ultimate goal is to contribute to a more personalized approach to the best standard of care and a potential cure for PLHIV.

Vos Wilhelm A J W, Groenendijk Albert L, Blaauw Marc J T, van Eekeren Louise E, Navas Adriana, Cleophas Maartje C P, Vadaq Nadira, Matzaraki Vasiliki, Dos Santos Jéssica C, Meeder Elise M G, Fröberg Janeri, Weijers Gert, Zhang Yue, Fu Jingyuan, Ter Horst Rob, Bock Christoph, Knoll Rainer, Aschenbrenner Anna C, Schultze Joachim, Vanderkerckhove Linos, Hwandih Talent, Wonderlich Elizabeth R, Vemula Sai V, van der Kolk Mike, de Vet Sterre C P, Blok Willem L, Brinkman Kees, Rokx Casper, Schellekens Arnt F A, de Mast Quirijn, Joosten Leo A B, Berrevoets Marvin A H, Stalenhoef Janneke E, Verbon Annelies, van Lunzen Jan, Netea Mihai G, van der Ven Andre J A M

2022

COVID-19, HIV extreme phenotype, HIV reservoir, HIV-1, cardiovascular disease, hepatic disease, multi-omics, non-AIDS comorbidities

oncology Oncology

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes.

In Cell systems

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.

Gfeller David, Schmidt Julien, Croce Giancarlo, Guillaume Philippe, Bobisse Sara, Genolet Raphael, Queiroz Lise, Cesbron Julien, Racle Julien, Harari Alexandre

2022-Dec-23

CD8(+) T cell epitopes, HLA-I peptidomics, antigen presentation, computational biology, epitope predictions, immunology, machine learning

Public Health Public Health

Analysis and forecasting of air quality index based on satellite data.

In Inhalation toxicology

OBJECTIVE : The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI.

MATERIALS AND METHODS : Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla).

RESULTS : AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores.

CONCLUSIONS : Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

Singh Tinku, Sharma Nikhil, Satakshi Kumar

2023-Jan-05

Google Earth Engine (GEE), beta distribution, pollutants, remote sensing, satellite data

Radiology Radiology

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

In Journal of medical virology

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns heart rate variability patterns in pre-symptom by tracking relationships in sequential HR data. In the cross-validation results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and AUROC of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the cross-validation: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared to the vaccinated patients. The last finding is that the model trained in a certain period of times may provide degraded diagnosis performances as the virus continues to mutate. This article is protected by copyright. All rights reserved.

Chung Heewon, Ko Hoon, Lee Hooseok, Yon Dong Keon, Lee Won Hee, Kim Tae-Seong, Kim Kyung Won, Lee Jinseok

2023-Jan-05

COVID-19, deep learning, early diagnosis, heart rate, heart rate variability, smartwatch, transformer model

General General

Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

In BMC bioinformatics

BACKGROUND : Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss.

RESULTS : By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research.

CONCLUSIONS : MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Liu Chaozhong, Wang Linhua, Liu Zhandong

2023-Jan-04

COVID-19, Data integration, Deep learning, Single-cell sequencing analysis

General General

Neurophenotypes of COVID-19: risk factors and recovery trajectories.

In Research square

Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed "long COVID". It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery trajectories. We examined post-acute outcomes following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective neuropsychological measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits ("normal cognition" neurophenotype), although mild subjective attention and memory complaints were reported. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processed speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery trajectories at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing stability, the dysexecutive group showing improvement, and the memory-speed impaired group showing persistent processing speed deficits and fatigue, as well as worse functional outcomes. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery trajectories. This information may inform phenotype-specific approaches to treatment.

Prabhakaran Divya, Day Gregory, Munipalli Bala, Rush Beth, Pudalov Lauren, Niazi Shehzad, Brennan Emily, Powers Harry, Durvasula Ravi, Athreya Arjun, Blackmon Karen

2022-Dec-21

General General

A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program.

In medRxiv : the preprint server for health sciences

BACKGROUND : As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data.

METHODS AND FINDINGS : In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values.

CONCLUSIONS : The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

FUNDING SOURCE : This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.

DISCLAIMER : The content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.

Lorman Vitaly, Razzaghi Hanieh, Song Xing, Morse Keith, Utidjian Levon, Allen Andrea J, Rao Suchitra, Rogerson Colin, Bennett Tellen D, Morizono Hiroki, Eckrich Daniel, Jhaveri Ravi, Huang Yungui, Ranade Daksha, Pajor Nathan, Lee Grace M, Forrest Christopher B, Bailey L Charles

2022-Dec-26

General General

Diagnostic performance of artificial intelligence algorithms for detection of pulmonary involvement by COVID-19 based on portable radiography.

In Medicina clinica (English ed.)

INTRODUCTION AND OBJECTIVES : To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX).

MATERIAL AND METHODS : Prospective observational study that included patients admitted for suspected COVID-19 infection in a university hospital between July and November 2020. The reference standard of pulmonary involvement by SARS-CoV-2 comprised a positive PCR test and low-tract respiratory symptoms.

RESULTS : 493 patients were included, 140 (28%) with positive PCR and 32 (7%) with SARS-CoV-2 pneumonia. The AI-B algorithm had the best diagnostic performance (areas under the ROC curve AI-B 0.73, vs. AI-A 0.51, vs. AI-C 0.57). Using a detection threshold greater than 55%, AI-B had greater diagnostic performance than the specialist [(area under the curve of 0.68 (95% CI 0.64-0.72), vs. 0.54 (95% CI 0.49-0.59)].

CONCLUSION : AI algorithms based on portable RX enabled a diagnostic performance comparable to human assessment for the detection of SARS-CoV-2 lung involvement.

Cobeñas Ricardo Luis, de Vedia María, Florez Juan, Jaramillo Daniela, Ferrari Luciana, Re Ricardo

2022-Dec-30

Artificial intelligence, COVID-19, Lung, Machine learning, Pneumonia, Thoracic RX

General General

Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition.

In Machine vision and applications

Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Castro Eduardo, Ferreira Pedro M, Rebelo Ana, Rio-Torto Isabel, Capozzi Leonardo, Ferreira Mafalda Falcão, Gonçalves Tiago, Albuquerque Tomé, Silva Wilson, Afonso Carolina, Gamelas Sousa Ricardo, Cimarelli Claudio, Daoudi Nadia, Moreira Gabriel, Yang Hsiu-Yu, Hrga Ingrid, Ahmad Javed, Keswani Monish, Beco Sofia

2023

Computer vision, Deep learning, Fashion intelligence, Image retrieval, Summer school competition

General General

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

In Journal of digital imaging

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Yin Minyue, Liang Xiaolong, Wang Zilan, Zhou Yijia, He Yu, Xue Yuhan, Gao Jingwen, Lin Jiaxi, Yu Chenyan, Liu Lu, Liu Xiaolin, Xu Chao, Zhu Jinzhou

2023-Jan-03

Asymptomatic coronavirus-disease-2019 patients, Chest CT images, Convolutional neural networks, Deep learning, Transfer learning, Transformer

Public Health Public Health

Cluster analysis of adults unvaccinated for COVID-19 based on behavioral and social factors, National Immunization Survey-Adult COVID Module, United States.

In Preventive medicine ; h5-index 62.0

By the end of 2021, approximately 15% of U.S. adults remained unvaccinated against COVID-19, and vaccination initiation rates had stagnated. We used unsupervised machine learning (K-means clustering) to identify clusters of unvaccinated respondents based on Behavioral and Social Drivers (BeSD) of COVID-19 vaccination and compared these clusters to vaccinated participants to better understand social/behavioral factors of non-vaccination. The National Immunization Survey Adult COVID Module collects data on U.S. adults from September 26-December 31,2021 (n = 187,756). Among all participants, 51.6% were male, with a mean age of 61 years, and the majority were non-Hispanic White (62.2%), followed by Hispanic (17.2%), Black (11.9%), and others (8.7%). K-means clustering procedure was used to classify unvaccinated participants into three clusters based on 9 survey BeSD items, including items assessing COVID-19 risk perception, social norms, vaccine confidence, and practical issues. Among unvaccinated adults (N = 23,397), 3 clusters were identified: the "Reachable" (23%), "Less reachable" (27%), and the "Least reachable" (50%). The least reachable cluster reported the lowest concern about COVID-19, mask-wearing behavior, perceived vaccine confidence, and were more likely to be male, non-Hispanic White, with no health conditions, from rural counties, have previously had COVID-19, and have not received a COVID-19 vaccine recommendation from a healthcare provider. This study identified, described, and compared the characteristics of the three unvaccinated subgroups. Public health practitioners, healthcare providers and community leaders can use these characteristics to better tailor messaging for each sub-population. Our findings may also help inform decisionmakers exploring possible policy interventions.

Meng Lu, Masters Nina B, Lu Peng-Jun, Singleton James A, Kriss Jennifer L, Zhou Tianyi, Weiss Debora, Black Carla L

2022-Dec-31

COVID-19 vaccines, Cluster analysis, Health communication, Health policy, SARS-CoV-2, Vaccine hesitancy

General General

Investigating mental health outcomes of undergraduates and graduate students in Taiwan during the COVID-19 pandemic.

In Journal of American college health : J of ACH

Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.

Lin Ching-Hui, Lin Szu-Yin, Hu Bo-Hsien, Lo C Owen

2023-Jan-03

College students, Covid-19, mental health, postsecondary education

General General

Users' Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Within a few months, the COVID-19 pandemic has spread to many countries and has been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates, and have faced lack of confidence before marketing in France.

OBJECTIVE : This study aims to identify and investigate the opinion of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.

METHODS : This study was conducted in two phases. First, we filtered a collection of tweets related to COVID-19 available on twitter from February to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand labeled subset of 4,548 tweets.

RESULTS : A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focus mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled to extract nearly 350k tweets in French. The sentiment analysis model achieved a 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets and 43% of neutral tweets. This allowed to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose, and that COVID-19 is a commercial argument for the pharmaceutical companies.

CONCLUSIONS : Twitter might be a useful tool to investigate the arguments of vaccine mistrust as it unveils a political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

Dupuy-Zini Alexandre, Audeh Bissan, Gérardin Christel, Duclos Catherine, Gagneux-Brunon Amandine, Bousquet Cedric

2022-Aug-09

General General

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.

In PloS one ; h5-index 176.0

During the course of this research, we came up with a brand new distribution that is superior; we then presented and analysed the mathematical properties of this distribution; finally, we assessed its fuzzy reliability function. Because the novel distribution provides a number of advantages, like the reality that its cumulative distribution function and probability density function both have a closed form, it is very useful in a wide range of disciplines that are related to data science. One of these fields is machine learning, which is a sub field of data science. We used both traditional methods and Bayesian methodologies in order to generate a large number of different estimates. A test setup might have been carried out to assess the effectiveness of both the classical and the Bayesian estimators. At last, three different sets of Covid-19 death analysis were done so that the effectiveness of the new model could be demonstrated.

Almuqrin Muqrin A, Almutlak Salemah A, Gemeay Ahmed M, Almetwally Ehab M, Karakaya Kadir, Makumi Nicholas, Hussam Eslam, Aldallal Ramy

2023

General General

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans.

In Cognitive computation

This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.

Bhatele Kirti Raj, Jha Anand, Tiwari Devanshu, Bhatele Mukta, Sharma Sneha, Mithora Muktasha R, Singhal Stuti

2022-Dec-29

COVID-19, CT scan, Chest X-ray, Deep transfer learning, Machine learning

General General

Screening of COVID-19 Based on GLCM Features from CT Images Using Machine Learning Classifiers.

In SN computer science

In healthcare, the decision-making process is crucial, including COVID-19 prevention methods should include fast diagnostic methods. Computed tomography (CT) is used to diagnose COVID patients' conditions. There is inherent variation in the texture of a CT image of COVID, much like the texture of a CT image of pneumonia. The process of diagnosing COVID images manually is difficult and challenging. Using low-resolution images and a small COVID dataset, the extraction of discriminant characteristics and fine-tuning of hyperparameters in classifiers provide challenges for computer-assisted diagnosis. In radiomics, quantitative image analysis is frequently used to evaluate the prognosis and diagnose diseases. This research tests an ML model built on GLCM features collected from chest CT images to screen for COVID-19. In this study, Support Vector Machines, K-nearest neighbors, Random Forest, and XGBoost classifiers are used together with LBGM. Tuning tests were used to regulate the hyperparameters of the model. With cross-validation, tenfold results were obtained. Random Forest and SVM were the best classification methods for GLCM features with an overall accuracy of 99.94%. The network's performance was assessed in terms of sensitivity, accuracy, and specificity.

Godbin A Beena, Jasmine S Graceline

2023

COVID-19, Feature extraction, GLCM, LGBM, Machine learning, SVM

General General

Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.

In Computer methods and programs in biomedicine update

BACKGROUND : In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic.

METHODS : This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity.

RESULTS : We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system's sensitivity while producing more stable predictions.

CONCLUSIONS : We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data. .

Villegas Marta, Gonzalez-Agirre Aitor, Gutiérrez-Fandiño Asier, Armengol-Estapé Jordi, Carrino Casimiro Pio, Fernández David Pérez, Soares Felipe, Serrano Pablo, Pedrera Miguel, García Noelia, Valencia Alfonso

2022-Dec-29

COVID-19, Mortality prediction, Recurrent Neural Network, Time series

Public Health Public Health

Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.

In BMJ health & care informatics

OBJECTIVES : The objective of this study is the implementation of an automatic procedure to weekly detect new SARS-CoV-2 variants and non-neutral variants (variants of concern (VOC) and variants of interest (VOI)).

METHODS : We downloaded spike protein primary sequences from the public resource GISAID and we represented each sequence as k-mer counts. For each week since 1 July 2020, we evaluate if each sequence represents an anomaly based on a One Class support vector machine (SVM) classification algorithm trained on neutral protein sequences collected from February to June 2020.

RESULTS : We assess the ability of the One Class classifier to detect known VOC and VOI, such as Alpha, Delta or Omicron, ahead of their official classification by health authorities. In median, the classifier predicts a non-neutral variant as outlier 10 weeks before the official date of designation as VOC/VOI.

DISCUSSION : The identification of non-neutral variants during a pandemic usually relies on indicators available during time, such as changing population size of a variant. Automatic variant surveillance systems based on protein sequences can enhance the fast identification of variants of potential concern.

CONCLUSION : Machine learning, and in particular One Class SVM classification, can support the detection of potentially VOC/VOI variants during an evolving pandemics.

Nicora Giovanna, Salemi Marco, Marini Simone, Bellazzi Riccardo

2022-Dec

COVID-19, data science, machine learning, public health informatics

General General

Fitness Dependent Optimizer with Neural Networks for COVID-19 patients.

In Computer methods and programs in biomedicine update

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

Abdulkhaleq Maryam T, Rashid Tarik A, Hassan Bryar A, Alsadoon Abeer, Bacanin Nebojsa, Chhabra Amit, Vimal S

2022-Dec-27

COVID 19, FDO, Fitness Dependent Optimizer, Machine Learning, Swarm Intelligence

General General

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images.

In Annals of operations research

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.

Gülmez Burak

2022-Dec-25

COVID-19, Convolutional neural network, Deep learning, Genetic algorithm, Xception

General General

Multi-Objective deep learning framework for COVID-19 dataset problems.

In Journal of King Saud University. Science

BACKGROUND : It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes.

METHODS : This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN).

RESULTS : The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%.

CONCLUSIONS : The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.

Mohammedqasem Roa’a, Mohammedqasim Hayder, Asad Ali Biabani Sardar, Ata Oguz, Alomary Mohammad N, Almehmadi Mazen, Amer Alsairi Ahad, Azam Ansari Mohammad

2022-Dec-28

Artificial intelligence, COVID-19, Deep learning, Hyperparameter optimization, Missing value

General General

Development of CNN-LSTM combinational architecture for COVID-19 detection.

In Journal of ambient intelligence and humanized computing

The world has been under extreme pressure due to the spread of the coronavirus. The urgency to eradicate the virus has caused distress amongst civilians and medical agencies to an equal extent. Due to anomalies observed in the results from reverse transcription-polymerase chain reaction (RTPCR) tests, more reliable options like computed tomography (CT) scan-based tests are being researched upon. In this paper, a novel combinational architecture is built upon the principles of Convolution Neural Networks (CNN) and Long Short Term Memory (LSTM) Networks to detect COVID-19 virus. This method uses chest X-ray images as inputs to combinational architecture for the classification of samples. The CNN part of the network will be used to extract features that help in the classification, and the LSTM part will be used for classification based on the extracted features. A total of 8 convolutional layers and 4 pooling layers are used for CNN and 4 LSTM layers of 64 and 128 cells respectively. Instead of the sigmoid function, a rectified linear unit function is used as an activation function. This provides non-linearity to the CNN and better accuracies in comparison. The proposed model employs a padding layer to prevent the loss of information. Accuracy, loss, F1 score, and Matthew's Correlation Coefficient (MCC) are calculated to analyse the effectiveness of the proposed architecture. The proposed model is validated using a relatively larger dataset of 7292 images. The combinational architecture provides a more informative and truthful result in the evaluation of classification as it caters to both the size of positive elements and negative elements in the dataset. The proposed CNN-LSTM model gives an accuracy of 98.91% and an MCC value of 97.84% respectively. The model is also compared with models employing transfer learning methods for similar applications.

Narula Abhinav, Vaegae Naveen Kumar

2022-Dec-24

COVID-19, Chest X-ray, Convolution neural network, Deep learning, Image processing, LSTM

General General

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies.

In Complex & intelligent systems

Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook's Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

Ghosh Indranil, Dragan Pamucar

2022-Dec-26

Ensemble empirical mode decomposition, Explainable artificial intelligence, Facebook’s prophet algorithm, Financial stress, Long short-term memory network, Technical indicators

General General

Cough Audio Analysis for COVID-19 Diagnosis.

In SN computer science

Humanity has suffered catastrophically due to the COVID-19 pandemic. One of the most reliable diagnoses of COVID-19 is RT-PCR (reverse-transcription polymer chain reaction) testing. This method, however, has its limitations. It is time consuming and requires scalability. This research work carries out a preliminary prognosis of COVID-19, which is scalable and less time consuming. The research carried out a competitive analysis of four machine-learning models namely, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory, and VGG-19 with Support Vector Machines. Out of these models, Multilayer Perceptron outperformed with higher specificity of 94.5% and accuracy of 96.8%. The results show that Multilayer Perceptron was able to distinguish between positive and negative COVID-19 coughs by a robust feature embedding technique.

Kapoor Teghdeep, Pandhi Tanya, Gupta Bharat

2023

CNN, COVID-19, COVID-19 preliminary diagnosis, Cough diagnosis, Deep learning, LSTM, MLP, Machine learning, RNN, SVM

General General

Think Twice: First for Tech, Then for Ed.

In SN computer science

The embodiment of technology in education can make learning easier, more enjoyable, and more accessible. From Learning Machines to artificial intelligence (AI), educational technology has repeatedly tested its strength as an aider or a substitute to in-person teaching. During the COVID-19 pandemic international organisations promoted the idea of the transformation of education using technology. Comparison of their texts published in 2020 with texts published in 2021 indicates that much of the early enthusiasm concerning the transition from in-person to remote learning gave its position to more thoughtful accounts after considering the learning losses and students' disappointment from the disruption of in-person relationships. This publication highlights aspects of education technology usually overlooked in futuristic accounts of education. Adopting a non-deterministic view of technology attempts to contribute to the more human-centred incorporation of technologies in education.

Photopoulos Panos, Triantis Dimos

2023

Blended learning, COVID-19, Face-to-face education, Online learning, Technological determinism, Technology driven change

General General

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.

In Journal of scientific computing

We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

Silva Vinicius L S, Heaney Claire E, Li Yaqi, Pain Christopher C

2023

COVID-19, Compartmental model, Data assimilation, Deep learning, Epidemiology, Generative adversarial networks, Reduced-order model, Spatio-temporal prediction

General General

Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.

In International journal of biomedical imaging

This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.

Islam Rumana, Tarique Mohammed

2022

General General

De novo design of site-specific protein interactions with learned surface fingerprints

bioRxiv Preprint

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Gainza, P.; Wehrle, S.; Van Hall-Beauvais, A.; Marchand, A.; Scheck, A.; Harteveld, Z.; Ni, D.; Tan, S.; Sverrisson, F.; Goverde, C.; Turelli, P.; Raclot, C.; Teslenko, A.; Pacesa, M.; Rosset, S.; Buckley, S.; Georgeon, S.; Marsden, J.; Petruzzella, A.; Liu, K.; Xu, Z.; Chai, Y.; Han, P.; Gao, G. F.; Oricchio, E.; Fierz, B.; Trono, D.; Stahlberg, H.; Bronstein, M.; Correia, B. E.

2023-01-03

General General

Biosensors - A Miraculous Detecting Tool in Combating the War against COVID-19.

In Current pharmaceutical biotechnology

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly known as COVID-19, created rack and ruin and erupted as a global epidemic. Nearly 482.3 million cases and approximately 6.1 million deaths have been reported. The World Health Organization (WHO) designated it an international medical emergency on January 30, 2020; shortly in March 2020, it was declared a pandemic. To address this situation, governments and scientists around the globe were urged to combat and prevent its spread, mainly when no treatment was available. Presently, quantitative real-time polymerase chain reaction (qRT-PCR) is the most widely utilized technique for diagnosing SARS-CoV-2. But this method is cumbersome, tedious, and might not be quickly accessible in isolated areas with a circumscribed budget. Therefore, there is a quest for novel diagnostic techniques which can diagnose the disease in a lesser time in an economical way. This paper outlines the potential of biosensors in the diagnosis of SARS-CoV-2. This review highlights the current state of presently available detection techniques, expected potential limits, and the benefits of biosensor-implicated tests against SARS-Cov-2 diagnosis. CRISPR-Cas9 implanted paper strip, field-effect transistor (FET) implanted sensor, nucleic-acid centric, aptamers-implanted biosensor, antigen-Au/Ag nanoparticles-based electrochemical biosensor, surface-enhanced Raman scattering (SERS)-based biosensor, Surface Plasmon Resonance, potential electrochemical biosensor, optical biosensor, as well as artificial intelligence (AI) are some of the novel biosensing devices that are being utilized in the prognosis of coronaviruses.

Deshmukh Rohitas, Mishra Sakshi, Singh Rajesh

2023-Jan-02

Biosensors, COVID-19, CRISPR-Cas9., SARS-CoV-2, Virus, respiratory, syndrome

Cardiology Cardiology

Did Australia's COVID-19 restrictions impact statin incidence, prevalence or adherence?

In Current problems in cardiology

OBJECTIVE : COVID-19 restrictions may have an unintended consequence of limiting access to cardiovascular care. Australia implemented adaptive interventions (e.g. telehealth consultations, digital image prescriptions, continued dispensing, medication delivery) to maintain medication access. This study investigated whether COVID-19 restrictions in different jurisdictions coincided with changes in statin incidence, prevalence and adherence.

METHODS : Analysis of a 10% random sample of national medication claims data from January 2018 to December 2020 was conducted across three Australian jurisdictions. Weekly incidence and prevalence were estimated by dividing the number statin initiations and any statin dispensing by the Australian population aged 18-99 years. Statin adherence was analysed across the jurisdictions and years, with adherence categorised as <40%, 40-79% and ≥80% based on dispensings per calendar year.

RESULTS : Overall, 309,123, 315,703 and 324,906 people were dispensed and 39029, 39816, and 44979 initiated statins in 2018, 2019 and 2020 respectively. Two waves of COVID-19 restrictions in 2020 coincided with no meaningful change in statin incidence or prevalence per week when compared to 2018 and 2019. Incidence increased 0.3% from 23.7 to 26.2 per 1000 people across jurisdictions in 2020 compared to 2019. Prevalence increased 0.14% from 158.5 to 159.9 per 1000 people across jurisdictions in 2020 compared to 2019. The proportion of adults with ≥80% adherence increased by 3.3% in Victoria, 1.4% in NSW and 1.8% in other states and territories between 2019 and 2020.

CONCLUSIONS : COVID-19 restrictions did not coincide with meaningful changes in the incidence, prevalence or adherence to statins suggesting adaptive interventions succeeded in maintaining access to cardiovascular medications.

Livori Adam C, Lukose Dickson, Bell J Simon, Webb Geoffrey I, Ilomäki Jenni

2022-Dec-28

Statin, cardiology, cardiovascular, drug utilisation, medication adherence

General General

Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns.

METHODS : Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD.

RESULTS : A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02).

LIMITATIONS : The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data.

CONCLUSIONS : The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Wasserzug Yael, Degani Yoav, Bar-Shaked Mili, Binyamin Milana, Klein Amit, Hershko Shani, Levkovitch Yechiel

2022-Dec-28

Depression screening, Machine learning, Predictive analytics, Remote patient monitoring, Speech prosody, Voice analysis

Internal Medicine Internal Medicine

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices.

In Clinical immunology (Orlando, Fla.)

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Asteris Panagiotis G, Kokoris Styliani, Gavriilaki Eleni, Tsoukalas Markos Z, Houpas Panagiotis, Paneta Maria, Koutzas Andreas, Argyropoulos Theodoros, Alkayem Nizar Faisal, Armaghani Danial J, Bardhan Abidhan, Cavaleri Liborio, Cao Maosen, Mansouri Iman, Mohammed Ahmed Salih, Samui Pijush, Gerber Gloria, Boumpas Dimitrios T, Tsantes Argyrios, Terpos Evangelos, Dimopoulos Meletios A

2022-Dec-28

Artificial intelligence, Artificial neural networks, COVID-19, Laboratory indices, SARS-CoV2

Public Health Public Health

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

In BMC medical research methodology

BACKGROUND : The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients.

METHODS : This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models.

RESULTS : Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features.

CONCLUSIONS : Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

Moslehi Samad, Mahjub Hossein, Farhadian Maryam, Soltanian Ali Reza, Mamani Mojgan

2022-Dec-31

COVID-19, Feature selection, Generalized neural additive, Laboratory markers, Machine learning, Prediction

General General

Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach.

In PloS one ; h5-index 176.0

The COVID-19 pandemic has presented unprecedented challenges for university students, creating uncertainties for their academic careers, social lives, and mental health. Our study utilized a machine learning approach to examine the degree to which students' college adjustment and coping styles impacted their adjustment to COVID-19 disruptions. More specifically, we developed predictive models to distinguish between well-adjusted and not well-adjusted students in each of five psychological domains: academic adjustment, emotionality adjustment, social support adjustment, general COVID-19 regulations response, and discriminatory impact. The predictive features used for these models are students' individual characteristics in three psychological domains, i.e., Ways of Coping (WAYS), Adaptation to College (SACQ), and Perceived Stress Scale (PSS), assessed using established commercial and open-access questionnaires. We based our study on a proprietary survey dataset collected from 517 U.S. students during the initial peak of the pandemic. Our models achieved an average of 0.91 AUC score over the five domains. Using the SHAP method, we further identified the most relevant risk factors associated with each classification task. The findings reveal the relationship of students' general adaptation to college and coping in relation to their adjustment during COVID-19. Our results could help universities identify systemic and individualized strategies to support their students in coping with stress and to facilitate students' college adjustment in this era of challenges and uncertainties.

Zhao Yijun, Ding Yi, Chekired Hayet, Wu Ying

2022

General General

Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States.

In Quality management in health care

BACKGROUND AND OBJECTIVES : Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States.

METHODS : A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool.

RESULTS : Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing.

CONCLUSION : Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

Schilling Josh, Moeller F Gerard, Peterson Rachele, Beltz Brandon, Joshi Deepti, Gartner Danielle, Vang Jee, Jain Praduman

Radiology Radiology

COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence.

In Cureus

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs.

MATERIALS AND METHODS : Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values.

RESULTS : A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78).

CONCLUSION : Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

Baruah Dhiraj, Runge Louis, Jones Richard H, Collins Heather R, Kabakus Ismail M, McBee Morgan P

2022-Nov

artificial intelligence, chest radiography, covid, receiver operating characteristic (roc) analysis, rt-pcr

General General

The role of artificial intelligence technology in the care of diabetic foot ulcers: the past, the present, and the future.

In World journal of diabetes ; h5-index 53.0

Foot ulcers are common complications of diabetes mellitus and substantially increase the morbidity and mortality due to this disease. Wound care by regular monitoring of the progress of healing with clinical review of the ulcers, dressing changes, appropriate antibiotic therapy for infection and proper offloading of the ulcer are the cornerstones of the management of foot ulcers. Assessing the progress of foot ulcers can be a challenge for the clinician and patient due to logistic issues such as regular attendance in the clinic. Foot clinics are often busy and because of manpower issues, ulcer reviews can be delayed with detrimental effects on the healing as a result of a lack of appropriate and timely changes in management. Wound photographs have been historically useful to assess the progress of diabetic foot ulcers over the past few decades. Mobile phones with digital cameras have recently revolutionized the capture of foot ulcer images. Patients can send ulcer photographs to diabetes care professionals electronically for remote monitoring, largely avoiding the logistics of patient transport to clinics with a reduction on clinic pressures. Artificial intelligence-based technologies have been developed in recent years to improve this remote monitoring of diabetic foot ulcers with the use of mobile apps. This is expected to make a huge impact on diabetic foot ulcer care with further research and development of more accurate and scientific technologies in future. This clinical update review aims to compile evidence on this hot topic to empower clinicians with the latest developments in the field.

Pappachan Joseph M, Cassidy Bill, Fernandez Cornelius James, Chandrabalan Vishnu, Yap Moi Hoon

2022-Dec-15

Artificial intelligence technology, COVID-19 pandemic, Diabetic foot ulcers, Digital photography, Mobile app, Photographic monitoring

General General

What factors can support students' deep learning in the online environment: The mediating role of learning self-efficacy and positive academic emotions?

In Frontiers in psychology ; h5-index 92.0

INTRODUCTION : In 2020, COVID-19 forced higher education institutions in many countries to turn to online distance learning. The trend of using online education has accelerated across the world. However, this change in the teaching mode has led to the decline of students' online learning quality and resulted in students being unable to do deep learning. Therefore, the current research, aimed at promoting deep learning in the online environment, constructed a theoretical model with learning self-efficacy and positive academic emotions as mediators, deep learning as the dependent variable, perceived TPACK support, peer support, technical usefulness, and ease of use as independent variables.

METHODS : The theoretical model was verified by SPSS26.0 and smartPLS3.0, and to assess the measurement and structural models, the PLS approach to structural equation modeling (SEM) was performed.

RESULTS : The study found that (a) positive academic emotions play a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning; (b) learning self-efficacy plays a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning.

DISCUSSION : The findings of this study fill the gaps in the research on the theoretical models of deep learning in the online environment and provide a theoretical basis for online teaching, learning quality, and practical improvement strategies.

Zhao Jingxian, Liu Enyun

2022

deep learning, learning self-efficacy, perceived TPACK support, perceived peer support, perceived technical usefulness and ease of use, positive academic emotions

General General

A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data.

METHODS : This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models.

RESULTS : We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation.

CONCLUSIONS : The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.

Bottrighi Alessio, Pennisi Marzio, Roveta Annalisa, Massarino Costanza, Cassinari Antonella, Betti Marta, Bolgeo Tatiana, Bertolotti Marinella, Rava Emanuele, Maconi Antonio

2022-Dec-28

COVID-19, Explainability, Machine learning, Patient risk prediction

General General

Emerging Dominant SARS-CoV-2 Variants.

In Journal of chemical information and modeling

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants, Omicron (BA.1), BA.2, and BA.4/BA.5, were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. On the basis of newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BP.1, BL*, BA.2.75*, BQ.1*, and particularly BN.1* have a high potential to become the new dominant variants to drive the next surge. Our key projection about these variants dominance made on Oct. 18, 2022 (see arXiv:2210.09485) became reality in late November 2022.

Chen Jiahui, Wang Rui, Hozumi Yuta, Liu Gengzhuo, Qiu Yuchi, Wei Xiaoqi, Wei Guo-Wei

2022-Dec-28

General General

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

In GigaScience

BACKGROUND : Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade.

RESULTS : In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants.

CONCLUSIONS : By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants.

AVAILABILITY : The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

Avila Cartes Jorge, Anand Santosh, Ciccolella Simone, Bonizzoni Paola, Della Vedova Gianluca

2022-Dec-28

, GISAID clades, SARS-CoV-2, chaos game representation, classification of genome sequences, convolutional neural networks, deep learning

General General

Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation.

MATERIAL AND METHODS : We included COVID-19 patients admitted to intensive care units for >24 hours from March 2020 to October 2021, divided into training and testing development and testing only holdout cohorts. We developed ECMO-deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0-48 hours, compared to PaO2/FiO2 (PF) ratio, Sequential Organ Failure Assessment (SOFA) score, PREdiction of Survival on ECMO Therapy-Score (PRESET) score, logistic regression (LR), and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics.

RESULTS : ECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-hour prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO had the highest AUROC (0.94 & 0.95) and AUPRC (0.54 & 0.37) in development and holdout cohorts in identifying ECMO patients without data 18-hours prior to ECMO.

DISCUSSION AND CONCLUSION : We developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multi-center validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.

Xue Bing, Shah Neel, Yang Hanqing, Kannampallil Thomas, Payne Philip Richard Orrin, Lu Chenyang, Said Ahmed Sameh

2022-Dec-28

COVID-19, ECMO, early alert, machine learning, prediction, resource allocation

General General

Towards a soft three-level voting model (Soft T-LVM) for fake news detection.

In Journal of intelligent information systems

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

Jlifi Boutheina, Sakrani Chayma, Duvallet Claude

2022-Dec-23

COVID-19., Ensemble learning models, Fake news detection, Machine learning algorithms, Natural Language Processing (NLP), Social media

General General

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19.

In Neural processing letters

COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is fine-tuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets.

Kour Harnain, Gupta Manoj K

2022-Dec-23

BERT, COVID-19, LSTM, Sentiment analysis, Transfer learning, Tweets

General General

Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space.

In Earth science informatics

Spectroscopy is a methodology for gaining knowledge of particles, especially biomolecules, by quantifying the interactions between matter and light. By examining the level of light absorbed, reflected or released by a specimen, its constituents, properties, and volume can be determined. Spectra obtained through spectroscopy procedures are quick, harmless and contactless; hence nowadays preferred in chemometrics. Due to the high dimensional nature of the spectra, it is challenging to build a robust classifier with good performance metrics. Many linear and nonlinear dimensionality reduction-based classification models have been previously implemented to overcome this issue. However, they lack in capturing the subtle details of the spectra into the low dimension space or cannot efficiently handle the nonlinearity present in the spectral data. We propose a graph-based neural network embedding approach to extract appropriate features into latent space and circumvent the spectrums' nonlinearity problem. Our approach performs dimensionality reduction into two phases: constructing a nearest neighbor graph and producing almost linear embedding using a fully connected neural network. Further, the low dimensional embedding is subjected to classification using the Random Forest algorithm. In this paper, we have implemented and compared our technique with four nonlinear dimensionality techniques widely used for spectral data analysis. In this study, we have considered five different spectral datasets belonging to specific applications. The various classification performance metrics of all the techniques are evaluated. The proposed approach is able to perform competitively well on six different low-dimensional spaces for each dataset with an accuracy score above 95% and Matthew's correlation coefficient value close to 1. The trustworthiness score of almost 1 show that the presented dimensionality reduction approach preserves the closest neighbor structure of high dimensional spectral inputs into latent space.

Yousuff Mohamed, Babu Rajasekhara

2022-Dec-23

COVID-19, Chemometrics, Dimensionality reduction, Machine learning, Random Forest, Spectroscopy

General General

A robust deep learning platform to predict CD8+ T-cell epitopes

bioRxiv Preprint

T-cells play a crucial role in the adaptive immune system by inducing an anti-tumour response, defending against pathogens, and maintaining tolerance against self-antigens, which has sparked interest in the development of T-cell-based vaccines and immunotherapies. Because screening antigens driving the T-cell response is currently low-throughput and laborious, computational methods for predicting CD8+ T-cell epitopes have emerged. However, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8+ T-cell epitopes. Therefore, we developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning platform for predicting CD8+ T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8+ T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to dissimilarity to self from cancer studies. We used TRAP to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. Thus, this study presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

Lee, C. H.-J.; Huh, J.; Buckley, P. R.; Jang, M. J.; Pereira Pinho, M.; Fernandes, R.; Antanaviciute, A.; Simmons, A.; Koohy, H.

2022-12-29

Public Health Public Health

Prevalence of Asymptomatic SARS-CoV-2 Infection in Japan.

In JAMA network open

IMPORTANCE : Real-world evidence of SARS-CoV-2 transmission is needed to understand the prevalence of infection in the Japanese population.

OBJECTIVE : To conduct sentinel screening of the Japanese population to determine the prevalence of SARS-CoV-2 infection in asymptomatic individuals, with complementary analysis for symptomatic patients as reported by active epidemiologic surveillance used by the government.

DESIGN, SETTING, AND PARTICIPANTS : This cross-sectional study of a sentinel screening program investigated approximately 1 million asymptomatic individuals with polymerase chain reaction (PCR) testing for SARS-CoV-2 infection between February 22 and December 8, 2021. Participants included children, students, employed adults, and older individuals, as well as volunteers to broadly reflect the general Japanese population in the 14 prefectures of Japan that declared a state of emergency. Saliva samples and a cycle threshold (Ct) value of approximately 40 as standard in Japan were used. Polymerase chain reaction testing for symptomatic patients was separately done by public health authorities, and the results were obtained from the Ministry of Health, Labour, and Welfare of Japan to complement data on asymptomatic infections from the present study.

MAIN OUTCOMES AND MEASURES : Temporal trends in positivity and prevalence (including surges of different variants) and demographic associations (eg, age, geographic location, and vaccination status) were assessed.

RESULTS : The positive rate of SARS-CoV-2 infection in 1 082 976 asymptomatic individuals (52.08% males; mean [SD] age 39.4 [15.7] years) was 0.03% (95% CI, 0.02%-0.05%) during periods without surges and a maximum of 0.33% (95% CI, 0.25%-0.43%) during peak surges at the Japanese standard Ct value of approximately 40; however, the positive rate would have been 10-fold less at a Ct value of 25 as used elsewhere in the world (eg, UK). There was an increase in patients with a positive PCR test result with a Ct value of 25 or 30 preceding surges in infection and hotspots of asymptomatic infections.

CONCLUSIONS AND RELEVANCE : In this cross-sectional study of asymptomatic SARS-CoV-2 infection in the general population of Japan in 2021, as investigated by sentinel surveillance, a low rate of infection was seen in the Japanese population compared with reported levels elsewhere in the world. This finding provides real-world data on the state of infection in Japan.

Suzuki Toru, Aizawa Kenichi, Shibuya Kenji, Yamanaka Shinya, Anzai Yuichiro, Kurokawa Kiyoshi, Nagai Ryozo

2022-Dec-01

Public Health Public Health

Depression and anxiety on Twitter during the COVID-19 stay-at-home period in seven major US cities.

In AJPM focus

INTRODUCTION : While surveys are a well-established instrument to capture population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders due to COVID-19, and anxiety and depression in seven major US cities utilizing Twitter data.

METHODS : We collected 18 million Tweets from January to September 2019 (baseline), and 2020 from seven US cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine-learning-based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google trends data using search query frequencies. A qualitative evaluation of trends is presented.

RESULTS : Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all seven locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer, and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual States. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results.

CONCLUSIONS : Our study demonstrates feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

Levanti Danielle, Monastero Rebecca N, Zamani Mohammadzaman, Eichstaedt Johannes C, Giorgi Salvatore, Schwartz H Andrew, Meliker Jaymie R

2022-Dec-22

coronavirus, mental health, social media, stay-at-home order

Radiology Radiology

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

In IEEE transactions on technology and society

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.

Allahabadi Himanshi, Amann Julia, Balot Isabelle, Beretta Andrea, Binkley Charles, Bozenhard Jonas, Bruneault Frederick, Brusseau James, Candemir Sema, Cappellini Luca Alessandro, Chakraborty Subrata, Cherciu Nicoleta, Cociancig Christina, Coffee Megan, Ek Irene, Espinosa-Leal Leonardo, Farina Davide, Fieux-Castagnet Genevieve, Frauenfelder Thomas, Gallucci Alessio, Giuliani Guya, Golda Adam, van Halem Irmhild, Hildt Elisabeth, Holm Sune, Kararigas Georgios, Krier Sebastien A, Kuhne Ulrich, Lizzi Francesca, Madai Vince I, Markus Aniek F, Masis Serg, Mathez Emilie Wiinblad, Mureddu Francesco, Neri Emanuele, Osika Walter, Ozols Matiss, Panigutti Cecilia, Parent Brendan, Pratesi Francesca, Moreno-Sanchez Pedro A, Sartor Giovanni, Savardi Mattia, Signoroni Alberto, Sormunen Hanna-Maria, Spezzatti Andy, Srivastava Adarsh, Stephansen Annette F, Theng Lau Bee, Tithi Jesmin Jahan, Tuominen Jarno, Umbrello Steven, Vaccher Filippo, Vetter Dennis, Westerlund Magnus, Wurth Renee, Zicari Roberto V

2022-Dec

Artificial intelligence, COVID-19, Z-Inspection®, case study, ethical tradeoff, ethics, explainable AI, healthcare, pandemic, radiology, trust, trustworthy AI

General General

Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression.

In Biomedical signal processing and control

Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data.

You Mingyu, Wang Weihao, Li You, Liu Jiaming, Xu Xianghuai, Qiu Zhongmin

2022-Feb

BiLSTM, Boundary regression, C-BiLSTM, Cough detection, Deep learning

General General

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach.

In Energy (Oxford, England)

The COVID-19 pandemic broke the balance of oil supply and demand. Meeting these oil market challenges induced by the pandemic required a more accurate assessment of the impact of the pandemic on oil consumption. The existing measurement of the impact of the pandemic on oil consumption was based on year-over-year calculation. In this work, a new measurement approach based on a comparison of simulated and actual oil consumption was proposed. In this proposed measurement model, the actual oil consumption was from the official statistics, whereas the simulated oil demand came from business-as-usual (without pandemic) scenario simulation. In order to reduce the simulation error, three hybrid simulation approaches were developed by combining the simulation technique and machine learning technique. The mean relative errors of the proposed simulation approaches were between 1.08% and 2.51%, within the high precision level. An empirical research on the US oil consumption was conducted by running the proposed measurement model. Through analyzing the difference between the simulated and real US oil consumption, we found the impact of the epidemic on U.S. oil consumption was obvious in April-May 2020 and January-February 2021. At its worst, the oil decline in the United States reached 973 trillion British thermal units, compared to the state without the epidemic. During the entire survey period (January 2020-March 2021), the US oil consumption under the epidemic was about 18.14% lower than that under the normal epidemic-free situation, which was 5% higher than the 13% inter-annual decline rate reported. This work contributed to understand the impact of the pandemic on oil consumption more comprehensively, and also provided a new approach for analyzing the impact of the pandemic on energy consumption.

Wang Qiang, Li Shuyu, Zhang Min, Li Rongrong

2022-Jan-15

COVID-19, Pandemic-free scenario, Simulation, U.S. petroleum

General General

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance.

In Journal of air transport management

One of the purposes of Artificial Intelligence tools is to ease the analysis of large amounts of data. In order to support the strategic decision-making process of the airlines, this paper proposes a Data Mining approach (focused on visualization) with the objective of extracting market knowledge from any database of industry players or competitors. The method combines two clustering techniques (Self-Organizing Maps, SOMs, and K-means) via unsupervised learning with promising dynamic applications in different sectors. As a case study, 30-year data from 18 diverse US passenger airlines is used to showcase the capabilities of this tool including the identification and assessment of market trends, M&A events or the COVID-19 consequences.

Pérez-Campuzano Darío, Rubio Andrada Luis, Morcillo Ortega Patricio, López-Lázaro Antonio

2022-Jun

Airlines, COVID-19, Data mining (DM), K-means, Self-organizing map (SOM), Unsupervised learning

General General

Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.

In Ecological informatics

In this study, mean monthly and diurnal variations in fine particulate matters (PM2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NOx, O3, nitrate (NO3 -), and sulfate (SO4 2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NOx concentration due to a decrease in traffic flow under the NOx-saturated regime was observed to enhance the secondary NO3 - and O3 formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NOx, O3, NO3 -, and SO4 2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH3, HNO3, and H2SO4, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Lin Guan-Yu, Chen Wei-Yea, Chieh Shao-Heng, Yang Yi-Tsung

2022-Jul

And SO42− prediction, Artificial neural network, Level 3 COVID-19 alert, Meteorological effect factors, NO3−, NOx, O3

General General

Covid-19 Diagnosis by WE-SAJ.

In Systems science & control engineering

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

Wang Wei, Zhang Xin, Wang Shui-Hua, Zhang Yu-Dong

2022-Dec-31

COVID-19, Deep Learning, Diagnosis, Jaya, Self-adaptive Jaya, Wavelet Entropy

General General

Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread.

In Computers & industrial engineering

Deep Neural Networks (DNN) form a powerful deep learning model that can process unprecedented volumes of data. The hyperparameters of DNN have a significant influence on its prediction performance. Evolutionary algorithms (EAs) form a heuristic-based approach that provides an opportunity to optimize deep learning models to obtain good performance. Therefore, we propose an evolutionary deep learning model called IPSO-DNN based on DNN for prediction and an improved Particle Swarm Optimization (IPSO) algorithm to optimize the kernel hyperparameters of DNN in a self-adaptive evolutionary way. In the IPSO algorithm, a micro population size setting is introduced to improve the search efficiency of the algorithm, and the generalized opposition-based learning strategy is used to guide the population evolution. In addition, the IPSO algorithm employs a self-adaptive update strategy to prevent premature convergence and then improves the exploitation and exploration parameter optimization performance of DNN. In this paper, we show that the IPSO algorithm provides an efficient approach for tuning the hyperparameters of DNN with saving valuable computational resources. We explore the proposed IPSO-DNN model to predict the effect of social distancing on the spread of COVID-19 based on the social distancing metrics. The preliminary experimental results reveal that the proposed IPSO-DNN model has the least computation cost and yields better prediction accuracy results when compared to the other models. The experiments of the IPSO-DNN model also illustrate that aggressive and extensive social distancing interventions are crucial to help flatten the COVID-19 epidemic curve in the United States.

Liu Dixizi, Ding Weiping, Dong Zhijie Sasha, Pedrycz Witold

2022-Apr

COVID-19 Pandemic, Deep Neural Network, Evolutionary Algorithm, Generalized Opposition-Based Learning, Particle Swarm Optimization, Social Distancing

General General

Modeling the social influence of COVID-19 via personalized propagation with deep learning.

In World wide web

Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to analyze the proposed algorithm's efficiency and effectiveness. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

Liu Yufei, Cao Jie, Wu Jia, Pi Dechang

2022-Dec-17

COVID-19, Deep learning, Personalized propagation, Social influence

General General

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods.

In Applied energy ; h5-index 131.0

Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%-25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector.

Şahin Utkucan, Ballı Serkan, Chen Yan

2021-Nov-15

Covid-19, Electricity generation, Forecasting, Fractional grey model, Machine learning, Seasonal fluctuations

General General

A physiological signal compression approach using optimized Spindle Convolutional Auto-encoder in mHealth applications.

In Biomedical signal processing and control

BACKGROUND AND OBJECTIVES : The COVID-19 pandemic manifested the need of developing robust digital platforms for facilitating healthcare services such as consultancy, clinical therapies, real time remote monitoring, early diagnosis and future predictions. Innovations made using technologies such as Internet of Things (IoT), edge computing, cloud computing and artificial intelligence are helping address this crisis. The urge for remote monitoring, symptom analysis and early detection of diseases lead to tremendous increase in the deployment of wearable sensor devices. They facilitate seamless gathering of physiological data such as electrocardiogram (ECG) signals, respiration traces (RESP), galvanic skin response (GSR), pulse rate, body temperature, photoplethysmograms (PPG), oxygen saturation (SpO2) etc. For diagnosis and analysis purpose, the gathered data needs to be stored. Wearable devices operate on batteries and have a memory constraint. In mHealth application architectures, this gathered data is hence stored on cloud based servers. While transmitting data from wearable devices to cloud servers via edge devices, a lot of energy is consumed. This paper proposes a deep learning based compression model SCAElite that reduces the data volume, enabling energy efficient transmission.

RESULTS : Stress Recognition in Automobile Drivers dataset and MIT-BIH dataset from PhysioNet are used for validation of algorithm performance. The model achieves a compression ratio of up to 300 fold with reconstruction errors within 8% over the stress recognition dataset and 106.34-fold with reconstruction errors within 8% over the MIT-BIH dataset. The computational complexity of SCAElite is 51.65% less compared to state-of-the-art deep compressive model.

CONCLUSION : It is experimentally validated that SCAElite guarantees a high compression ratio with good quality restoration capabilities for physiological signal compression in mHealth applications. It has a compact architecture and is computationally more efficient compared to state-of-the-art deep compressive model.

Barot Vishal, Patel Dr Ritesh

2022-Mar

Data compression, Energy efficiency, Physiological signal compression, Spindle Convolutional Auto-encoder, mHealth applications

General General

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

bioRxiv Preprint

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. Here we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction. The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1,046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Chen, J.; Kuhn, L. A.; Raschka, S.

2022-12-27

General General

A comparative bibliometric analysis of Omicron and Delta variants during the COVID-19 pandemic.

In Annals of palliative medicine ; h5-index 20.0

BACKGROUND : To compare the research hotspots of infections with the Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic and to identify future research trends.

METHODS : Studies about Delta and Omicron variant infections published over the last 3 years were retrieved from the Web of Science (WoS) database. A comparative bibliometric analysis was conducted through machine learning and visualization tools, including VOSviewer, Bibliographic Item Co-Occurrence Matrix Builder, and Graphical Clustering Toolkit. Research hotspots and trends in the field were analyzed, and the contributions and collaborations of countries, institutions, and authors were documented. A cross-sectional analysis of the relevant studies registered at ClinicalTrials.gov was also performed to clarify the direction of future research.

RESULTS : A total of 1,787 articles distributed in 107 countries and 374 publications from 77 countries focused on the Delta and Omicron variants were included in our bibliometric analysis. The top five productive countries in both variants were the USA, China, the UK, India, and Germany. In 5,999 and 1,107 keywords identified from articles on the Delta and Omicron, the top two frequent keywords were the same: "COVID-19" (occurrence: 713, total link strength: 1,525 in Delta; occurrence: 137, total link strength: 354 in Omicron), followed by "SARS-CoV-2" (occurrence: 553, total link strength: 1,478 in Delta; occurrences 132, total link strength: 395 in Omicron). Five theme clusters from articles on Delta variant were identified: transmission, molecular structure, activation mode, epidemiology, and co-infection. While other three theme clusters were recognized for the Omicron variant: vaccine, human immune response, and infection control. Meanwhile, 21 interventional studies had been registered up to April 2022, most of which aimed to evaluate the immunogenicity and safety of different kinds of vaccines in various populations.

CONCLUSIONS : Publications and clinical trials related to COVID-19 increased annually. As the first comparative bibliometric analysis for Delta and Omicron variants, we noticed that the relevant research trends have shifted from vaccine development to infection control and management of complications. The ongoing clinical studies will verify the safety and efficacy of promising drugs.

Liu Yang-Xi, Wang Li-Hui, Zhu Cheng, Zha Qiong-Fang, Yu Yue-Tian

2022-Dec-14

Bibliometric analysis, Delta variant, Omicron variant, coronavirus disease (COVID), research trends

General General

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.

In Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

Wang Wei, Pei Yanrong, Wang Shui-Hua, Gorrz Juan Manuel, Zhang Yu-Dong

2023

COVID-19, Convolutional Neural Network, Hyperparameters Tuning, Particle Swarm Optimisation, SARS-CoV-2

General General

Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images.

In Expert systems with applications

The COVID-19 pandemic has been affecting the world since December 2019, and nowadays, the number of infected is increasing rapidly. Chest X-ray images are clinical adjuncts that can be used in the diagnosis of COVID-19 disease. Because of the rapid spread of COVID-19 disease worldwide and the limited number of expert radiologists, the proposed method uses the automatic diagnosis method rather than a manual diagnosis method. In the paper, COVID-19 Positive/Negative (2275 Positive, 4626 Negative) and Normal/Pneumonia (2313 Normal, 2313 Pneumonia) are diagnosed using chest X-ray images. Herein, 80 % and 20 % of the images are used in the training and validation set, respectively. In the proposed method, six different classifiers are trained using chest X-ray images, and the five most successful classifiers are used in both phases. In Phase-1 and Phase-2, image features are extracted using the Bag of Features method for Cosine K-Nearest Neighbor (KNN), Linear Discriminant, Logistic Regression, Bagged Trees Ensemble, Medium Gaussian Support Vector Machine (SVM), excluding SqueezeNet Deep Learning (K = 2000 and K = 1500 for Phase-1 and Phase-2, respectively). In both phases, the five most successful classifiers are determined, and images classify with the help of the Majority Voting (Mathematical Evaluation) method. The application of the proposed method is designed for users to diagnose COVID-19 Positive, Normal, and Pneumonia. The results show that accuracy values obtained by Majority Voting (Mathematical Evaluation) method for Phase-1 and Phase-2 are equal to 99.86 % and 99.28 %, respectively. Thus, it indicates that the accuracy of the whole system is 99.63 %. When we analyze the classification performance metrics for Phase-1 and Phase-2, Specificity (%), Precision (%), Recall (%), F1 Score (%), Area Under Curve (AUC), and Matthews Correlation Coefficient (MCC) are equal to 99.98-99.83-99.07-99.51-0.9974-0.9855 and 99.73-99.69-98.63-99.23-0.9928-0.9518, respectively. Moreover, if the classification performance metrics of the whole system are examined, it is seen that Specificity (%), Precision (%), Recall (%), F1 Score (%), AUC, and MCC are 99.88, 99.78, 98.90, 99.40, 0.9956, and 0.9720, respectively. When the studies in the literature are examined, the results show that the proposed model is better than its counterparts. Because the best performance metrics for the dataset used were obtained in this study. In addition, since the biphasic majority voting technique is used in the study, it is seen that the proposed model is more reliable. On the other hand, although there are tens of thousands of studies on this subject, the usability of these models is debatable since most of them do not have graphical user interface applications. Already, in artificial intelligence technologies, besides the performance of the developed models, their usability is also important. Because the developed models can generally be used by people who are less knowledgeable about artificial intelligence.

Sunnetci Kubilay Muhammed, Alkan Ahmet

2023-Apr-15

Bag of features, COVID-19, Deep learning, Machine learning, Majority voting

General General

Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation.

In Applied soft computing

With the widespread deployment of COVID-19 vaccines all around the world, billions of people have benefited from the vaccination and thereby avoiding infection. However, huge amount of clinical cases revealed diverse side effects of COVID-19 vaccines, among which cervical lymphadenopathy is one of the most frequent local reactions. Therefore, rapid detection of cervical lymph node (LN) is essential in terms of vaccine recipients' healthcare and avoidance of misdiagnosis in the post-pandemic era. This paper focuses on a novel deep learning-based framework for the rapid diagnosis of cervical lymphadenopathy towards COVID-19 vaccine recipients. Existing deep learning-based computer-aided diagnosis (CAD) methods for cervical LN enlargement mostly only depend on single modal images, e.g., grayscale ultrasound (US), color Doppler ultrasound, and CT, while failing to effectively integrate information from the multi-source medical images. Meanwhile, both the surrounding tissue objects of the cervical LNs and different regions inside the cervical LNs may imply valuable diagnostic knowledge which is pending for mining. In this paper, we propose an Tissue-Aware Cervical Lymph Node Diagnosis method (TACLND) via multi-modal ultrasound semantic segmentation. The method effectively integrates grayscale and color Doppler US images and realizes a pixel-level localization of different tissue objects, i.e., lymph, muscle, and blood vessels. With inter-tissue and intra-tissue attention mechanisms applied, our proposed method can enhance the implicit tissue-level diagnostic knowledge in both spatial and channel dimension, and realize diagnosis of cervical LN with normal, benign or malignant state. Extensive experiments conducted on our collected cervical LN US dataset demonstrate the effectiveness of our methods on both tissue detection and cervical lymphadenopathy diagnosis. Therefore, our proposed framework can guarantee efficient diagnosis for the vaccine recipients' cervical LN, and assist doctors to discriminate between COVID-related reactive lymphadenopathy and metastatic lymphadenopathy.

Gao Yue, Fu Xiangling, Chen Yuepeng, Guo Chenyi, Wu Ji

2023-Jan

Attention mechanism, COVID-19, Cervical lymphadenopathy, Computer-aided diagnosis, Healthcare administration, Human-in-the-loop, Image classification, Multi-modal, Semantic segmentation

General General

On the improvement of heart rate prediction using the combination of singular spectrum analysis and copula-based analysis approach.

In PeerJ

In recent years, many people have been working from home due to the exceptional circumstances concerning the coronavirus disease 2019 (COVID-19) pandemic. It has also negatively influenced general health and quality of life. Therefore, physical activity has been gaining much attention in preventing the spread of Severe Acute Respiratory Syndrome Coronavirus. For planning an effective physical activity for different clients, physical activity intensity and load degree needs to be appropriately adjusted depending on the individual's physical/health conditions. Heart rate (HR) is one of the most critical health indicators for monitoring exercise intensity and load degree because it is closely related to the heart rate. Heart rate prediction estimates the heart rate at the next moment based on now and other influencing factors. Therefore, an accurate short-term HR prediction technique can deliver efficient early warning for human health and decrease the happening of harmful events. The work described in this article aims to introduce a novel hybrid approach to model and predict the heart rate dynamics for different exercises. The results indicate that the combination of singular spectrum analysis (SSA) and the Clayton Copula model can accurately predict HR for the short term.

Namazi Asieh

2022

Artificial intelligence, Heart rate, Machine learning, Prediction, Wearable sensors

General General

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.

In Journal of biomedical physics & engineering

BACKGROUND : Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA).

OBJECTIVE : This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality.

MATERIAL AND METHODS : In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models.

RESULTS : A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively.

CONCLUSION : The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.

Afrash Mohammad Reza, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi

2022-Dec

** Artificial Intelligence, Coronavirus (COVID-19), Data Mining, Machine Learning, Mortality**

Internal Medicine Internal Medicine

Prediction of COVID-19 Patients' Survival by Deep Learning Approaches.

In Medical journal of the Islamic Republic of Iran

Background: Despite many studies done to predict severe coronavirus 2019 (COVID-19) patients, there is no applicable clinical prediction model to predict and distinguish severe patients early. Based on laboratory and demographic data, we have developed and validated a deep learning model to predict survival and assist in the triage of COVID-19 patients in the early stages. Methods: This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data. The database consisted of data from 487 patients with COVID-19 diagnosed by the reverse transcription-polymerase chain reaction test and admitted to Imam Khomeini hospital affiliated to Tehran University of Medical Sciences from February 21, 2020, to June 24, 2020. Results: The developed model achieved an area under the curve (AUC) of 0.96 for survival prediction. The results demonstrated the developed model provided high precision (0.95, 0.93), recall (0.90,0.97), and F1-score (0.93,0.95) for low- and high-risk groups. Conclusion: The developed model is a deep learning-based, data-driven prediction tool that can predict the survival of COVID-19 patients with an AUC of 0.96. This model helps classify admitted patients into low-risk and high-risk groups and helps triage patients in the early stages.

Taheriyan Moloud, Ayyoubzadeh Seyed Mehdi, Ebrahimi Mehdi, R Niakan Kalhori Sharareh, Abooei Amir Hossien, Gholamzadeh Marsa, Ayyoubzadeh Seyed Mohammad

2022

COVID-19, Deep Learning, Prediction, Survival Analysis, Triage

General General

On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19.

In European journal of operational research

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with low accuracy, and it may be too complex to understand and explain. This paper proposes and studies a novel Mathematical Optimization model to build a sparse ensemble, which trades off the accuracy of the ensemble and the number of base regressors used. The latter is controlled by means of a regularization term that penalizes regressors with a poor individual performance. Our approach is flexible to incorporate desirable properties one may have on the ensemble, such as controlling the performance of the ensemble in critical groups of records, or the costs associated with the base regressors involved in the ensemble. We illustrate our approach with real data sets arising in the COVID-19 context.

Benítez-Peña Sandra, Carrizosa Emilio, Guerrero Vanesa, Jiménez-Gamero M Dolores, Martín-Barragán Belén, Molero-Río Cristina, Ramírez-Cobo Pepa, Romero Morales Dolores, Sillero-Denamiel M Remedios

2021-Dec-01

COVID-19, Ensemble Method, Machine Learning, Mathematical Optimization, Selective Sparsity

General General

Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations.

In Information processing & management

This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.

Blanco Guillermo, Lourenço Anália

2022-May

Conversation, Covid-19 pandemic, Emotion classification, Emotion shift, Sociome

General General

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system.

In Journal of cloud computing (Heidelberg, Germany)

In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the "Ad-hoc Cloud System" idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

Mawgoud Ahmed A, Taha Mohamed Hamed N, Abu-Talleb Amr, Kotb Amira

2022

Ad-hoc system, Cloud computing, Cloud security, Deep learning, Encryption, Steganography

General General

Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire.

In Briefings in bioinformatics

Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.

Zhao Yu, He Bing, Xu Zhimeng, Zhang Yidan, Zhao Xuan, Huang Zhi-An, Yang Fan, Wang Liang, Duan Lei, Song Jiangning, Yao Jianhua

2022-Dec-24

COVID-19, TCR repertoire, artificial intelligence, diagnosis, prognosis

General General

New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.

In Archives of gynecology and obstetrics ; h5-index 44.0

Preeclampsia, a multisystem disorder in pregnancy, is still one of the main causes of maternal morbidity and mortality. Due to a lack of a causative therapy, an accurate prediction of women at risk for the disease and its associated adverse outcomes is of utmost importance to tailor care. In the past two decades, there have been successful improvements in screening as well as in the prediction of the disease in high-risk women. This is due to, among other things, the introduction of biomarkers such as the sFlt-1/PlGF ratio. Recently, the traditional definition of preeclampsia has been expanded based on new insights into the pathophysiology and conclusive evidence on the ability of angiogenic biomarkers to improve detection of preeclampsia-associated maternal and fetal adverse events.However, with the widespread availability of digital solutions, such as decision support algorithms and remote monitoring devices, a chance for a further improvement of care arises. Two lines of research and application are promising: First, on the patient side, home monitoring has the potential to transform the traditional care pathway. The importance of the ability to input and access data remotely is a key learning from the COVID-19 pandemic. Second, on the physician side, machine-learning-based decision support algorithms have been shown to improve precision in clinical decision-making. The integration of signals from patient-side remote monitoring devices into predictive algorithms that power physician-side decision support tools offers a chance to further improve care.The purpose of this review is to summarize the recent advances in prediction, diagnosis and monitoring of preeclampsia and its associated adverse outcomes. We will review the potential impact of the ability to access to clinical data via remote monitoring. In the combination of advanced, machine learning-based risk calculation and remote monitoring lies an unused potential that allows for a truly patient-centered care.

Hackelöer Max, Schmidt Leon, Verlohren Stefan

2022-Dec-25

Angiogenic factors, Artificial intelligence, Decision trees, Hypertensive pregnancy disorders, Machine learning, Multivariable modeling, Preeclampsia, Remote monitoring

General General

Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak.

In Bulletin of mathematical biology

Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions' predictions.

Assad Daniel Bouzon Nagem, Cara Javier, Ortega-Mier Miguel

2022-Dec-24

COVID-19, Forecasting, Multivariate approach, Outbreak disease, Univariate approach

General General

Measuring daily-life fear perception change: A computational study in the context of COVID-19.

In PloS one ; h5-index 176.0

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

Chai Yuchen, Palacios Juan, Wang Jianghao, Fan Yichun, Zheng Siqi

2022

General General

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.

In PloS one ; h5-index 176.0

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

Sailunaz Kashfia, Bestepe Deniz, Özyer Tansel, Rokne Jon, Alhajj Reda

2022

General General

The effects of department of Veterans Affairs medical centers on socio-economic outcomes: Evidence from the Paycheck Protection Program.

In PloS one ; h5-index 176.0

Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck Protection Program (PPP) between April and June as a source of variation. First, we find that Veterans received 3.5% more loans and 6.8% larger loans than their counterparts (p < 0.01), controlling for a wide array of zipcode characteristics. Second, we develop models to predict the number of PPP loans awarded to Veterans, finding that the inclusion of local VA medical center characteristics adds almost as much explanatory power as the industry and occupational composition in an area and even more than the education, race, and age distribution combined. Our results suggest that VA medical centers can play an important role in helping Veterans thrive even beyond addressing their direct medical needs.

Makridis Christos A, Kelly J D, Alterovitz Gil

2022

General General

Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model.

In Journal of imaging

The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.

Phung Kim Anh, Nguyen Thuan Trong, Wangad Nileshkumar, Baraheem Samah, Vo Nguyen D, Nguyen Khang

2022-Dec-05

disease recognition, doctor consultation-inspired, medical image processing

General General

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms.

In Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion

BACKGROUND : The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals.

OBJECTIVES : To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university.

METHODS : A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19.

RESULTS : The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables.

CONCLUSIONS : ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Villagrana-Bañuelos Karen E, Maeda-Gutiérrez Valeria, Alcalá-Rmz Vanessa, Oropeza-Valdez Juan J, Herrera-Van Oostdam Ana S, Castañeda-Delgado Julio E, López Jesús Adrián, Borrego Moreno Juan C, Galván-Tejada Carlos E, Galván-Tejeda Jorge I, Gamboa-Rosales Hamurabi, Luna-García Huizilopoztli, Celaya-Padilla José M, López-Hernández Yamilé

2022

Biomarker, COVID-19, Genetic algorithm, LC-MS, Machine learning, Metabolomics, Random forest

General General

The role of advanced technologies against COVID-19: Prevention, Detection, and Treatments.

In Current stem cell research & therapy

Concurrent with the global outbreak of COVID-19, the race began among scientists to generate effective therapeutics for the treatment of COVID-19. In this regard, advanced technology such as nanotechnology, cell-based therapies, tissue engineering and regenerative medicine, nerve stimulation and artificial intelligence (AI) are attractive because they can offer new solutions for the prevention, diagnosis and treatment of COVID-19. Nanotechnology can design rapid and specific tests with high sensitivity for detecting infection and synthases new drugs and vaccines based on nanomaterials to directly deliver the intended antiviral agent to the desired site in the body and also provide new surfaces that do not allow virus adhesion. Mesenchymal stem cells and exosomes secreted from them apply in regenerative medicine and regulate inflammatory responses. Cell therapy and tissue engineering are combined to repair or substitute damaged tissues or cells. Tissue engineering using biomaterials, cells, and signaling molecules can develop new therapeutic and diagnostic platforms and help scientists fight viral diseases. Nerve stimulation technology can augment body's natural ability to modulate the inflammatory response and inhibit pro-inflammatory cytokines and consequently suppress cytokine storm. People can access free online health counseling services through AI and it helps very fast for screening and diagnosis of COVID-19 patients. This study is aimed first to give brief information about COVID-19 and the epidemiology of the disease. After that, we highlight important developments in the field of advanced technologies relevant to the prevention, detection, and treatment of the current pandemic.

Hasanzadeh Elham, Rafati Adele, Tamijani Seyedeh Masoumeh Seyed Hosseini, Rafaiee Raheleh, Golchin Ali, Abasi Mozhgan

2022-Dec-21

Artificial intelligence, COVID-19, Nanotechnology, Nerve stimulation, Stem cell therapy, Tissue engineering

General General

Physical fitness changes in adolescents due to social distancing during the coronavirus disease pandemic in Korea.

In PeerJ

BACKGROUND : At least 60 min of moderate-intensity physical activity per day is recommended for physical and mental health of adolescents. Schools are one of the most suitable places for promoting students' health as it is a place where vigorous physical activity occurs. However, the physical activity of students is threatened because schools are closed worldwide owing to the coronavirus disease (COVID-19) outbreak in 2019. Therefore, this study aimed to analyze the physical fitness changes in 27,782 Korean adolescents during the pandemic and present alternative education and health policies to the school.

METHODS : We included 29,882 middle school students (age: 13-15 years; males: 14,941, females: 12,841) in Korea from 2019 to 2021 . Participants' physical fitness at school was measured using the physical activity promotion system (PAPS) manual developed to measure students' physical fitness. Physical fitness variables included body mass index (BMI), 20 m shuttle run, handgrip strength, sit-and-reach, and 50 m run.

RESULTS : The COVID-19 pandemic has had a negative impact on the BMI and cardiorespiratory endurance of Korean middle school students. Specifically, male students' BMI increased, while body composition, cardiorespiratory endurance, and grip strength decreased significantly. Female students showed significant decreases in BMI and sit-and-reach test scores. It is clear that the physical fitness level of adolescents decreased by a greater degree after the COVID-19 pandemic than before, and the decrease in the physical fitness level of male students was noticeable. Therefore, a lesson strategy should be prepared that considers the contents and methods of physical education classes to improve the physical fitness level of male and female adolescents.

CONCLUSIONS : Fitness-based classes suitable for online methods should be urgently added as alternative physical education classes to prepare for the second COVID-19 outbreak. In addition, it is necessary to create an environment in which physical activity is a possibility in physical education classes, in any situation using artificial intelligence and virtual reality.

Lee Kwang-Jin, Seon Se-Young, Noh Byungjoo, An Keun-Ok

2022

Middle school students, Physical activity, Physical fitness level

Public Health Public Health

Mediating Role of Fine Particles Abatement on Pediatric Respiratory Health During COVID-19 Stay-at-Home Order in San Diego County, California.

In GeoHealth

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID-19 stay-at-home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay-at-home order, we quantified the specific contribution of fine-scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n = 72,333) at the ZIP-code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015-2020) and ZIP-code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay-at-home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay-at-home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4-year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay-at-home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits.

Aguilera Rosana, Leibel Sydney, Corringham Thomas, Bialostozky Mario, Nguyen Margaret B, Gershunov Alexander, Benmarhnia Tarik

2022-Sep

General General

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization.

In Frontiers in microbiology

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

Wang Yibai, Xiang Ju, Liu Cuicui, Tang Min, Hou Rui, Bao Meihua, Tian Geng, He Jianjun, He Binsheng

2022

SARS-CoV-2, bilinear matrix factorization, drug repositioning, machine learning, molecular docking

General General

Artificial Neural Networks for the Prediction of Monkeypox Outbreak.

In Tropical medicine and infectious disease

While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg-Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors' knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries.

Manohar Balakrishnama, Das Raja

2022-Dec-08

COVID-19, Hessian matrix, K-fold cross-validation, Levenberg–Marquardt model, machine learning, regression analysis, sigmoid function

General General

Artificial Neural Networks for the Prediction of Monkeypox Outbreak.

In Tropical medicine and infectious disease

While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg-Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors' knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries.

Manohar Balakrishnama, Das Raja

2022-Dec-08

COVID-19, Hessian matrix, K-fold cross-validation, Levenberg–Marquardt model, machine learning, regression analysis, sigmoid function

General General

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach.

In Environmental monitoring and assessment

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

Dutta Debashree, Pal Sankar K

2022-Dec-22

Air quality, Bidirectional LSTM, COVID-19, Deep learning, Lockdown, PM10, PM2.5, Random forest, Transfer learning

General General

Maimuna (Maia) Majumder.

In Cell reports. Medicine

Maimuna Majumder (she/they) is an assistant professor in the Computational Health Informatics Program at Harvard Medical School and Boston Children's Hospital. Her team has been engaged in COVID-19 response efforts since January 2020. Here, she discusses the role of artificial intelligence in pandemic-related research and computational epidemiology as a field.

**

2022-Dec-20

General General

Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation.

In Computers in biology and medicine

The COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improve the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. The achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset. Source code is available at https://github.com/YujiaKCL/COVID-CT-Similarity-Regularization.

Xu Yujia, Lam Hak-Keung, Jia Guangyu, Jiang Jian, Liao Junkai, Bao Xinqi

2022-Dec-15

CNNs, COVID-19, Computed tomography, Deep learning, Similarity regularization

General General

Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis.

In Applied animal behaviour science ; h5-index 32.0

Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10-23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8 am to 8 pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2 = 17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.

Byosiere Sarah-Elizabeth, Feighelstein Marcelo, Wilson Kristiina, Abrams Jennifer, Elad Guy, Farhat Nareed, van der Linden Dirk, Kaplun Dmitrii, Sinitca Aleksandr, Zamansky Anna

2022-May

Applied behavior, COVID-19, Computer vision, Dog behavior, Machine learning, Shelter research

General General

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening.

In Scientific reports ; h5-index 158.0

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

Xiong Hao, Berkovsky Shlomo, Kâafar Mohamed Ali, Jaffe Adam, Coiera Enrico, Sharan Roneel V

2022-Dec-20

General General

Dementia Analytics Research User Group (DARUG) - A Model for meaningful stakeholder engagement in dementia research.

In Alzheimer's & dementia : the journal of the Alzheimer's Association

BACKGROUND : The importance of involving stakeholders in research is widely recognised but few studies provide details to implementation in practice. The use of real-time technology involving patients, carers and professionals in project design, monitoring, delivery and reporting could maximise contribution. Stakeholder engagement was included as part of a Dementia Analytics Research User Group project which applied machine learning to the Trinity-Ulster-Department of Agriculture (TUDA) data set, identifying clinical and lifestyle factors associated with cognitive health in 5000 community-dwelling older Irish adults.

METHOD : An innovative model for engagement (ENGAGE) was used1 - a methodological and technology platform, that gains insight into group thinking and consensus. Developed by Ulster University, this produces a report in real time for sharing to all stakeholders, thus ensuring active involvement in defining the research question. Using a Personal and Public Involvement (PPI) approach, representatives from patient and carer groups (including TUDA participants ), charities (e.g., Alzheimer's UK), as well as professionals, were invited to attend one of three scoping events. Each event commenced with an overview by the project team of the value of data analytics and initial data analysis. The PPI groups were then invited to answer specific questions relating to risk factors for dementia and were asked to articulate their expectations on the potential outcomes from the project. These responses were analysed using ENGAGE and word clouds generated for discussion to help refine the project going forward.

RESULTS : Participants (n=87) Lifestyle, Genetics, Stress and were the dominant emerging themes for risk factors of dementia. Prevention, Help and Information/ Research emerged as strong themes, with the mind maps showing stimulus, understanding and awareness as key outputs of this project. The outcomes of this engagement model were utilised to successfully inform the subsequent data analytics portion of the study2 .

CONCLUSION : The model performed well, capturing discussions in real time. Feedback was positive and helped to focus and inform the research team's thinking. What was not so successful was the longer-term inclusion in the project, with engagement through remote channels tending to drift over time, somewhat exacerbated by COVID 19 restrictions. The team aim to follow up on this aspect.

Carlin Paul, Wallace Jonathan, Moore Adrian, Hughes Catherine, Black Michaela, Rankin Deborah, Hoey Leane, McNulty Helene

2022-Dec

General General

Drug development process and COVID-19 pandemic: Flourishing era of outsourcing.

In Indian journal of pharmacology

Traditional drug development is a tedious process with involvement of enormous cost and a high attrition rate. Outsourcing drug development services to contract research organizations (CROs) has become an important strategy for cost and risk reduction, capacity building, and data generation. The therapeutic and operational expertise of these CROs has allowed pharmaceutical industry to reduce in-house infrastructure as well as research capacity. Working with specialized CROs has not only increased the rate of success but also the speed of drug discovery process. Small firms with promising molecules but limited resources and large firms interested in diversifying their dimensions are utilizing the services of efficient CROs. Globally, approximately one-third of the drug development processes are now being outsourced and the data generated by the independent third party are well appreciated during regulatory submissions. In this article, we discuss the international and national trends, outsourcing services and models, key considerations while selecting CRO, and benefits and challenges of outsourcing. Further, we discuss how the technical expertise of competent CROs was utilized when traditional ways of conducting clinical trials were disrupted by the COVID-19 pandemic. Taken together, the increasing health-care demands, COVID-19 pandemic or any other such upcoming health crisis, and recent advances in advanced technologies (machine learning and artificial intelligence, etc.) are likely to fuel global CRO market in the coming years.

Wasan Himika, Singh Devendra, Reeta K H, Gupta Pooja, Gupta Yogendra Kumar

2022

Artificial intelligence, COVID-19, contract research organizations, drug development, outsourcing

General General

D-Cov19Net: A DNN based COVID-19 detection system using lung sound.

In Journal of computational science

The limitations of proper detectors for COVID-19 for the proliferating number of patients provoked us to build an auto-diagnosis system to detect COVID-19 infection using only one parameter. Our designed model is based on Deep Convolution Neural Network and considers lung/respiratory sound as the deterministic input for our approach. 'D-Cov19Net' has been trained with 23,592 recordings, begetting an AUC of 0.972 and sensitivity of 0.983 after 100 epochs. The model can be of immense utility in biomedical technology due to its significant accuracy, simplicity, user convenience, feasibility, and faster detection while maintaining social distancing.

Chatterjee Sukanya, Roychowdhury Jishnu, Dey Anilesh

2022-Dec-15

Auto-diagnosis system, COVID-19 Detection, Convolution Neural Network (CNN), Deep Learning, Lung/Respiratory sound

General General

A residual network-based framework for COVID-19 detection from CXR images.

In Neural computing & applications

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a large population. Polymerase chain reaction is currently being utilized to diagnose COVID-19 in suspected patients; however, its sensitivity is quite low. The researchers also developed automated approaches for reliably and timely identifying COVID-19 from X-ray images. However, traditional machine learning-based image classification algorithms necessitate manual image segmentation and feature extraction, which is a time-consuming task. Due to promising results and robust performance, Convolutional Neural Network (CNN)-based techniques are being used widely to classify COVID-19 from Chest X-rays (CXR). This study explores CNN-based COVID-19 classification methods. A series of experiments aimed at COVID-19 detection and classification validates the viability of our proposed framework. Initially, the dataset is preprocessed and then fed into two Residual Network (ResNet) architectures for deep feature extraction, such as ResNet18 and ResNet50, whereas support vector machines with its multiple kernels, including Quadratic, Linear, Gaussian and Cubic, are used to classify these features. The experimental results suggest that the proposed framework efficiently detects COVID-19 from CXR images. The proposed framework obtained the best accuracy of 97.3% using ResNet50.

Kibriya Hareem, Amin Rashid

2022-Dec-15

COVID-19, CXR images, Machine learning, ResNet50, SVM

General General

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks.

In Computer networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE (Transferable Traffic Prediction in MUlti-Service Edge Networks), a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning.

Xu Luyang, Liu Haoyu, Song Junping, Li Rui, Hu Yahui, Zhou Xu, Patras Paul

2023-Feb

Edge model transfer, Multi-service traffic prediction, Service clustering

General General

A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction.

In Pattern recognition

With the outbreak and wide spread of novel coronavirus (COVID-19), contactless fingerprint recognition has attracted more attention for personal recognition because it can provide significantly higher user convenience and hygiene than the traditional contact-based fingerprint recognition. However, it is still challenging to achieve a highly accurate recognition due to the low ridge-valley contrast and pose variances of contactless fingerprints. Minutiae points are a kind of ridge flow discontinuities, and robust and accurate extraction is an important step for most automatic fingerprint recognition algorithms. Most of existing methods are based on two stages which locate the minutiae points first and then compute their directions. The two-stage method cannot make full use of location and direction information. In this paper, we propose a multi-task fully deep convolutional neural network for jointly learning the minutiae location detection and its corresponding direction computation which operates directly on the whole gray scale contactless fingerprints. The proposed method consists of offline training and online testing stages. In the training stage, a fully deep convolutional neural network is built for the tasks of minutiae detection and its direction regression, with an attention mechanism to make the direction regression branch concentrate on the minutiae points. A new loss function is proposed to jointly learn the tasks of minutiae detection and its direction regression from the whole fingerprints. In the testing stage, the trained network is applied on the whole contactless fingerprint to generate the minutiae location and direction maps. The proposed multi-task leaning method performs better than the individual single task and it operates directly on the raw gray-scale contactless fingerprints without preprocessing. The results on three contactless fingerprint datasets show the proposed algorithm performs better than other minutiae extraction algorithms and the commercial software.

Zhang Zhao, Liu Shuxin, Liu Manhua

2021-Dec

00-01, 99-00, Contactless fingerprint, Deep convolutional neural network, Minutiae extraction, Multi-task learning

General General

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

bioRxiv Preprint

The success of the human body in fighting SARS-CoV-2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV-2 compared with uninfected controls. In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

Safra, M.; Tamari, Z.; Polak, P.; Shiber, S.; Matan, M.; Karameh, H.; Helviz, Y.; Levy-Barda, A.; Yahalom, V.; Peretz, A.; Ben-Chetrit, E.; Brenner, B.; Tuller, T.; Gal-Tanamy, M.; Yaari, G.

2022-12-21

Internal Medicine Internal Medicine

Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data.

In Frontiers in medicine

BACKGROUND : When facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19.

MATERIALS AND METHODS : We enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission.

RESULTS : The model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care.

CONCLUSION : In clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.

Muto Reiko, Fukuta Shigeki, Watanabe Tetsuo, Shindo Yuichiro, Kanemitsu Yoshihiro, Kajikawa Shigehisa, Yonezawa Toshiyuki, Inoue Takahiro, Ichihashi Takuji, Shiratori Yoshimune, Maruyama Shoichi

2022

COVID-19, artificial intelligence-human collaboration, clinical practice, oxygen needs, sodium chloride difference

Public Health Public Health

An NLP tool for data extraction from electronic health records: COVID-19 mortalities and comorbidities.

In Frontiers in public health

BACKGROUND : The high infection rate, severe symptoms, and evolving aspects of the COVID-19 pandemic provide challenges for a variety of medical systems around the world. Automatic information retrieval from unstructured text is greatly aided by Natural Language Processing (NLP), the primary approach taken in this field. This study addresses COVID-19 mortality data from the intensive care unit (ICU) in Kuwait during the first 18 months of the pandemic. A key goal is to extract and classify the primary and intermediate causes of death from electronic health records (EHRs) in a timely way. In addition, comorbid conditions or concurrent diseases were retrieved and analyzed in relation to a variety of causes of mortality.

METHOD : An NLP system using the Python programming language is constructed to automate the process of extracting primary and secondary causes of death, as well as comorbidities. The system is capable of handling inaccurate and messy data, this includes inadequate formats, spelling mistakes and mispositioned information. A machine learning decision trees method is used to classify the causes of death.

RESULTS : For 54.8% of the 1691 ICU patients we studied, septic shock or sepsis-related multiorgan failure was the leading cause of mortality. About three-quarters of patients die from acute respiratory distress syndrome (ARDS), a common intermediate cause of death. An arrhythmia (AF) disorder was determined to be the strongest predictor of intermediate cause of death, whether caused by ARDS or other causes.

CONCLUSION : We created an NLP system to automate the extraction of causes of death and comorbidities from EHRs. Our method processes messy and erroneous data and classifies the primary and intermediate causes of death of COVID-19 patients. We advocate arranging the EHR with well-defined sections and menu-driven options to reduce incorrect forms.

BuHamra Sana S, Almutairi Abdullah N, Buhamrah Abdullah K, Almadani Sabah H, Alibrahim Yusuf A

2022

SARS-CoV-2, decision tree, information extraction, mortality, natural language processing, prediction, text mining

Public Health Public Health

Can a chatbot enhance hazard awareness in the construction industry?

In Frontiers in public health

Safety training enhances hazard awareness in the construction industry. Its effectiveness is a component of occupational safety and health. While face-to-face safety training has dominated in the past, the frequent lockdowns during COVID-19 have led us to rethink new solutions. A chatbot is messaging software that allows people to interact, obtain answers, and handle sales and inquiries through a computer algorithm. While chatbots have been used for language education, no study has investigated their usefulness for hazard awareness enhancement after chatbot training. In this regard, we developed four Telegram chatbots for construction safety training and designed the experiment as the treatment factor. Previous researchers utilized eye-tracking in the laboratory for construction safety research; most have adopted it for qualitative analyses such as heat maps or gaze plots to study visual paths or search strategies via eye-trackers, which only studied the impact of one factor. Our research has utilized an artificial intelligence-based eye-tracking tool. As hazard awareness can be affected by several factors, we filled this research void using 2-way interaction terms using the design of experiment (DOE) model. We designed an eye-tracking experiment to study the impact of site experience, Telegram chatbot safety training, and task complexity on hazard awareness, which is the first of its kind. The results showed that Telegram chatbot training enhanced the hazard awareness of participants with less onsite experience and in less complex scenarios. Low-cost chatbot safety training could improve site workers' danger awareness, but the design needs to be adjusted according to participants' experience. Our results offer insights to construction safety managers in safety knowledge sharing and safety training.

Zhu Xiaoe, Li Rita Yi Man, Crabbe M James C, Sukpascharoen Khunanan

2022

chatbot safety training, construction hazard awareness, construction practitioners, design of experiment, eye-tracking

General General

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients.

In PeerJ

BACKGROUND : The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients.

METHODS : The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings.

RESULTS : We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained.

CONCLUSIONS : In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

Martinez Gustavo, Garduno Alexis, Mahmud-Al-Rafat Abdullah, Ostadgavahi Ali Toloue, Avery Ann, de Avila E Silva Scheila, Cusack Rachael, Cameron Cheryl, Cameron Mark, Martin-Loeches Ignacio, Kelvin David

2022

Artificial Neural Networks, Biomarkers, Classification, Deep learning, Immunology

General General

COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.

In Chaos, solitons, and fractals

Many severe epidemics and pandemics have hit human civilizations throughout history. The recent Sever Actuate Respiratory disease SARS-CoV-2 known as COVID-19 became a global disease and is still growing around the globe. It has severely affected the world's economy and ways of life. It necessitates predicting the spread in advance and considering various control policies to avoid the country's complete closure. In this paper, we propose deep learning-based stacked Bi-directional long short-term memory (Stacked Bi-LSTM) network that forecasts COVID-19 more accurately for the country of South Korea. The paper's main objectives are to present a lightweight, accurate, and optimized model to predict the spread considering restriction policies such as school closure, workspace closing, and the canceling of public events. Based on the fourteen parameters (including control policies), we predict and forecast the future value of the number of positive, dead, recovered, and quarantined cases. In this paper, we use the dataset of South Korea comprised of several control policies implemented for minimizing the spread of COVID-19. We compare the performance of the stacked Bi-LSTM with the traditional time-series models and LSTM model using the performance metrics mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, we study the impact of control policies on forecasting accuracy. We further study the impact of changing the Bi-LSTM default activation functions Tanh with ReLU on forecasting accuracy. The research provides insight to policymakers to optimize the pooling of resources more optimally on the correct date and time prior to the event and to control the spread by employing various strategies in the meantime.

Ali Furqan, Ullah Farman, Khan Junaid Iqbal, Khan Jebran, Sardar Abdul Wasay, Lee Sungchang

2022-Dec-13

COVID-19, Deep Learning, Forecasting, Long short-term memory, Pandemic, Stacked Bi-LSTM, Time series

Public Health Public Health

Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death.

In Frontiers in artificial intelligence

The rapid spread of COVID-19 and its variants have devastated communities worldwide, and as the highly transmissible Omicron variant becomes the dominant strain of the virus in late 2021, the need to characterize and understand the difference between the new variant and its predecessors has been an increasing priority for public health authorities. Artificial Intelligence has played a significant role in the analysis of various facets of COVID-19 since the early stages of the pandemic. This study proposes the use of AI, specifically an XGBoost model, to quantify the impact of various medical risk factors (or "population features") on the possibility of a patient outcome resulting in hospitalization, ICU admission, or death. The results are compared between the Delta and Omicron COVID-19 variants. Results indicated that older age and an unvaccinated patient status most consistently correspond as the most significant population features contributing to all three scenarios (hospitalization, ICU, death). The top 15 features for each variant-outcome scenario were determined, which most frequently included diabetes, cardiovascular disease, chronic kidney disease, and complications of pneumonia as highly significant population features contributing to serious illness outcomes. The Delta/Hospitalization model returned the highest performance metric scores for the area under the receiver operating characteristic (AUROC), F1, and Recall, while Omicron/ICU and Omicron/Hospitalization had the highest accuracy and precision values, respectively. The recall was found to be above 0.60 in most cases (with only two exceptions), indicating that the total number of false positives was generally minimized (accounting for more of the people who would theoretically require medical care).

Hilal Waleed, Chislett Michael G, Snider Brett, McBean Edward A, Yawney John, Gadsden S Andrew

2022

COVID-19, Delta variant, Omicron variant, XGBoost, healthcare, hospitalization, medical risk factors

General General

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.

In Frontiers in artificial intelligence

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Shen John, Ghatti Siddharth, Levkov Nate Ryan, Shen Haiying, Sen Tanmoy, Rheuban Karen, Enfield Kyle, Facteau Nikki Reyer, Engel Gina, Dowdell Kim

2022

AI, COVID-19 detection, IoT, mobile devices, telemedicine

General General

COVID-19 and human development: An approach for classification of HDI with deep CNN.

In Biomedical signal processing and control

The measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Convolutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).

Kavuran Gürkan, Gökhan Şeyma, Yeroğlu Celaleddin

2023-Mar

Artificial intelligence, COVID-19, Classification, Continuous wavelet transform, Deep learning, Human Development Index

General General

The design of compounds with desirable properties - The anti-HIV case study.

In Journal of computational chemistry

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.

Novak Jurica, Pathak Prateek, Grishina Maria A, Potemkin Vladimir A

2022-Dec-19

3CLpro, HIV-1 protease, QSAR, cytotoxicity, drug repurposing

General General

Does media sentiment affect stock prices? Evidence from China's STAR market.

In Frontiers in psychology ; h5-index 92.0

OBJECTIVE : This paper explores the impact of media sentiment on stock prices on the Shanghai Stock Exchange Science and Technology Innovation Board (hereinafter the STAR market) from a behavioral finance perspective.

METHODS : We collect Baidu News coverage of STAR-listed firms as the text, and measure text sentiment using a machine learning-based text analysis technique. We then empirically examine the impact of media sentiment on STAR market stock prices from two aspects: IPO pricing efficiency and IPO first-day stock performance.

RESULTS : (1) Media sentiment has no significant impact on IPO pricing efficiency, thus suggesting that institutional investors participating in such offerings are generally not affected by media sentiment. (2) Optimistic media sentiment has a positive impact on IPO first-day returns, which indicates that individual investors are more easily influenced by media sentiment and therefore likely to abandon their rational judgment. (3) Media sentiment had a greater impact on IPO first-day returns during the COVID-19 pandemic than those before it, which suggests that individual investors are more influenced by media sentiment during pandemics.

DISCUSSION : Our findings deepen the understanding of stock price formation on the STAR market, which provide a statistical basis for formulating policy directions and investment strategies.

Dong Xiuliang, Xu Shiying, Liu Jianing, Tsai Fu-Sheng

2022

IPO first-day stock performance, IPO pricing efficiency, machine learning, media sentiment, stock price, the STAR market

General General

Research on the state of blended learning among college students - A mixed-method approach.

In Frontiers in psychology ; h5-index 92.0

In the wake of the COVID-19 pandemic in 2019, China's education leaders began to focus on and promote blended learning. The process is still in its infancy in Chinese colleges and universities, and its development remains a problem to be solved. By combining technology acceptance and student participation, this article proposes an analysis model for assessing the factors influencing blended learning. A questionnaire was designed and distributed, and 796 valid responses were collected. The mean and variance were used to examine the status of students' technology acceptance and satisfaction with blended learning. The t-test method was employed to analyze the gender differences between students in regard to the topic. The results show that: (1) students majoring in computer science view the factors as having a high level of influence in blended learning. (2) There are major variances regarding the perception of service quality between male and female computer science major students. There is no significant difference between them in terms of perceived usefulness, perceived ease of use, or computer self-efficacy. (3) There are considerable disparities in the skill involvement and participation of computer science major college students. The results show that the technology acceptance and participation of students determine the effect of blended learning. Based on these findings, this article provides theoretical and practical suggestions for the implementation of blended learning to improve its effect.

Deng Chao, Peng Jiao, Li ShuFei

2022

TAM 3, blended learning, learner engagement theory, student engagement, technology acceptance

General General

Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.

In Information sciences

Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.

Mahbub Md Kawsher, Biswas Milon, Gaur Loveleen, Alenezi Fayadh, Santosh K C

2022-May

ACC, Accuracy, AI, Artificial Intelligence, AUC, Area Under the Curve, CADx, Computer-Aided Diagnosis, CNN, Convolutional Neural Network, CT, Computed Tomography, CXR, Chest X-ray, Chest X-ray, Covid-19, DL, Deep Learning, DNN, DNN, Deep Neural Network, Infectious DiseaseX, ML, Machine Learning, MTB, Mycobacterium Tuberculosis, Medical imaging, NN, Neural Network, Pneumonia, SEN, Sensitivity, SPEC, Specificity, TB, Tuberculosis, Tuberculosis, WHO, World Health Organization

Public Health Public Health

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests.

In PeerJ. Computer science

Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.

Nguyen Hieu, Gokhale Swapna

2022

Anti-government, COVID-19, Lockdown protests, Machine Learning, Social media

General General

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations.

In PeerJ. Computer science

Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for instance, is crucial for the success of the global vaccination programs and thus achieving herd immunity. To combat the proliferation of COVID-19-related misinformation, considerable research efforts have been and are still being dedicated to building and sharing COVID-19 misinformation detection datasets and models for Arabic and other languages. However, most of these datasets provide binary (true/false) misinformation classifications. Besides, the few studies that support multi-class misinformation classification deal with a small set of misinformation classes or mix them with situational information classes. False news stories about COVID-19 are not equal; some tend to have more sinister effects than others (e.g., fake cures and false vaccine info). This suggests that identifying the sub-type of misinformation is critical for choosing the suitable action based on their level of seriousness, ranging from assigning warning labels to the susceptible post to removing the misleading post instantly. We develop comprehensive annotation guidelines in this work that define 19 fine-grained misinformation classes. Then, we release the first Arabic COVID-19-related misinformation dataset comprising about 6.7K tweets with multi-class and multi-label misinformation annotations. In addition, we release a version of the dataset to be the first Twitter Arabic dataset annotated exclusively with six different situational information classes. Identifying situational information (e.g., caution, help-seeking) helps authorities or individuals understand the situation during emergencies. To confirm the validity of the collected data, we define three classification tasks and experiment with various machine learning and transformer-based classifiers to offer baseline results for future research. The experimental results indicate the quality and validity of the data and its suitability for constructing misinformation and situational information classification models. The results also demonstrate the superiority of AraBERT-COV19, a transformer-based model pretrained on COVID-19-related tweets, with micro-averaged F-scores of 81.6% and 78.8% for the multi-class misinformation and situational information classification tasks, respectively. Label Powerset with linear SVC achieved the best performance among the presented methods for multi-label misinformation classification with micro-averaged F-scores of 76.69%.

Obeidat Rasha, Gharaibeh Maram, Abdullah Malak, Alharahsheh Yara

2022

BERT, COVID-19, Data annotation, Data collection, Deep learning, Fake news, Machine learning, Misinformation detection, Situational information, Transformers

General General

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In SN computer science

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

Kumar Yogesh, Koul Apeksha, Kaur Sukhpreet, Hu Yu-Chen

2023

COVID-19, Facebook Prophet, Holt model, Prediction, RANSAC regressor, Random forest regressor, Stacked gated recurrent units, Stacked long short-term memory, XG Boost

General General

A Precise Method to Detect Post-COVID-19 Pulmonary Fibrosis Through Extreme Gradient Boosting.

In SN computer science

The association of pulmonary fibrosis with COVID-19 patients has now been adequately acknowledged and caused a significant number of mortalities around the world. As automatic disease detection has now become a crucial assistant to clinicians to obtain fast and precise results, this study proposes an architecture based on an ensemble machine learning approach to detect COVID-19-associated pulmonary fibrosis. The paper discusses Extreme Gradient Boosting (XGBoost) and its tuned hyper-parameters to optimize the performance for the prediction of severe COVID-19 patients who developed pulmonary fibrosis after 90 days of hospital discharge. A dataset comprising Electronic Health Record (EHR) and corresponding High-resolution computed tomography (HRCT) images of chest of 1175 COVID-19 patients has been considered, which involves 725 pulmonary fibrosis cases and 450 normal lung cases. The experimental results achieved an accuracy of 98%, precision of 99% and sensitivity of 99%. The proposed model is the first in literature to help clinicians in keeping a record of severe COVID-19 cases for analyzing the risk of pulmonary fibrosis through EHRs and HRCT scans, leading to less chance of life-threatening conditions.

Jha Manika, Gupta Richa, Saxena Rajiv

2023

COVID-19, Clinical decision support, Extreme gradient boosting, Machine learning, Medical diagnosis, Pulmonary fibrosis, Tree boosting

General General

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.

In Multimedia tools and applications

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

Lasker Asifuzzaman, Ghosh Mridul, Obaidullah Sk Md, Chakraborty Chandan, Roy Kaushik

2022-Dec-03

Chest radiography, Covid-19, Deep neural network, Lung diseases, Pneumonia, Stack ensemble technique

General General

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning.

In Biomedical signal processing and control

Blood Oxygen ( SpO 2 ), a key indicator of respiratory function, has received increasing attention during the COVID-19 pandemic. Clinical results show that patients with COVID-19 likely have distinct lower SpO 2 before the onset of significant symptoms. Aiming at the shortcomings of current methods for monitoring SpO 2 by face videos, this paper proposes a novel multi-model fusion method based on deep learning for SpO 2 estimation. The method includes the feature extraction network named Residuals and Coordinate Attention (RCA) and the multi-model fusion SpO 2 estimation module. The RCA network uses the residual block cascade and coordinate attention mechanism to focus on the correlation between feature channels and the location information of feature space. The multi-model fusion module includes the Color Channel Model (CCM) and the Network-Based Model(NBM). To fully use the color feature information in face videos, an image generator is constructed in the CCM to calculate SpO 2 by reconstructing the red and blue channel signals. Besides, to reduce the disturbance of other physiological signals, a novel two-part loss function is designed in the NBM. Given the complementarity of the features and models that CCM and NBM focus on, a Multi-Model Fusion Model(MMFM) is constructed. The experimental results on the PURE and VIPL-HR datasets show that three models meet the clinical requirement(the mean absolute error ⩽ 2%) and demonstrate that the multi-model fusion can fully exploit the SpO 2 features of face videos and improve the SpO 2 estimation performance. Our research achievements will facilitate applications in remote medicine and home health.

Hu Min, Wu Xia, Wang Xiaohua, Xing Yan, An Ning, Shi Piao

2023-Mar

Coordinate attention, Deep learning, Estimation, Multi-model fusion, Remote photo-plethysmography, Residual network

General General

MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.

In Information sciences

The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%.

Ding Weiping, Abdel-Basset Mohamed, Hawash Hossam, Pedrycz Witold

2023-Apr

COVID-19, Data Heterogeneity, Deep Learning, Fog Computing, Internet of Medical Things

Internal Medicine Internal Medicine

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

In Applied soft computing

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

Custode Leonardo Lucio, Mento Federico, Tursi Francesco, Smargiassi Andrea, Inchingolo Riccardo, Perrone Tiziano, Demi Libertario, Iacca Giovanni

2023-Jan

COVID-19, Decision trees, Evolutionary algorithms, Grammatical evolution, Lung ultrasound, Neuro-symbolic artificial intelligence

General General

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals.

In Frontiers in immunology ; h5-index 100.0

INTRODUCTION : Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome.

METHODS : By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals.

RESULTS : Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1β, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors.

DISCUSSION : In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Chattopadhyay Partha, Khare Kriti, Kumar Manish, Mishra Pallavi, Anand Alok, Maurya Ranjeet, Gupta Rohit, Sahni Shweta, Gupta Ayushi, Wadhwa Saruchi, Yadav Aanchal, Devi Priti, Tardalkar Kishore, Joshi Meghnad, Sethi Tavpritesh, Pandey Rajesh

2022

COVID-19, T-cell activation, bayesian network model, immune response, recovered COVID-19 individuals, single cell multi-omics

Public Health Public Health

SARS-CoV-2 induces "cytokine storm" hyperinflammatory responses in RA patients through pyroptosis.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : The coronavirus disease (COVID-19) is a pandemic disease that threatens worldwide public health, and rheumatoid arthritis (RA) is the most common autoimmune disease. COVID-19 and RA are each strong risk factors for the other, but their molecular mechanisms are unclear. This study aims to investigate the biomarkers between COVID-19 and RA from the mechanism of pyroptosis and find effective disease-targeting drugs.

METHODS : We obtained the common gene shared by COVID-19, RA (GSE55235), and pyroptosis using bioinformatics analysis and then did the principal component analysis(PCA). The Co-genes were evaluated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for functional enrichment, the protein-protein interaction (PPI) network was built by STRING, and the k-means machine learning algorithm was employed for cluster analysis. Modular analysis utilizing Cytoscape to identify hub genes, functional enrichment analysis with Metascape and GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network pharmacology analysis was performed to identify target drug-related genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub genes and construct transcription factor (TF)-hub genes and miRNA-hub genes networks by NetworkAnalyst. The Co-hub genes were validated using GSE55457 and GSE93272 to acquire the Key gene, and their efficacy was assessed using receiver operating curves (ROC); SPEED2 was then used to determine the upstream pathway. Immune cell infiltration was analyzed using CIBERSORT and validated by the HPA database. Molecular docking, molecular dynamics simulation, and molecular mechanics-generalized born surface area (MM-GBSA) were used to explore and validate drug-gene relationships through computer-aided drug design.

RESULTS : COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways(the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex, death-inducing signaling complex, regulation of interleukin production), natural immune pathways (Network map of SARS-CoV-2 signaling pathway, activation of NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm pathways (IL, nuclear factor-kappa B (NF-κB), TNF signaling pathway and regulation of cytokine-mediated signaling). Of these, CASP1 is the most involved pathway and is closely related to minocycline. YY1, hsa-mir-429, and hsa-mir-34a-5p play an important role in the expression of CASP1. Monocytes are high-caspase-1-expressing sentinel cells. Minocycline can generate a highly stable state for biochemical activity by docking closely with the active region of caspase-1.

CONCLUSIONS : Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and it may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline may counteract cytokine storm inflammation in patients with COVID-19 combined with RA by inhibiting caspase-1 expression.

Zheng Qingcong, Lin Rongjie, Chen Yuchao, Lv Qi, Zhang Jin, Zhai Jingbo, Xu Weihong, Wang Wanming

2022

COVID-19, SARS-CoV-2, caspase-1, minocycline, pyroptosis, rheumatoid arthritis

Dermatology Dermatology

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : A lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways.

METHODS : The RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification.

RESULTS : We recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible.

CONCLUSIONS : To sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.

Zhang Teng, Feng Hao, Zou Xiaoyan, Peng Shixiong

2022

COVID-19, bioinformatics, biomarker, chronic Urticaria (CU), immunology

General General

COVID vaccine stigma: detecting stigma across social media platforms with computational model based on deep learning.

In Applied intelligence (Dordrecht, Netherlands)

The study presents the first computational model of COVID vaccine stigma that can identify stigmatised sentiment with a high level of accuracy and generalises well across a number of social media platforms. The aim of the study is to understand the lexical features that are prevalent in COVID vaccine discourse and disputes between anti-vaccine and pro-vaccine groups. This should provide better insight for healthcare authorities, enabling them to better navigate those discussions. The study collected posts and their comments related to COVID vaccine sentiment in English, from Reddit, Twitter, and YouTube, for the period from April 2020 to March 2021. The labels used in the model, "stigma", "not stigma", and "undefined", were collected from a smaller Facebook (Meta) dataset and successfully propagated into a larger dataset from Reddit, Twitter, and YouTube. The success of the propagation task and consequent classification is a result of state-of-the-art annotation scheme and annotated dataset. Deep learning and pre-trained word vector embedding significantly outperformed traditional algorithms, according to two-tailed P(T≤t) test and achieved F1 score of 0.794 on the classification task with three classes. Stigmatised text in COVID anti-vaccine discourse is characterised by high levels of subjectivity, negative sentiment, anxiety, anger, risk, and healthcare references. After the first half of 2020, anti-vaccination stigma sentiment appears often in comments to posts attempting to disprove COVID vaccine conspiracy theories. This is inconsonant with previous research findings, where anti-vaccine people stayed primarily within their own in-group discussions. This shift in the behaviour of the anti-vaccine movement from affirming climates to ones with opposing opinions will be discussed and elaborated further in the study.

Straton Nadiya

2022-Dec-07

COVID-19, Deep learning, Social media, Stigma, Vaccine

General General

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control.

In Computer communications

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyra- mid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fu- sion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense align- ment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network's performance. In addition, to achieve high real-time performance, our net- work is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.

Kong Weiyi, You Zhisheng, Lv Xuebin

2022-Dec-13

3D face recognition, Deep learning, Epidemic control, Face reconstruction, Multimodal fusion

General General

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach.

In Computer communications

COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.

Dhasarathan Chandramohan, Hasan Mohammad Kamrul, Islam Shayla, Abdullah Salwani, Mokhtar Umi Asma, Javed Abdul Rehman, Goundar Sam

2022-Dec-14

Deep learning system, Healthcare, Homomorphic, Privacy metrics, Privacy preserving, Security

General General

SARS-CoV-2 virus classification based on stacked sparse autoencoder.

In Computational and structural biotechnology journal

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural network based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.

Coutinho Maria G F, Câmara Gabriel B M, Barbosa Raquel de M, Fernandes Marcelo A C

2023

COVID-19, Deep learning, SARS-CoV-2, Sparse autoencoder, Viral classification

General General

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.

In Array (New York, N.Y.)

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.

Paul Showmick Guha, Saha Arpa, Biswas Al Amin, Zulfiker Md Sabab, Arefin Mohammad Shamsul, Rahman Md Mahfujur, Reza Ahmed Wasif

2022-Dec-10

Artificial intelligence, COVID-19, Deep learning, Machine learning, Pandemic

General General

FreeEnricher: Enriching Face Landmarks without Additional Cost

ArXiv Preprint

Recent years have witnessed significant growth of face alignment. Though dense facial landmark is highly demanded in various scenarios, e.g., cosmetic medicine and facial beautification, most works only consider sparse face alignment. To address this problem, we present a framework that can enrich landmark density by existing sparse landmark datasets, e.g., 300W with 68 points and WFLW with 98 points. Firstly, we observe that the local patches along each semantic contour are highly similar in appearance. Then, we propose a weakly-supervised idea of learning the refinement ability on original sparse landmarks and adapting this ability to enriched dense landmarks. Meanwhile, several operators are devised and organized together to implement the idea. Finally, the trained model is applied as a plug-and-play module to the existing face alignment networks. To evaluate our method, we manually label the dense landmarks on 300W testset. Our method yields state-of-the-art accuracy not only in newly-constructed dense 300W testset but also in the original sparse 300W and WFLW testsets without additional cost.

Yangyu Huang, Xi Chen, Jongyoo Kim, Hao Yang, Chong Li, Jiaolong Yang, Dong Chen

2022-12-19

Pathology Pathology

Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation

ArXiv Preprint

Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications. However, NMT models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. It becomes thus crucial to implement effective preventive strategies to guarantee their proper functioning. In this paper, we address the problem of hallucination detection in NMT by following a simple intuition: as hallucinations are detached from the source content, they exhibit encoder-decoder attention patterns that are statistically different from those of good quality translations. We frame this problem with an optimal transport formulation and propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. Experimental results show that our detector not only outperforms all previous model-based detectors, but is also competitive with detectors that employ large models trained on millions of samples.

Nuno M. Guerreiro, Pierre Colombo, Pablo Piantanida, André F. T. Martins

2022-12-19

General General

Predicting Ejection Fraction from Chest X-rays Using Computer Vision for Diagnosing Heart Failure

ArXiv Preprint

Heart failure remains a major public health challenge with growing costs. Ejection fraction (EF) is a key metric for the diagnosis and management of heart failure however estimation of EF using echocardiography remains expensive for the healthcare system and subject to intra/inter operator variability. While chest x-rays (CXR) are quick, inexpensive, and require less expertise, they do not provide sufficient information to the human eye to estimate EF. This work explores the efficacy of computer vision techniques to predict reduced EF solely from CXRs. We studied a dataset of 3488 CXRs from the MIMIC CXR-jpg (MCR) dataset. Our work establishes benchmarks using multiple state-of-the-art convolutional neural network architectures. The subsequent analysis shows increasing model sizes from 8M to 23M parameters improved classification performance without overfitting the dataset. We further show how data augmentation techniques such as CXR rotation and random cropping further improves model performance another ~5%. Finally, we conduct an error analysis using saliency maps and Grad-CAMs to better understand the failure modes of convolutional models on this task.

Walt Williams, Rohan Doshi, Yanran Li, Kexuan Liang

2022-12-19

General General

Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media

ArXiv Preprint

In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. This set encompasses measures of syntactic complexity, lexical sophistication and diversity, readability, and register-specific ngram frequencies, as well as sentiment and emotion lexicons. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions.

Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, Elma Kerz

2022-12-19

Radiology Radiology

Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling

ArXiv Preprint

Background and objective: COVID-19 and its variants have caused significant disruptions in over 200 countries and regions worldwide, affecting the health and lives of billions of people. Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19 since the common occurrence of radiological pneumonia findings in COVID-19 patients. We present a novel high-accuracy COVID-19 detection method that uses CXR images. Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. Conclusions: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

2022-12-19

General General

PAL: Persona-Augmented Emotional Support Conversation Generation

ArXiv Preprint

Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers' persona. We first train a model for inferring the seeker's persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that our proposed model, PAL, achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.

Jiale Cheng, Sahand Sabour, Hao Sun, Zhuang Chen, Minlie Huang

2022-12-19

Radiology Radiology

COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images

ArXiv Preprint

Purpose: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. Methods: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. Results: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. Conclusions: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

2022-12-19

General General

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images.

In Scientific reports ; h5-index 158.0

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.

Uddin Khandaker Mohammad Mohi, Dey Samrat Kumar, Babu Hafiz Md Hasan, Mostafiz Rafid, Uddin Shahadat, Shoombuatong Watshara, Moni Mohammad Ali

2022-Dec-16

Public Health Public Health

Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study.

In BMJ open

OBJECTIVE : To investigate determining factors of happiness during the COVID-19 pandemic.

DESIGN : Observational study.

SETTING : Large online surveys in Japan before and during the COVID-19 pandemic.

PARTICIPANTS : A random sample of 25 482 individuals who are representatives of the Japanese population.

MAIN OUTCOME MEASURE : Self-reported happiness measured using a 10-point Likert scale, where higher scores indicated higher levels of happiness. We defined participants with ≥8 on the scale as having high levels of happiness.

RESULTS : Among the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6-8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; p<0.001). Conversely, self-reported poor health, anxiety about future household income, psychiatric diseases except depression and feeling isolated were negatively associated with happiness (eg, aOR for self-reported poor health 0.44; 95% CI 0.39 to 0.48; p<0.001). Using machine-learning methods, we found that meaning in life and social capital (eg, having a spouse and trust in communities) were the strongest positive determinants of happiness, whereas poor health, anxiety about future household income and feeling isolated were important negative determinants of happiness. Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.

CONCLUSION : Using machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.

Osawa Itsuki, Goto Tadahiro, Tabuchi Takahiro, Koga Hayami K, Tsugawa Yusuke

2022-Dec-16

COVID-19, MENTAL HEALTH, PUBLIC HEALTH

Radiology Radiology

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

In European radiology ; h5-index 62.0

OBJECTIVES : Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.

METHODS : We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence.

RESULTS : The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores.

CONCLUSION : This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence.

KEY POINTS : • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Meng Fanyang, Kottlors Jonathan, Shahzad Rahil, Liu Haifeng, Fervers Philipp, Jin Yinhua, Rinneburger Miriam, Le Dou, Weisthoff Mathilda, Liu Wenyun, Ni Mengzhe, Sun Ye, An Liying, Huai Xiaochen, Móré Dorottya, Giannakis Athanasios, Kaltenborn Isabel, Bucher Andreas, Maintz David, Zhang Lei, Thiele Frank, Li Mingyang, Perkuhn Michael, Zhang Huimao, Persigehl Thorsten

2022-Dec-16

Artificial intelligence, COVID-19, Computed tomography, Radiologists

General General

Cardiopulmonary examinations of athletes returning to high-intensity sport activity following SARS-CoV-2 infection.

In Scientific reports ; h5-index 158.0

After SARS-CoV-2 infection, strict recommendations for return-to-sport were published. However, data are insufficient about the long-term effects on athletic performance. After suffering SARS-CoV-2 infection, and returning to maximal-intensity trainings, control examinations were performed with vita-maxima cardiopulmonary exercise testing (CPET). From various sports, 165 asymptomatic elite athletes (male: 122, age: 20y (IQR: 17-24y), training:16 h/w (IQR: 12-20 h/w), follow-up:93.5 days (IQR: 66.8-130.0 days) were examined. During CPET examinations, athletes achieved 94.7 ± 4.3% of maximal heart rate, 50.9 ± 6.0 mL/kg/min maximal oxygen uptake (V̇O2max), and 143.7 ± 30.4L/min maximal ventilation. Exercise induced arrhythmias (n = 7), significant horizontal/descending ST-depression (n = 3), ischemic heart disease (n = 1), hypertension (n = 7), slightly elevated pulmonary pressure (n = 2), and training-related hs-Troponin-T increase (n = 1) were revealed. Self-controlled CPET comparisons were performed in 62 athletes: due to intensive re-building training, exercise time, V̇O2max and ventilation increased compared to pre-COVID-19 results. However, exercise capacity decreased in 6 athletes. Further 18 athletes with ongoing minor long post-COVID symptoms, pathological ECG (ischemic ST-T changes, and arrhythmias) or laboratory findings (hsTroponin-T elevation) were controlled. Previous SARS-CoV-2-related myocarditis (n = 1), ischaemic heart disease (n = 1), anomalous coronary artery origin (n = 1), significant ventricular (n = 2) or atrial (n = 1) arrhythmias were diagnosed. Three months after SARS-CoV-2 infection, most of the athletes had satisfactory fitness levels. Some cases with SARS-CoV-2 related or not related pathologies requiring further examinations, treatment, or follow-up were revealed.

Babity Mate, Zamodics Mark, Konig Albert, Kiss Anna Reka, Horvath Marton, Gregor Zsofia, Rakoczi Reka, Kovacs Eva, Fabian Alexandra, Tokodi Marton, Sydo Nora, Csulak Emese, Juhasz Vencel, Lakatos Balint Karoly, Vago Hajnalka, Kovacs Attila, Merkely Bela, Kiss Orsolya

2022-Dec-15

Radiology Radiology

Development of criteria for cognitive dysfunction in post-COVID syndrome: the IC-CoDi-COVID approach.

In Psychiatry research ; h5-index 64.0

BACKGROUND : We aimed to develop objective criteria for cognitive dysfunction associated with the post-COVID syndrome.

METHODS : Four hundred and four patients with post-COVID syndrome from two centers were evaluated with comprehensive neuropsychological batteries. The International Classification for Cognitive Disorders in Epilepsy (IC-CoDE) framework was adapted and implemented. A healthy control group of 145 participants and a complementary data-driven approach based on unsupervised machine-learning clustering algorithms were also used to evaluate the optimal classification and cutoff points.

RESULTS : According to the developed criteria, 41.2% and 17.3% of the sample were classified as having at least one cognitive domain impaired using -1 and -1.5 standard deviations as cutoff points. Attention/processing speed was the most frequently impaired domain. There were no differences in base rates of cognitive impairment between the two centers. Clustering analysis revealed two clusters, although with an important overlap (silhouette index 0.18-0.19). Cognitive impairment was associated with younger age and lower education levels, but not hospitalization.

CONCLUSIONS : We propose a harmonization of the criteria to define and classify cognitive impairment in the post-COVID syndrome. These criteria may be extrapolated to other neuropsychological batteries and settings, contributing to the diagnosis of cognitive deficits after COVID-19 and facilitating multicenter studies to guide biomarker investigation and therapies.

Matias-Guiu Jordi A, Herrera Elena, González-Nosti María, Krishnan Kamini, Delgado-Alonso Cristina, Díez-Cirarda María, Yus Miguel, Martínez-Petit Álvaro, Pagán Josué, Matías-Guiu Jorge, Ayala José Luis, Busch Robyn, Hermann Bruce P

2022-Dec-10

COVID-19, Cognitive, IC-CoDi-COVID, Machine learning, Post-COVID syndrome., neuropsychological

Public Health Public Health

Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record.

In Frontiers in public health

BACKGROUND : As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient.

OBJECTIVE : This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms.

METHODS : The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups (i.e., 4-group classification) and using two groups after regrouping the four severity level into two (i.e., binary classification). Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE.

RESULTS : Of the four maximum severity groups, the moderate group had the highest percentage (1,115 patients; 49.5%). As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (http://statgen.snu.ac.kr/software/nomogramDaeguCovid/).

CONCLUSIONS : We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field.

Hwangbo Suhyun, Kim Yoonjung, Lee Chanhee, Lee Seungyeoun, Oh Bumjo, Moon Min Kyong, Kim Shin-Woo, Park Taesung

2022

COVID-19, artificial intelligence, machine learning, nomogram, severity

Pathology Pathology

PredictION: a predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2.

In PeerJ

The optimization of resources for research in developing countries forces us to consider strategies in the wet lab that allow the reuse of molecular biology reagents to reduce costs. In this study, we used linear regression as a method for predictive modeling of coverage depth given the number of MinION reads sequenced to define the optimum number of reads necessary to obtain >200X coverage depth with a good lineage-clade assignment of SARS-CoV-2 genomes. The research aimed to create and implement a model based on machine learning algorithms to predict different variables (e.g., coverage depth) given the number of MinION reads produced by Nanopore sequencing to maximize the yield of high-quality SARS-CoV-2 genomes, determine the best sequencing runtime, and to be able to reuse the flow cell with the remaining nanopores available for sequencing in a new run. The best accuracy was -0.98 according to the R squared performance metric of the models. A demo version is available at https://genomicdashboard.herokuapp.com/.

Valencia-Valencia David E, Lopez-Alvarez Diana, Rivera-Franco Nelson, Castillo Andres, Piña Johan S, Pardo Carlos A, Parra Beatriz

2022

Genomes, Linear models, Machine learning, Oxford nanopore technologies, Sequences

General General

A signal recognition particle-related joint model of LASSO regression, SVM-RFE and artificial neural network for the diagnosis of systemic sclerosis-associated pulmonary hypertension.

In Frontiers in genetics ; h5-index 62.0

Background: Systemic sclerosis-associated pulmonary hypertension (SSc-PH) is one of the most common causes of death in patients with systemic sclerosis (SSc). The complexity of SSc-PH and the heterogeneity of clinical features in SSc-PH patients contribute to the difficulty of diagnosis. Therefore, there is a pressing need to develop and optimize models for the diagnosis of SSc-PH. Signal recognition particle (SRP) deficiency has been found to promote the progression of multiple cancers, but the relationship between SRP and SSc-PH has not been explored. Methods: First, we obtained the GSE19617 and GSE33463 datasets from the Gene Expression Omnibus (GEO) database as the training set, GSE22356 as the test set, and the SRP-related gene set from the MSigDB database. Next, we identified differentially expressed SRP-related genes (DE-SRPGs) and performed unsupervised clustering and gene enrichment analyses. Then, we used least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) to identify SRP-related diagnostic genes (SRP-DGs). We constructed an SRP scoring system and a nomogram model based on the SRP-DGs and established an artificial neural network (ANN) for diagnosis. We used receiver operating characteristic (ROC) curves to identify the SRP-related signature in the training and test sets. Finally, we analyzed immune features, signaling pathways, and drugs associated with SRP and investigated SRP-DGs' functions using single gene batch correlation analysis-based GSEA. Results: We obtained 30 DE-SRPGs and found that they were enriched in functions and pathways such as "protein targeting to ER," "cytosolic ribosome," and "coronavirus disease-COVID-19". Subsequently, we identified seven SRP-DGs whose expression levels and diagnostic efficacy were validated in the test set. As one signature, the area under the ROC curve (AUC) values for seven SRP-DGs were 0.769 and 1.000 in the training and test sets, respectively. Predictions made using the nomogram model are likely beneficial for SSc-PH patients. The AUC values of the ANN were 0.999 and 0.860 in the training and test sets, respectively. Finally, we discovered that some immune cells and pathways, such as activated dendritic cells, complement activation, and heme metabolism, were significantly associated with SRP-DGs and identified ten drugs targeting SRP-DGs. Conclusion: We constructed a reliable SRP-related ANN model for the diagnosis of SSc-PH and investigated the possible role of SRP in the etiopathogenesis of SSc-PH by bioinformatics methods to provide a basis for precision and personalized medicine.

Xu Jingxi, Liang Chaoyang, Li Jiangtao

2022

artificial neural network, diagnostic model, machine learning, signal recognition particle, systemic sclerosis-associated pulmonary hypertension

General General

Digital Omicron Detection using Unscripted Voice Samples from Social Media.

In medRxiv : the preprint server for health sciences

The success of artificial intelligence in clinical environments relies upon the diversity and availability of training data. In some cases, social media data may be used to counterbalance the limited amount of accessible, well-curated clinical data, but this possibility remains largely unexplored. In this study, we mined YouTube to collect voice data from individuals with self-declared positive COVID-19 tests during time periods in which Omicron was the predominant variant 1,2,3 , while also sampling non-Omicron COVID-19 variants, other upper respiratory infections (URI), and healthy subjects. The resulting dataset was used to train a DenseNet model to detect the Omicron variant from voice changes. Our model achieved 0.85/0.80 sensitivity/specificity in separating Omicron samples from healthy samples and 0.76/0.70 sensitivity/specificity in separating Omicron samples from symptomatic non-COVID samples. In comparison with past studies, which used scripted voice samples, we showed that leveraging the intra-sample variance inherent to unscripted speech enhanced generalization. Our work introduced novel design paradigms for audio-based diagnostic tools and established the potential of social media data to train digital diagnostic models suitable for real-world deployment.

Anibal James T, Landa Adam J, Nguyen Hang T, Peltekian Alec K, Shin Andrew D, Song Miranda J, Christou Anna S, Hazen Lindsey A, Rivera Jocelyne, Morhard Robert A, Bagci Ulas, Li Ming, Clifton David A, Wood Bradford J

2022-Oct-06

General General

Investigating pregnant women's health information needs during pregnancy on internet platforms.

In Frontiers in physiology

Artificial intelligence gives pregnant women another avenue for receiving healthcare information. With the advancement of information and communication technology, searching online for pregnancy information has become commonplace during COVID-19. This study aimed to explore pregnant women's information-seeking behavior based on data mining and text analysis in China. Posts on maternal and infant-related websites were collected during 1 June 2020, and 31 January 2021. A total of 5,53,117 valid posts were obtained. Based on the data, we performed correlation analysis, topic analysis, and sentiment analysis. The correlation analysis showed the positive effects of population, population with a college education or above, and GDP on post counts. The topic analysis extracted six, nineteen, eighteen, thirteen, eleven, sixteen, thirteen, sixteen, nineteen, and fourteen topics in different months of pregnancy, reflecting different information needs in various pregnancy periods. The results of sentiment analysis show that a peak of the posts emerged in the second month of pregnancy and the proportion of emotionally positive posts reached its peak in the sixth month of pregnancy. The study provides important insights for understanding pregnant women's information-seeking behavior.

Hou Keke, Hou Tingting

2022

health information, pregnancy, sentiment analysis, text analysis, topic analysis

General General

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

In Cell biochemistry and function

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

Sekaran Karthik, Polachirakkal Varghese Rinku, Gnanasambandan R, Karthik G, Ramya I, George Priya Doss C

2022-Dec-14

C1-inhibitor, COVID-19, Shapley additive explanations, druggability assessment, explainable artificial intelligence, gene expression, immune-related genes, machine learning

General General

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers

ArXiv Preprint

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker, Jobie Budd, Richard Payne, Emma Karoune, David Hurley, Alexander Titcomb, Sabrina Egglestone, Ana Tendero Cañadas, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel, Tracey Thornley, Peter Diggle, Sylvia Richardson, Josef Packham, Björn W. Schuller, Davide Pigoli, Steven Gilmour, Stephen Roberts, Chris Holmes

2022-12-15

General General

The effects of gender bias in word embeddings on depression prediction

ArXiv Preprint

Word embeddings are extensively used in various NLP problems as a state-of-the-art semantic feature vector representation. Despite their success on various tasks and domains, they might exhibit an undesired bias for stereotypical categories due to statistical and societal biases that exist in the dataset they are trained on. In this study, we analyze the gender bias in four different pre-trained word embeddings specifically for the depression category in the mental disorder domain. We use contextual and non-contextual embeddings that are trained on domain-independent as well as clinical domain-specific data. We observe that embeddings carry bias for depression towards different gender groups depending on the type of embeddings. Moreover, we demonstrate that these undesired correlations are transferred to the downstream task for depression phenotype recognition. We find that data augmentation by simply swapping gender words mitigates the bias significantly in the downstream task.

Gizem Sogancioglu, Heysem Kaya

2022-12-15

Public Health Public Health

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

In PloS one ; h5-index 176.0

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

Wichmann Roberta Moreira, Fagundes Thales Pardini, de Oliveira Tiago Almeida, Batista André Filipe de Moraes, Chiavegatto Filho Alexandre Dias Porto

2022

General General

Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19

ArXiv Preprint

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

Davide Pigoli, Kieran Baker, Jobie Budd, Lorraine Butler, Harry Coppock, Sabrina Egglestone, Steven G. Gilmour, Chris Holmes, David Hurley, Radka Jersakova, Ivan Kiskin, Vasiliki Koutra, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel, Richard Payne, Stephen J. Roberts, Björn W. Schuller, Ana Tendero-Cañadas, Tracey Thornley, Alexander Titcomb

2022-12-15

General General

Doctor/Data Scientist/Artificial Intelligence Communication Model. Case Study.

In Procedia computer science

The last two years have taught us that we need to change the way we practice medicine. Due to the COVID-19 pandemic, obstetrics and gynecology setting has changed enormously. Monitoring pregnant women prevents deaths and complications. Doctors and computer data scientists must learn to communicate and work together to improve patients' health. In this paper we present a good practice example of a competitive/collaborative communication model for doctors, computer scientists and artificial intelligence systems, for signaling fetal congenital anomalies in the second trimester morphology scan.

Belciug Smaranda, Ivanescu Renato Constantin, Popa Sebastian-Doru, Iliescu Dominic Gabriel

2022

computer aided medical diagnosis, congenital anomalies, deep learning, second trimester morphology, statistical learning, statistics

General General

Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection

ArXiv Preprint

Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chaotic variational autoencoder (C-VAE to perform one-class classification (OCC) on genuine transactions. Here, we employed the logistic chaotic map to generate random noise in the latent space. The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets. We considered vanilla Variational Auto Encoder (VAE) as the baseline. It is observed that C-VAE outperformed VAE in both datasets. C-VAE achieved a classification rate of 77.9% and 87.25% in health and automobile insurance datasets respectively. Further, the t-test conducted at 1% level of significance and 18 degrees of freedom infers that C-VAE is statistically significant than the VAE.

K. S. N. V. K. Gangadhar, B. Akhil Kumar, Yelleti Vivek, Vadlamani Ravi

2022-12-15

Cardiology Cardiology

Mitogen Activated Protein Kinase (MAPK) Activation, p53, and Autophagy Inhibition Characterize the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike Protein Induced Neurotoxicity.

In Cureus

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and prions use common pathogenic pathways to induce toxicity in neurons. Infectious prions rapidly activate the p38 mitogen activated protein kinase (MAPK) pathway, and SARS-CoV-2 spike proteins rapidly activate both the p38 MAPK and c-Jun NH2-terminal kinase (JNK) pathways through toll-like receptor signaling, indicating the potential for similar neurotoxicity, causing prion and prion-like disease. In this review, we analyze the roles of autophagy inhibition, molecular mimicry, elevated intracellular p53 levels and reduced Wild-type p53-induced phosphatase 1 (Wip1) and dual-specificity phosphatase (DUSP) expression in neurons in the disease process. The pathways induced by the spike protein via toll-like receptor activation induce both the upregulation of PrPC (the normal isoform of the prion protein, PrP) and the expression of β amyloid. Through the spike-protein-dependent elevation of p53 levels via β amyloid metabolism, increased PrPC expression can lead to PrP misfolding and impaired autophagy, generating prion disease. We conclude that, according to the age of the spike protein-exposed patient and the state of their cellular autophagy activity, excess sustained activity of p53 in neurons may be a catalytic factor in neurodegeneration. An autoimmune reaction via molecular mimicry likely also contributes to neurological symptoms. Overall results suggest that neurodegeneration is in part due to the intensity and duration of spike protein exposure, patient advanced age, cellular autophagy activity, and activation, function and regulation of p53. Finally, the neurologically damaging effects can be cumulatively spike-protein dependent, whether exposure is by natural infection or, more substantially, by repeated mRNA vaccination.

Kyriakopoulos Anthony M, Nigh Greg, McCullough Peter A, Seneff Stephanie

2022-Dec

aging, autoimmunity, autophagy, covid-19, mrna vaccines, p53, prion and prion-like diseases, sars-cov-2 spike protein, senescence, wip1

General General

Crowdsourcing Temporal Transcriptomic Coronavirus Host Infection Data: resources, guide, and novel insights.

bioRxiv Preprint

The emergence of SARS-CoV-2 reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of COVID-19 during the pandemic. This study coupled existing transcriptomic data from SARS-CoV-1 lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g., CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA Damage Response and Repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients that died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens. The data from this study is available in an interactive portal (http://18.222.95.219:8047).

Flynn, J.; Ahmadi, M.; McFarland, C.; Kubal, M.; Taylor, M.; Cheng, Z.; Torchia, E. C.; Edwards, M.

2022-12-15

General General

A large-scale and PCR-referenced vocal audio dataset for COVID-19

ArXiv Preprint

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Jobie Budd, Kieran Baker, Emma Karoune, Harry Coppock, Selina Patel, Ana Tendero Cañadas, Alexander Titcomb, Richard Payne, David Hurley, Sabrina Egglestone, Lorraine Butler, Jonathon Mellor, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Radka Jersakova, Rachel A. McKendry, Peter Diggle, Sylvia Richardson, Björn W. Schuller, Steven Gilmour, Davide Pigoli, Stephen Roberts, Josef Packham, Tracey Thornley, Chris Holmes

2022-12-15

General General

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning.

In Journal of business research

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

Hu Hui, Xu Jiajun, Liu Mengqi, Lim Ming K

2023-Feb

BILSTM, Bidirectional Long-Short Term Memory, Blockchain, CNN, Convolutional Neural Network, COVID-19 pandemic, DTs, Digital Technologies, GRU, Gate Recurrent Unit, IPFS, Interplanetary File System, Intelligent system, Internet of things, IoT, Internet of Things, LSTM, Long-Short Term Memory, Machine learning, RFID, Radio Frequency Identification, RNN, Recurrent Neural Network, VSC, Vaccine Supply Chain, Vaccine supply chain, dApp, Decentralized Application

General General

Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning.

In Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

In the problem of composite hypothesis testing, identifying the potential uniformly most powerful (UMP) unbiased test is of great interest. Beyond typical hypothesis settings with exponential family, it is usually challenging to prove the existence and further construct such UMP unbiased tests with finite sample size. For example in the COVID-19 pandemic with limited previous assumptions on the treatment for investigation and the standard of care, adaptive clinical trials are appealing due to ethical considerations, and the ability to accommodate uncertainty while conducting the trial. Although several methods have been proposed to control Type I error rates, how to find a more powerful hypothesis testing strategy is still an open question. Motivated by this problem, we propose an automatic framework of constructing test statistics and corresponding critical values via machine learning methods to enhance power in a finite sample. In this article, we particularly illustrate the performance using Deep Neural Networks (DNN) and discuss its advantages. Simulations and two case studies of adaptive designs demonstrate that our method is automatic, general and prespecified to construct statistics with satisfactory power in finite-sample. Supplemental materials are available online including R code and an R shiny app.

Zhan Tianyu, Kang Jian

2022

Confirmatory adaptive clinical trials, Deep neural networks, Efficient inference methods, Neyman-Pearson Lemma, Research assistant tools

General General

Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding.

In Network modeling and analysis in health informatics and bioinformatics

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of this virus has led to the insurgence of many variants that impact protein sequence and structure that need continuous monitoring and analysis of the sequences to understand the genetic evolution and to prevent possible dangerous outcomes. Some variants causing the modification of the structure of the proteins, such as the Spike protein S, need to be monitored. Protein contact networks (PCNs) have been recently proposed as a modelling framework for protein structures. In such a framework, the protein structure is represented as an unweighted graph whose nodes are the central atoms of the backbones (C- α ), and edges connect two atoms falling in the spatial distance between 4 and 7 Å. PCN may also be a data-rich representation since we may add to each node/atom biological and topological information. Such formalism enables the possibility of using algorithms from graph theory to analyze the graph. In particular, we refer to graph embedding methods enabling the analysis of such graphs with deep learning methods. In this work, we explore the possibility of embedding PCN using Graph Neural Networks and then analyze in the embedded space each residue to distinguish mutated residues from non-mutated ones. In particular, we analyzed the structure of the Spike protein of the coronavirus. First, we obtained the PCNs of the Spike protein for the wild-type, α , β , and δ variants. Then we used the GraphSage embedding algorithm to obtain an unsupervised embedding. Then we analyzed the point of mutation in the embedded space. Results show the characteristics of the mutation point in the embedding space.

Guzzi Pietro Hiram, Lomoio Ugo, Puccio Barbara, Veltri Pierangelo

2023

General General

Profiling the B cell immune response elicited by vaccination against the respiratory virus SARS-CoV-2.

In Frontiers in immunology ; h5-index 100.0

B cells play a fundamental role in host defenses against viral infections. Profiling the B cell response elicited by SARS-CoV-2 vaccination, including the generation and persistence of antigen-specific memory B cells, is essential for improving the knowledge of vaccine immune responsiveness, beyond the antibody response. mRNA-based vaccines have shown to induce a robust class-switched memory B cell response that persists overtime and is boosted by further vaccine administration, suggesting that memory B cells are critical in driving a recall response upon re-exposure to SARS-CoV-2 antigens. Here, we focus on the role of the B cell response in the context of SARS-CoV-2 vaccination, offering an overview of the different technologies that can be used to identify spike-specific B cells, characterize their phenotype using machine learning approaches, measure their capacity to reactivate following antigen encounter, and tracking the maturation of the B cell receptor antigenic affinity.

Pettini Elena, Medaglini Donata, Ciabattini Annalisa

2022

B cell ELISpot, BCR repertoire, COVID-19 vaccines, SARS-CoV-2, computational flow cytometry, memory B cells, respiratory virus

General General

Vaccine supply chain coordination using blockchain and artificial intelligence technologies.

In Computers & industrial engineering

Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a closed-loop vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study's major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers' difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.

Gao Ye, Gao Hongwei, Xiao Han, Yao Fanjun

2022-Dec-05

AI technology, Blockchain technology, Differential game, Supply chain management, Vaccine supply chain

General General

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.

In Biomedical signal processing and control

The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.

Rao Yunbo, Lv Qingsong, Zeng Shaoning, Yi Yuling, Huang Cheng, Gao Yun, Cheng Zhanglin, Sun Jihong

2023-Mar

Adaptive threshold, Attention mechanism, COVID-19, CT image, GGO segmentation, Pneumonia

Public Health Public Health

How public health agencies communicate with the public on TikTok under the normalization of COVID-19: A case of 2022 Shanghai's outbreak.

In Frontiers in public health

OBJECTIVE : As life with COVID-19 became a norm in 2022, the public's demand for and perception of COVID-19-related information has changed. This study analyzed the performance and responses of Healthy China and the public at various stages of COVID-19 normalization using the crisis and emergency risk communication (CERC) theory.

METHODS : This study was based on the 2022 Shanghai COVID-19 outbreak and data from "Healthy China," the official TikTok account of the National Health Commission of the People's Republic of China (NHCC). First, we divided the Shanghai lockdown into five stages in accordance with the CERC. Second, the videos released by Healthy China were open-coded. Third, to understand the distribution of strategies across the stages, we used counts and percentages to summarize the categorical variables. Fourth, we investigated the distribution of public participation indicators using descriptive statistical analysis. Finally, the relationship between stage and communication strategy was examined using the chi-square test and negative binomial regression.

RESULTS : (1) Healthy China adopted a more flexible approach to communication strategies; (2) new cases per day was the commonly used substrategy for uncertainty reduction; (3) there was a significant difference in the strategies used by Healthy China at different stages; (4) public participation was highest in the pre-crisis period; and (5) the stage had a significant positive impact on the number of views, favorites, likes, and shares.

CONCLUSIONS : This research provides insight into effective communication strategies for the government or public health agencies to employ during COVID-19 normalization.

Che ShaoPeng, Zhang Shunan, Kim Jang Hyun

2022

COVID-19, TikTok, communication strategy, crisis and emergency risk communication, negative binomial regression, public health agency, public health emergency, social media

General General

Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.

In Applied soft computing

Covid-19 has become a worldwide epidemic which has caused the death of millions in a very short time. This disease, which is transmitted rapidly, has mutated and different variations have emerged. Early diagnosis is important to prevent the spread of this disease. In this study, a new deep learning-based architecture is proposed for rapid detection of Covid-19 and other symptoms using CT and X-ray chest images. This method, called CovidDWNet, is based on a structure based on feature reuse residual block (FRB) and depthwise dilated convolutions (DDC) units. The FRB and DDC units efficiently acquired various features in the chest scan images and it was seen that the proposed architecture significantly improved its performance. In addition, the feature maps obtained with the CovidDWNet architecture were estimated with the Gradient boosting (GB) algorithm. With the CovidDWNet+GB architecture, which is a combination of CovidDWNet and GB, a performance increase of approximately 7% in CT images and between 3% and 4% in X-ray images has been achieved. The CovidDWNet+GB architecture achieved the highest success compared to other architectures, with 99.84% and 100% accuracy rates, respectively, on different datasets containing binary class (Covid-19 and Normal) CT images. Similarly, the proposed architecture showed the highest success with 96.81% accuracy in multi-class (Covid-19, Lung Opacity, Normal and Viral Pneumonia) X-ray images and 96.32% accuracy in the dataset containing X-ray and CT images. When the time to predict the disease in CT or X-ray images is examined, it is possible to say that it has a high speed because the CovidDWNet+GB method predicts thousands of images within seconds.

Celik Gaffari

2022-Dec-07

Covid-19 diagnosis, Deep learning, Depthwise dilated convolutions, Feature reuse residual block, Gradient boosting

General General

Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.

In Computers in biology and medicine

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.

Jyoti Kumari, Sushma Sai, Yadav Saurabh, Kumar Pawan, Pachori Ram Bilas, Mukherjee Shaibal

2022-Nov-23

COVID-19, Chest X-ray images, Image decomposition and classification, Memristive crossbar array (MCA) based model, TQWT method

General General

Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features.

In Computational intelligence and neuroscience

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.

Yazdani Azita, Fekri-Ershad Shervan, Jelvay Saeed

2022

General General

The influence of the Fourth Industrial Revolution on organisational culture: An empirical investigation.

In Frontiers in psychology ; h5-index 92.0

The Fourth Industrial Revolution (4IR) is known to transform and create opportunities for the world of work. However, little is known about how the future workforce, such as university students, are being equipped and exposed to 4IR technologies and ways of thinking in a South African (SA) context. This study's findings contribute to understanding the influence of organisational culture on the uptake of 4IR technology within higher education (HE) in SA during a pandemic. The study uses Edgar Schein's theoretical framework to explore the organisational culture at a university in the Gauteng province. The article responds further to the questions on how 4IR technology and principles are understood and applied within the context, and how to investigate to what extent the 4IR is reflected upon or embedded in the university's culture. A qualitative research design is used, and data are gathered through in-depth, semi-structured interviews from seven purposively selected academic and senior management staff members. Thematic analysis uncovered that the university's ambitious and competitive culture contributed to a positive uptake of 4IR technology and principles, even pre-COVID-19. Furthermore, the specific influence of the university's Vice-Chancellor to build 4IR thinking into the university helped shape more 4IR thinking and technologies, such as artificial intelligence, whilst still considering the existing disparities of SA, as a developing country.

Singaram Shwetha, Mayer Claude-Hélène

2022

COVID-19, Fourth Industrial Revolution, South Africa, higher education, organisational culture

General General

COVID-19 related TV news and stock returns: Evidence from major US TV stations.

In The Quarterly review of economics and finance : journal of the Midwest Economics Association

We investigate a novel dataset of more than half a million 15 seconds transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics discussed in US TV news. We find that several topics identified by the LDA predict significant and economically meaningful market reactions in the next day, even after controlling for the general TV tone derived from a field-specific COVID-19 tone dictionary. Our results suggest that COVID-19 related TV content had nonnegligible effects on financial markets during the pandemic.

Möller Rouven, Reichmann Doron

2023-Feb

COVID-19 TV news, Natural language processing, Stock returns, Topic modeling

Radiology Radiology

Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.

In Journal of medical imaging (Bellingham, Wash.)

PURPOSE : We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models.

APPROACH : Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features.

RESULTS : Out of 111 radiomic features, 43% had excellent reliability ( ICC > 0.90 ), and 55% had either good ( ICC > 0.75 ) or moderate ( ICC > 0.50 ) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations.

CONCLUSIONS : Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.

Castro Marcelo A, Reza Syed, Chu Winston T, Bradley Dara, Lee Ji Hyun, Crozier Ian, Sayre Philip J, Lee Byeong Y, Mani Venkatesh, Friedrich Thomas C, O’Connor David H, Finch Courtney L, Worwa Gabriella, Feuerstein Irwin M, Kuhn Jens H, Solomon Jeffrey

2022-Nov

COVID-19, animal models, computed tomography, radiomics, reliability, sensitivity

General General

Analyzing the Prospects of Blockchain in Healthcare Industry.

In Computational and mathematical methods in medicine

Deployment of secured healthcare information is a major challenge in a web-based environment. eHealth services are subjected to same security threats as other services. The purpose of blockchain is to provide a structure and security to the organization data. Healthcare data deals with confidential information. The medical records can be well organized and empower their propagation in a secured manner through the usage of blockchain technology. The study throws light on providing security of health services through blockchain technology. The authors have analyzed the various aspects of role of blockchain in healthcare through an extensive literature review. The application of blockchain in COVID-19 has also been analyzed and discussed in the study. Further application of blockchain in Indian healthcare has been highlighted in the paper. The study provides suggestions for strengthening the healthcare system by blending machine learning, artificial intelligence, big data, and IoT with blockchain.

Srivastava Shilpa, Pant Millie, Jauhar Sunil Kumar, Nagar Atulya K

2022

General General

SARS-CoV-2 primary and breakthrough infections in patients with cancer: Implications for patient care.

In Best practice & research. Clinical haematology

Initial reports of SARS-CoV-2 caused COVID-19 suggested that patients with malignant diseases were at increased risk for infection and its severe consequences. In order to provide early United States population-based assessments of SARS-CoV-2 primary infections in unvaccinated patients with hematologic malignancies or cancer, and SARS-CoV-2 breakthrough infections in vaccinated patients with hematologic malignancies or cancer, we conducted retrospective studies using two, unique nationwide electronic health records (EHR) databases. Using these massive databases to provide highly statistically significant data, our studies demonstrated that, compared to patients without malignancies, risk for COVID-19 was increased in patients with all cancers and with all hematologic malignancies. Risks varied with specific types of malignancy. Patients with hematologic malignancies or cancer were at greatest risk for COVID-19 during the first year after diagnosis. Risk for infection was increased for patients 65 years and older, compared to younger patients and among Black patients compared to white patients. When patients with hematologic malignancies or cancer were vaccinated against SARS-CoV-2, their risk for breakthrough infections was decreased relative to primary infections but remained elevated relative to vaccinated patients without malignancies. Compared to vaccinated patients without malignancies, vaccinated patients with hematologic malignancy or cancer showed increased risk for infection at earlier post vaccination time points. As with primary infections, risk for breakthrough infections was greatest in patients during their first year of hematologic malignancy or cancer. There were no signs of racial disparities among vaccinated patients with hematologic malignancies or cancer. These results provide the population basis to understand the significance of subsequent immunologic studies showing relative defective and delayed immunoresponsiveness to SARS-CoV-2 vaccines among patients with hematologic malignancies and cancers. These studies further provide the basis for recommendations regarding COVID-19 vaccination, vigilance and maintaining mitigation strategies in patients with hematologic malignancies and cancers.

Wang Lindsey, Wang William, Xu Rong, Berger Nathan A

2022-Sep

COVID-19, Cancer, Hematologic malignancies, SARS-CoV-2

General General

US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images.

In Computers in biology and medicine

BACKGROUND : Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis.

METHODS : We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set.

RESULTS : Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy.

CONCLUSIONS : The proposed framework can help improve the accuracy of ultrasound diagnosis.

Monkam Patrice, Lu Wenkai, Jin Songbai, Shan Wenjun, Wu Jing, Zhou Xiang, Tang Bo, Zhao Hua, Zhang Hongmin, Ding Xin, Chen Huan, Su Longxiang

2022-Nov-30

Deep learning, Improved diagnostic accuracy, Speckle suppression, Texture enhancement, Ultrasound image

Public Health Public Health

A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19.

In Frontiers in public health

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.

Li Zhuo, Xu Ruiqing, Shen Yifei, Cao Jiannong, Wang Ben, Zhang Ying, Li Shikang

2022

COVID-19, disease progression prediction, disease severity assessment, multimodal feature fusion, sequential stage-wise learning

General General

Deep Evolutionary Forecasting identifies highly-mutated SARS-CoV-2 variants via functional sequence-landscape enumeration.

In Research square

Host-pathogen interactions drive an evolutionary game of cat-and-mouse between a pathogen's protein virulence factors, the host's adaptive immune system, and therapeutics targeting the pathogen. There is an urgent need for treatments and prophylactics that remain effective as a pathogen evolves, and the ability to predict pathogen evolution is a longstanding challenge. Therefore, a common strategy has been to target conserved epitopes, but strong selective pressures can drive pathogens to evolve resistance nonetheless. Here, we report a novel, generally-applicable approach called Deep Evolutionary Forecasting that predicts protein evolution using artificial intelligence and molecular modeling. The first step is to perform a complete enumeration of the functional sequence landscape in silico for a target protein. Then, we construct a graph where the edges between sequence variants are weighted by evolutionary probability. Protein evolution is forecasted by traversing this graph. We chose the SARS-CoV-2 receptor binding domain (RBD) as a model system because highly-mutated viral variants have continued to emerge that escape available therapeutics and vaccines. The RBD variants that we forecasted carry up to 11 concurrent amino acid substitutions at the host receptor binding site. Pseudoviruses harboring forecasted RBDs are active and escape binding and neutralization by FDA-approved monoclonal antibody therapeutics. We identified bottlenecks in the evolutionary landscape of SARS-CoV-2 that are promising targets for therapeutics that preempt evolution.

Colom Mireia Solà, Vucinic Jelena, Adolf-Bryfogle Jared, Bowman James W, Verel Sébastien, Moczygemba Isabelle, Schiex Thomas, Simoncini David, Bahl Christopher D

2022-Dec-02

General General

[Chest imaging-based artificial intelligence in the diagnosis of coronavirus disease 2019 and prospects for future research].

In Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases

Artificial intelligence (AI) has been applied increasingly in the medical field during the past 5 years. Within respiratory medicine, chest imaging AI is one of the relevant hotspots, commonly trained to identify pulmonary nodules/lung tumors, tuberculosis, pneumonia, interstitial lung disease, chronic obstructive pulmonary disease, pulmonary embolism and other pathologies. Due to the non-specific clinical manifestations and the low detection rate of pathogens, precise diagnosis and treatment of pneumonia remain challengeable. Since the outbreak of coronavirus disease 2019 (COVID-19), chest imaging AI has demonstrated its clinical value in accurate diagnosis and quantitative measurements of COVID-19. Moreover, an AI system can assist the clinicians to identify the high-risk COVID-19 patients who warrant close monitoring and timely intervention. However, there are still some limitations in the existing studies, such as small sample size, lack of multi-modal assessment of the AI model, and rough classification of pneumonia. Therefore, some suggestions for future research were put forward in this paper. Most of all, more attention should be paid to the collection of high-quality datasets, standardization of image annotation, technology innovation, algorithm optimization and model verification. Besides, the application of imaging AI on other types of pneumonia including viral pneumonia, bacterial pneumonia and pneumomycosis deserves further study. In conclusion, chest imaging AI is expected to play a vital role in decision-making for pneumonia in the future.

Li Y, Liu S Y, Zheng J P

2022-Dec-12

Cardiology Cardiology

Impact of Technologic Innovation and COVID-19 Pandemic on Pediatric Cardiology Telehealth.

In Current treatment options in pediatrics

PURPOSE OF REVIEW : Established telehealth practices in pediatrics and pediatric cardiology are evolving rapidly. This review examines several concepts in contemporary telemedicine in our field: recent changes in direct-to-consumer (DTC) pediatric telehealth (TH) and practice based on lessons learned from the pandemic, scientific data from newer technological innovations in pediatric cardiology, and how TH is shaping global pediatric cardiology practice.

RECENT FINDINGS : In 2020, the global pandemic of COVID-19 led to significant changes in healthcare delivery. The lockdown and social distancing guidelines accelerated smart adaptations and pivots to ensure continued pediatric care albeit in a virtual manner. Remote cardiac monitoring technology is continuing to advance at a rapid pace secondary to advances in the areas of Internet access, portable hand-held devices, and artificial intelligence.

SUMMARY : TH should be approached programmatically by pediatric cardiac healthcare providers with careful selection of patients, technology platforms, infrastructure setup, documentation, and compliance. Payment parity with in-person visits should be advocated and legislated. Newer remote cardiac monitoring technology should be expanded for objective assessment and optimal outcomes. TH continues to be working beyond geographical boundaries in pediatric cardiology and should continue to expand and develop.

Shah Sanket S, Buddhavarapu Amulya, Husain Majid, Sable Craig, Satou Gary

2022

Digital, Global telehealth, Pediatric cardiology, Post-pandemic telemedicine, Telehealth

Surgery Surgery

Preparing for the next pandemic: Simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents.

In Frontiers in immunology ; h5-index 100.0

BACKGROUND : Preparation to address the critical gap in a future pandemic between non-pharmacological measures and the deployment of new drugs/vaccines requires addressing two factors: 1) finding virus/pathogen-agnostic pathophysiological targets to mitigate disease severity and 2) finding a more rational approach to repurposing existing drugs. It is increasingly recognized that acute viral disease severity is heavily driven by the immune response to the infection ("cytokine storm" or "cytokine release syndrome"). There exist numerous clinically available biologics that suppress various pro-inflammatory cytokines/mediators, but it is extremely difficult to identify clinically effective treatment regimens with these agents. We propose that this is a complex control problem that resists standard methods of developing treatment regimens and accomplishing this goal requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training successful game-playing artificial intelligences (AIs). This proof-of-concept study determines if simulated sepsis (e.g. infection-driven cytokine storm) can be controlled in the absence of effective antimicrobial agents by targeting cytokines for which FDA-approved biologics currently exist.

METHODS : We use a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. DRL training used a Deep Deterministic Policy Gradient (DDPG) approach with a clinically plausible control interval of 6 hours with manipulation of six cytokines for which there are existing drugs: Tumor Necrosis Factor (TNF), Interleukin-1 (IL-1), Interleukin-4 (IL-4), Interleukin-8 (IL-8), Interleukin-12 (IL-12) and Interferon-γ(IFNg).

RESULTS : DRL trained an AI policy that could improve outcomes from a baseline Recovered Rate of 61% to one with a Recovered Rate of 90% over ~21 days simulated time. This DRL policy was then tested on four different parameterizations not seen in training representing a range of host and microbe characteristics, demonstrating a range of improvement in Recovered Rate by +33% to +56.

DISCUSSION : The current proof-of-concept study demonstrates that significant disease severity mitigation can potentially be accomplished with existing anti-mediator drugs, but only through a multi-modal, adaptive treatment policy requiring implementation with an AI. While the actual clinical implementation of this approach is a projection for the future, the current goal of this work is to inspire the development of a research ecosystem that marries what is needed to improve the simulation models with the development of the sensing/assay technologies to collect the data needed to iteratively refine those models.

Cockrell Chase, Larie Dale, An Gary

2022

COVID - 19, agent - based modeling, cytokine storm, deep reinforcement learning, drug repurposing, machine learning and AI, multiscale modeling and simulation, sepsis

Surgery Surgery

Military Medical Role in Civilian Disaster.

In AACN advanced critical care

US military medical units have responded to natural disasters (eg, hurricanes, earthquakes), relieved overwhelmed civilian health care systems (eg, during the COVID-19 pandemic), and provided support to stabilization efforts after civil unrest. The military will continue to assist civilian agencies with future medical response to similar disasters, contagious outbreaks, or even terrorist attacks. The keys to an effective disaster response are unity of effort, prior coordination, and iterative practice during military-civilian exercises to identify strengths and areas of improvement. Critical care advanced practice nurses are likely to work concurrently with military medical colleagues in multiple scenarios in the future; therefore, it is important for these nurses to understand the capacities and limitations of military medical assets. This article describes the capabilities and collaboration needed between civilian and military medical assets during a variety of disaster scenarios.

Flarity Kathleen, DeDecker Lisa D, Averett-Brauer Tamara A, Duquette-Frame Teresa, Rougeau Tami R, Aycock Andrew, Urban Shane, McKay Jerome T, Cox Daniel B

2022-Dec-15

civil defense, disaster medicine, disasters, military, military medicine

General General

Insights from Incorporating Quantum Computing into Drug Design Workflows.

In Bioinformatics (Oxford, England)

MOTIVATION : While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations.

RESULTS : We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD.

AVAILABILITY : Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Lau Bayo, Emani Prashant S, Chapman Jackson, Yao Lijing, Lam Tarsus, Merrill Paul, Warrell Jonathan, Gerstein Mark B, Lam Hugo Y K

2022-Dec-07

Radiology Radiology

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.

In Scientific reports ; h5-index 158.0

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

Chung Joowon, Kim Doyun, Choi Jongmun, Yune Sehyo, Song Kyungdoo, Kim Seonkyoung, Chua Michelle, Succi Marc D, Conklin John, Longo Maria G Figueiro, Ackman Jeanne B, Petranovic Milena, Lev Michael H, Do Synho

2022-Dec-07

General General

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia.

In Scientific data

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Alzate-Grisales Jesús Alejandro, Mora-Rubio Alejandro, Arteaga-Arteaga Harold Brayan, Bravo-Ortiz Mario Alejandro, Arias-Garzón Daniel, López-Murillo Luis Humberto, Mercado-Ruiz Esteban, Villa-Pulgarin Juan Pablo, Cardona-Morales Oscar, Orozco-Arias Simon, Buitrago-Carmona Felipe, Palancares-Sosa Maria Jose, Martínez-Rodríguez Fernanda, Contreras-Ortiz Sonia H, Saborit-Torres Jose Manuel, Montell Serrano Joaquim Ángel, Ramirez-Sánchez María Mónica, Sierra-Gaber Mario Alfonso, Jaramillo-Robledo Oscar, de la Iglesia-Vayá Maria, Tabares-Soto Reinel

2022-Dec-07

General General

The First Case Series From Japan of Primary Headache Patients Treated by Completely Online Telemedicine.

In Cureus

Background Since March 2020, the coronavirus disease 2019 pandemic has increased the need for telemedicine to avoid in-person consultations. Online clinics for most diseases officially started in Japan in April 2022. Here, we report the cases of eight Japanese headache patients treated by completely online telemedicine for three months from the first visit. Methodology From the medical records between July 2022 and October 2022, we retrospectively investigated eight consecutive first-visit primary headache patients who consulted our online headache clinic via telemedicine and continued to see us via telemedicine only. The Headache Impact Test-6 (HIT-6) score, monthly headache days (MHD), and monthly acute medication intake days (AMD) were investigated over the observation period. Results A total of eight women were included, and the median (interquartile range) age was 30 (24-51) years. The median HIT-6 scores before, one, and three months after treatment were 63 (58-64), 54 (53-62), and 52 (49-54), respectively. MHD before, one, and three months after treatment were 15 (9-28), 12 (3-17), and 2 (2-8), respectively. AMD before, one, and three months after treatment were 10 (3-13), 3 (1-8), and 2 (0-3), respectively. Significant reductions in HIT-6 and MDH were observed three months after the initial consultation (p = 0.007 and p = 0.042, respectively). AMD was not significantly decreased at three months (p = 0.447). Conclusions This is the first report of Japanese patients treated by completely online telemedicine for three months from the first visit. HIT-6 and MDH can be significantly decreased at three months by only telemedicine. Online telemedicine is expected to be widely used to resolve unmet needs in headache treatment.

Katsuki Masahito

2022-Nov

artificial intelligence, coronavirus disease 2019 (covid-19), information technology, medication-overuse headache (moh), migraine, online telemedicine, tension-type headache

General General

Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach.

In Journal of business research

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

Pal Shounak, Biswas Baidyanath, Gupta Rohit, Kumar Ajay, Gupta Shivam

2023-Feb

Machine learning, Mobile health, Proportional-odds logit, Service-dominant logic, Text analytics

General General

Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control.

In The European physical journal. Special topics

This special issue contains 35 regular articles on the analysis and dynamics of COVID-19 with several applications. Some analyses are on the construction of mathematical models representing the dynamics of COVID-19, and some are on the estimations and predictions of the disease, a few with possible applications. The various contributions report important, timely, and promising results, such as the effects of several waves, deep learning-based COVID-19 classifications, and multivariate time series with applications.

Banerjee Santo

2022-Dec-02

General General

Supply chain risk management with machine learning technology: A literature review and future research directions.

In Computers & industrial engineering

Coronavirus disease 2019 (COVID-19) has placed tremendous pressure on supply chain risk management (SCRM) worldwide. Recent technological advances, especially machine learning (ML) technology, have shown the possibility to prevent supply chain risk (SCR) by decreasing the need for human labor, increasing response speed, and predicting risk. However, the literature lacks a comprehensive analysis of the relationship between ML and SCRM. This work conducts a comprehensive review of the relatively limited literature in this field. An analysis of 67 shortlisted articles from 9 databases shows that this area is still in the rapid development stage and that researchers have shown extraordinary interest in it. The main purpose of this study is to review the current research status so that researchers have a clear understanding of the research gaps in this area. Moreover, this study provides an opportunity for researchers and practitioners to pay attention to ML algorithms for SCRM during the COVID-19 pandemic.

Yang Mei, Lim Ming K, Qu Yingchi, Ni Du, Xiao Zhi

2023-Jan

Algorithm, COVID-19, Machine learning, Research status, Supply chain risk management

General General

Automatic approach for mask detection: effective for COVID-19.

In Soft computing

The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.

Banik Debajyoty, Rawat Saksham, Thakur Aayush, Parwekar Pritee, Satapathy Suresh Chandra

2022-Dec-02

Boundary-layer meteorology, CNN (Convolutional neural network), COVID-19, Grad CAM, MobileNetV2

General General

Immunophenotypes of anti-SARS-CoV-2 responses associated with fatal COVID-19.

In ERJ open research

BACKGROUND : The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood.

METHODS : A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors.

RESULTS : Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36-21.69; 95% CI 1.51-163.61 and HR 8.39-10.79; 95% CI 1.20-82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality.

CONCLUSIONS : By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice.

Šelb Julij, Bitežnik Barbara, Bidovec Stojković Urška, Rituper Boštjan, Osolnik Katarina, Kopač Peter, Svetina Petra, Cerk Porenta Kristina, Šifrer Franc, Lorber Petra, Trinkaus Leiler Darinka, Hafner Tomaž, Jerič Tina, Marčun Robert, Lalek Nika, Frelih Nina, Bizjak Mojca, Lombar Rok, Nikolić Vesna, Adamič Katja, Mohorčič Katja, Grm Zupan Sanja, Šarc Irena, Debeljak Jerneja, Koren Ana, Luzar Ajda Demšar, Rijavec Matija, Kern Izidor, Fležar Matjaž, Rozman Aleš, Korošec Peter

2022-Oct

Radiology Radiology

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19.

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe.

PURPOSE : To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19.

STUDY TYPE : Prospective observational.

POPULATION : A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis).

FIELD STRENGTH AND SEQUENCES : A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences.

ASSESSMENT : Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group.

STATISTICAL TESTS : Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05.

RESULTS : Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue.

DATA CONCLUSION : These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition.

EVIDENCE LEVEL : 2.

TECHNICAL EFFICACY : Stage 3.

Kim William S H, Ji Xiang, Roudaia Eugenie, Chen J Jean, Gilboa Asaf, Sekuler Allison, Gao Fuqiang, Lin Zhongmin, Jegatheesan Aravinthan, Masellis Mario, Goubran Maged, Rabin Jennifer S, Lam Benjamin, Cheng Ivy, Fowler Robert, Heyn Chris, Black Sandra E, Graham Simon J, MacIntosh Bradley J

2022-Dec-06

COVID-19, SARS-CoV-2, cerebral blood flow, fatigue, post-COVID-19

General General

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.

In Neural computing & applications

Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.

Ryalat Mohammad Hashem, Dorgham Osama, Tedmori Sara, Al-Rahamneh Zainab, Al-Najdawi Nijad, Mirjalili Seyedali

2022-Dec-01

CT images, Covid-19, Harris hawks optimization, Image segmentation, Multilevel thresholding, Otsu method

General General

Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.

In Patterns (New York, N.Y.)

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid collection shows that (1) most Long Covid articles do not refer to Long Covid by any name (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid.

Leaman Robert, Islamaj Rezarta, Allot Alexis, Chen Qingyu, Wilbur W John, Lu Zhiyong

2022-Dec-01

COVID-19, Long Covid, active learning, data programming, machine learning, natural language processing, post-acute sequelae of SARS-CoV-2 infection, text classification, weak supervision

Public Health Public Health

A four-generation family transmission chain of COVID-19 along the China-Myanmar border in October to November 2021.

In Frontiers in public health

BACKGROUND : Foreign imported patients and within-household transmission have been the focus and difficulty of coronavirus disease 2019 (COVID-19) prevention and control, which has also posed challenges to border areas' management. However, household transmission caused by foreign imported cases has not been reported in China's border areas. This study aimed to reveal a clear family clustering transmission chain of COVID-19 caused by contact with Myanmar refugees along the China-Myanmar border during an outbreak in October to November 2021.

METHODS : During the outbreak, detailed epidemiological investigations were conducted on confirmed patients with COVID-19 and their close contacts in daily activities. Patients were immediately transported to a designated hospital for treatment and quarantine, and their close contacts were quarantined at designated sites. Regular nucleic acid testing and SARS-CoV-2 antibody testing were provided to them.

RESULTS : A clear four-generation family clustering transmission involving five patients with COVID-19 was found along the China-Myanmar border. The index case (Patient A) was infected by brief conversations with Myanmar refugees across border fences during work. His wife (Patient B) and 9-month-old daughter (Patient C) were second-generation cases infected by daily contact with him. His 2-year-old daughter (Patient D) was the third-generation case infected by her mother and sister during quarantine in the same room and then transmitted the virus to her grandmother (Patient E, the fourth-generation case) who looked after her after Patients B and C were diagnosed and transported to the hospital. The household secondary attack rate was 80.0%, the average latent period was 4 days, and the generation time was 3 days. Ten of 942 close contacts (1.1%) of this family had positive IgM antibody during the medical observation period. In total 73.9% (696/942) of them were positive for IgG antibody and 8.3% (58/696) had IgG levels over 20 S/CO (optical density of the sample/cut-off value of the reagent).

CONCLUSION : This typical transmission chain indicated that it is essential to strengthen COVID-19 prevention and control in border areas, and explore more effective children care approaches in quarantine sites.

Yan Xiangyu, Xiao Wei, Zhou Saipeng, Wang Xuechun, Wang ZeKun, Zhao Mingchen, Li Tao, Jia Zhongwei, Zhang Bo, Shui Tiejun

2022

COVID-19, China-Myanmar border, outbreak, refugees, transmission chain

oncology Oncology

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection.

In Frontiers in public health

BACKGROUND : SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility.

METHODS : This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy.

RESULTS : Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials.

CONCLUSION : Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Song Qi-Xiang, Jin Zhichao, Fang Weilin, Zhang Chenxu, Peng Chi, Chen Min, Zhuang Xu, Zhai Wei, Wang Jun, Cao Min, Wei Shun, Cai Xia, Pan Lei, Xu Qingrong, Zheng Junhua

2022

SARS-CoV-2, infectivity, nucleic acid test, recurrently positive, viral load, virus isolation

General General

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning.

In Medicine in drug discovery

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID‑19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID‑19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

Gantla Maanaskumar R, Tsigelny Igor F, Kouznetsova Valentina L

2022-Nov-29

1D 2D 3D, one- two- three-dimensional, ADAM17, A disintegrin and metalloprotease 17, ARDS, acute respiratory distress syndrome, AT1R, Angiotensin II receptor type 1, AUROC, area under receiver operator characteristic curve, COVID–19, coronavirus disease 2019, COVID–19, CRS, cytokine release syndrome, CXCL10, CXC–chemokine ligand 10, FDA, Food and Drug Administration, G–CSF, granulocyte colony stimulating factor, IC50, half maximal inhibitory concentration, ICU, intensive care unit, IL, interleukin, JAK1, Janus kinase 1, MCP1, monocyte chemoattractant protein–1, MIP1α, macrophage inflammatory protein 1, ML, machine learning, NF–κB, Nuclear Factor–Kappa B, PDB, Protein Data Bank, PaDEL, Pharmaeutical data exploration laboratory, ROC, receiver operator characteristic curve, SARS–CoV–2, SMILES, Simplified Molecular-Input Line-Entry System, STAT3, signal transducer and activator of transcription 3, TNFα, tumor necrosis factor α, WEKA, Waikato Environment for Knowledge Analysis, docking, machine learning, multi-targeted drug discovery, screening of FDA-approved drugs

General General

On the accuracy of Covid-19 forecasting methods in Russia for two years.

In Procedia computer science

The effectiveness of predicting the dynamics of the coronavirus pandemic for Russia as a whole and for Moscow is studied for a two-year period beginning March 2020. The comparison includes well-proven population models and statistic methods along with a new data-driven model based on the LSTM neural network. The latter model is trained on a set of Russian regions simultaneously, and predicts the total number of cases on the 14-day forecast horizon. Prediction accuracy is estimated by the mean absolute percent error (MAPE). The results show that all the considered models, both simple and more complex, have similar efficiency. The lowest error achieved is 18% MAPE for Moscow and 8% MAPE for Russia.

Moloshnikov I A, Sboev A G, Naumov A V, Zavertyaev S V, Rybka R B

2022

SIR, covid-19 forecasting, machine learning, time series analysis, total cases prediction

General General

COVID-19 detection and classification for machine learning methods using human genomic data.

In Measurement. Sensors

Coronavirus is a disease connected to coronavirus. World Health Organization has declared COVID-19 a pandemic. It has an impact on 212 nations and territories worldwide. Examining and identifying patterns in X-Ray pictures of the lungs is still necessary. Early diagnosis may help to lessen a person's virus exposure and prevent it. Manual diagnosis is a time- and labor-intensive process. Since the COVID-19 virus has the potential to infect individuals all around the world, its finding is extremely concerning. The purpose of this study is to apply machine learning to identify and classify coronaviruses. The COVID-19 is anticipated to be discriminated and categorized in CT-Lung screening and computer-aided diagnosis (CAD). Several machine learning methods, including Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function, were utilised in conjunction with clinical samples from patients who had contracted corona. While some medical professionals think an RT-PCR test is the most reliable and economical way to detect Covid-19 patients, others think a lung CT scan is more precise and less expensive. Serum samples, respiratory secretions, and whole blood samples are examples of clinical specimens. As a result of the earlier clinical evaluations, these tissues are used to assess 15 different parameters. As part of the proposed four-phase CAD system, the CT lungs screening collection is followed by a pre-processing step that enhances the appearance of ground-glass opacities (GGOs) nodules, which are initially extremely fuzzy and poorly contrasting due to the absence of contrast. These zones will be found and segmented using a modified K-means technique. Support vector machines (SVM) and radial basis functions (RBF) will be used as the input and target data for machine learning classifiers with a 50x50 pixel resolution to categorise the contaminated zones found during the detection phase (RBF). The 15 input items gathered from clinical specimens may be entered into a graphical user interface (GUI) tool that has been created to help doctors receive accurate findings.

Ahemad Mohd Thousif, Hameed Mohd Abdul, Vankdothu Ramdas

2022-Dec

Classification, Corona virus, Covid -19, Human Genomic data, Machine learning, Pneumonia, X-rays

Surgery Surgery

An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.

In Frontiers in medicine

BACKGROUND : For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging.

PURPOSE : Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making.

METHODS : AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly.

RESULTS : A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies.

CONCLUSION : We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.

Liu Chung-Feng, Hung Chao-Ming, Ko Shian-Chin, Cheng Kuo-Chen, Chao Chien-Ming, Sung Mei-I, Hsing Shu-Chen, Wang Jhi-Joung, Chen Chia-Jung, Lai Chih-Cheng, Chen Chin-Ming, Chiu Chong-Chi

2022

artificial intelligence, intensive care unit, machine learning, optimal weaning timing, weaning mechanical ventilation

General General

Are Twitter Sentiments During COVID-19 Pandemic a Critical Determinant to Predict Stock Market Movements? A Machine Learning Approach.

In Scientific African

The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences.

Jena Pradyot Ranjan, Majhi Ritanjali

2022-Nov-29

COVID-19, LSTM, Sentiment Analysis, Stock Market, Twitter

General General

Enhanced sentiment analysis regarding COVID-19 news from global channels.

In Journal of computational social science

For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

Ahmad Waseem, Wang Bang, Martin Philecia, Xu Minghua, Xu Han

2022-Nov-27

COVID-19, Deep learning, News media, Sentiment analysis, Vaccine

General General

SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies

ArXiv Preprint

Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i.e., opioid use), and evaluate the extraction rate of SDoH using cancer populations. Methods: We identified SDoH categories and attributes and developed an SDoH corpus using clinical notes from a general cancer cohort. We compared four transformer-based NLP models to extract SDoH, examined the generalizability of NLP models to a cohort of patients prescribed with opioids, and explored customization strategies to improve performance. We applied the best NLP model to extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804), and colorectal cancer (n=6,240) cohorts. Results and Conclusion: We developed a corpus of 629 cancer patients notes with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH. The Bidirectional Encoder Representations from Transformers (BERT) model achieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH concept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts. Fine-tuning the NLP models using new annotations from opioid use patients improved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The extraction rates among 19 categories of SDoH varied greatly, where 10 SDoH could be extracted from >70% of cancer patients, but 9 SDoH had a low extraction rate (<70% of cancer patients). The SODA package with pre-trained transformer models is publicly available at https://github.com/uf-hobiinformatics-lab/SDoH_SODA.

Zehao Yu, Xi Yang, Chong Dang, Prakash Adekkanattu, Braja Gopal Patra, Yifan Peng, Jyotishman Pathak, Debbie L. Wilson, Ching-Yuan Chang, Wei-Hsuan Lo-Ciganic, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu

2022-12-06

General General

Predictive modeling and analysis of air quality - Visualizing before and during COVID-19 scenarios.

In Journal of environmental management

Quality air to breathe is the basic necessity for an individual and in recent times, emission from various sources caused by human activities has resulted in substantial degradation in the air quality. This work focuses to study the inadvertent effect of COVID-19 lockdown on air pollution. Pollutants' concentration before- and during- COVID-19 lockdown is captured to understand the variation in air quality. Firstly, multi-pollutant profiling using hierarchical cluster analysis of pollutants' concentration is performed that highlights the differences in the cluster compositions between before- and during-lockdown time periods. Results show that the particulate matter (PM10 and PM2.5) in air that formed the primary cluster before lock-down, came down to close similarity with other clusters during lockdown. Secondly, predicting air quality index (AQI) based on the forecasts of pollutants' concentration is performed using neural networks, support vector machine, decision tree, random forest, and boosting algorithms. The best-fitted models representing AQI is identified separately for before- and during-lockdown time periods based on its predictive power. While deterministic method reactively evaluates present AQI when current pollutants' concentration at a particular time and place are known, this study uses the best fitted data-driven model to determine future AQIs based on the forecasts of pollutant's concentration accurately (overall RMSE<0.1 for before lockdown scenario and <0.3 for during lockdown scenario). The study contributes to visualize the variation in pollutants' concentrations between the two scenarios. The results show that the reduced economic activities during lockdown period had led to the drop in concentration of PM10 and PM2.5 by 27% and 50% on an average. The findings of this study have practical and societal implications and serve as a reference mechanism for policymakers and governing bodies to revise their actions plans for regulating individual air pollutants in the atmospheric air.

Persis Jinil, Ben Amar Amine

2022-Dec-01

Air pollution, Air quality index, COVID-19, Machine learning

General General

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients.

In EJNMMI physics

BACKGROUND : COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.

METHODS : This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested.

RESULTS : The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia.

CONCLUSION : This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT.

HIGHLIGHTS : Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.

Baeza Sonia, Gil Debora, Garcia-Olivé Ignasi, Salcedo-Pujantell Maite, Deportós Jordi, Sanchez Carles, Torres Guillermo, Moragas Gloria, Rosell Antoni

2022-Dec-05

COVID-19, CT, Neural networks, Pulmonary embolism, Radiomics, SPECT

General General

A Bayesian analysis based on multivariate stochastic volatility model: evidence from green stocks.

In Journal of combinatorial optimization

Green stocks are companies environmental protective and friendly. We test Green stock index in Shanghai Stock Exchange and China Securities Index as safe-havens for global investors. Suitable multivariate-SV model and Bayesian method are used to estimate the spillover effect between different assets among local and global markets. We choose multivariate volatility model because it can efficiently simulate the spillover effect by using machine learning MCMC method. The results show that the Environmental Protection Index (EPI) of Shanghai Stock Exchange (SSE) and China Securities Index (CSI) have no significant volatility spillover from Shanghai Stock index, S &P index, gold price, oil future prices of USA and China. During COVID-19 pandemic, we find Green stock index is a suitable safe-haven with low volatility spillover. Green stock indexes has a strongly one-way spillover to the crude oil future price. Environmentally friendly investor can use diversity green assets to provide a low risk investment portfolio in EPI stock market. The DCGCt-MSV model using machine learning of MCMC method is accurate and outperform others in Bayes parameter estimation.

Ma Ming, Zhang Jing

2023

Bayesian analysis, Green stock, Machine learning, Markov chain Monte Carlo, Spillover Effect

General General

Automated Physical Distance Estimation and Crowd Monitoring Through Surveillance Video.

In SN computer science

The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends preventing COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. The spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. This paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. The proposed system introduced the TH-YOLOv5 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. TH-YOLOv5 included another prediction head to identify objects of varying sizes. The original prediction heads are then replaced with Transformer Heads (TH) to investigate the prediction capability of the self-attention mechanism. Then, we include the convolutional block attention model (CBAM) to identify attention areas in settings with dense objects. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. We use the MS COCO and HumanCrowd, CityPersons, and Oxford Town Centre (OTC) data sets for training and testing. Experimental results demonstrate that the proposed system obtained a weighted mAP score of 89.5% and an FPS score of 29; both are computationally comparable.

Junayed Masum Shah, Islam Md Baharul

2023

COVID-19 social distancing, Crowd monitoring, Distance measurement, Human detection and tracking, Video surveillance

General General

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.

In SN computer science

Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.

Lasker Asifuzzaman, Obaidullah Sk Md, Chakraborty Chandan, Roy Kaushik

2023

COVID-19, CT, Deep learning, Machine learning, Radiological imaging, X-ray

General General

Human migration-based graph convolutional network for PM2.5 forecasting in post-COVID-19 pandemic age.

In Neural computing & applications

Due to the coronavirus disease 2019 pandemic, local authorities always implanted non-pharmaceutical interventions, such as maintaining social distance to reduce human migration. Besides, previous studies have proved that human migration highly influenced air pollution concentration in an area. Therefore, this study aims to explore whether human migration can work as a significant factor in the post-pandemic age to help PM2.5 concentration forecasting. In this work, we first analyze the variations of PM2.5 in 11 cities of Hubei from 2015 to 2020 and further compare PM2.5 trends with the migration trends of Hubei province in 2020. Experimental results indicate that the human migration indirectly affected the urban PM2.5 concentration. Then, we established a graph data structure based on the migration network describing the migration flow size between any two areas in the Hubei province and proposed a migration attentive graph convolutional network (MAGCN) for forecasting PM2.5. Combined with the migration data. The proposed model can attentively aggregate the information of neighbor nodes through migration weights. Experimental results indicate that the proposed MAGCN can forecast PM2.5 concentration accurately.

Zhan Choujun, Jiang Wei, Min Hu, Gao Ying, Tse C K

2022-Nov-22

Air pollution, COVID-19, Deep learning, Graph neural network

General General

Multi-class classification of COVID-19 documents using machine learning algorithms.

In Journal of intelligent information systems

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classification.

Rabby Gollam, Berka Petr

2022-Nov-29

COVID-19, Machine learning algorithms, Multi-class classification, Text mining

General General

A Trustworthy Framework for Medical Image Analysis with Deep Learning

ArXiv Preprint

Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the framework are both trustworthy and high-performing. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.

Kai Ma, Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stéphane Tremblay, Alexander Wong

2022-12-06

General General

Differences between remote and analog design thinking through the lens of distributed cognition.

In Frontiers in artificial intelligence

Due to the huge surge in remote work all over the world caused by the COVID-19 pandemic, today's work is largely defined by tools for information exchange as well as new complex problems that must be solved. Design Thinking offers a well-known and established methodological approach for iterative, collaborative and interdisciplinary problem solving. Still, recent circumstances shed a new light on how to facilitate Design Thinking activities in a remote rather than an analog way. Due to Design Thinking's high production of artifacts and its focus on communication and interaction between team members, the theory of Distributed Cognition, specifically the Distributed Cognition for Teamwork (DiCoT) framework, provides an interesting perspective on the recent going-remote of Design Thinking activities. For this, we first highlight differences of analog vs. remote Design Thinking by analyzing corresponding literature from the recent years. Next, we apply the DiCoT framework to those findings, pointing out implications for practical facilitation of Design Thinking activities in an analog and remote setting. Finally, we discuss opportunities through artificial intelligence-based technologies and methods.

Wolferts Daniel, Stein Elisabeth, Bernards Ann-Kathrin, Reiners René

2022

Design Thinking (DT), artificial intelligence (AI), distributed cognition for teamwork, human-computer interaction (HCI), remote work

General General

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis.

In Multimedia tools and applications

The coronavirus is an irresistible virus that generally influences the respiratory framework. It has an effective impact on the global economy specifically, on the financial movement of stock markets. Recently, an accurate stock market prediction has been of great interest to investors. A sudden change in the stock movement due to COVID -19 appearance causes some problems for investors. From this point, we propose an efficient system that applies sentiment analysis of COVID-19 news and articles to extract the final impact of COVID-19 on the financial stock market. In this paper, we propose a stock market prediction system that extracts the stock movement with the COVID spread. It is important to predict the effect of these diseases on the economy to be ready for any disease change and protect our economy. In this paper, we apply sentimental analysis to stock news headlines to predict the daily future trend of stock in the COVID-19 period. Also, we use machine learning classifiers to predict the final impact of COVID-19 on some stocks such as TSLA, AMZ, and GOOG stock. For improving the performance and quality of future trend predictions, feature selection and spam tweet reduction are performed on the data sets. Finally, our proposed system is a hybrid system that applies text mining on social media data mining on the historical stock dataset to improve the whole prediction performance. The proposed system predicts stock movement for TSLA, AMZ, and GOOG with average prediction accuracy of 90%, 91.6%, and 92.3% respectively.

Sharaf Marwa, Hemdan Ezz El-Din, El-Sayed Ayman, El-Bahnasawy Nirmeen A

2022-Nov-28

COVID-19 pandemic, Machine learning, Prediction, Sentimental analysis, Stacked-LSTM, Stock market

General General

COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker.

In Multimedia tools and applications

Wearing masks in public areas is one of the effective protection methods for people. Although it is essential to wear the facemask correctly, there are few research studies about facemask detection and tracking based on image processing. In this work, we propose a new high performance two stage facemask detector and tracker with a monocular camera and a deep learning based framework for automating the task of facemask detection and tracking using video sequences. Furthermore, we propose a novel facemask detection dataset consisting of 18,000 images with more than 30,000 tight bounding boxes and annotations for three different class labels namely respectively: face masked/incorrectly masked/no masked. We based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model to train the YOLOv4-P6-FaceMask detector and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) approach to tracking faces. We suggest using DeepSORT to track faces by ID assignment to save faces only once and create a database of no masked faces. YOLOv4-P6-FaceMask is a model with high accuracy that achieves 93% mean average precision, 92% mean average recall and the real-time speed of 35 fps on single GPU Tesla-T4 graphic card on our proposed dataset. To demonstrate the performance of the proposed model, we compare the detection and tracking results with other popular state-of-the-art models of facemask detection and tracking.

Mokeddem Mohammed Lakhdar, Belahcene Mebarka, Bourennane Salah

2022-Nov-25

Deep learning, Detection, Localization, Scaled-YOLOv4, Tracking

General General

A data-driven spatially-specific vaccine allocation framework for COVID-19.

In Annals of operations research

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies.

Hong Zhaofu, Li Yingjie, Gong Yeming, Chen Wanying

2022-Nov-22

COVID-19, Data-driven decision making, Deep learning, Spatially-specific SEIR model, Vaccine allocation

General General

Correction to: The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

In Annals of operations research

[This corrects the article DOI: 10.1007/s10479-022-05024-4.].

Elamer Ahmed A, Abdou Hussein A, Ibrahim Bassam A

2022-Nov-21

General General

The labor market in the digital era: What matters for the Gulf Cooperation Council countries?

In Frontiers in sociology

Digital transformation affects all organizations, large and small. Waves of technological change are frequent and accelerating, requiring constant adaptation by companies and their employees. Artificial intelligence, automation, and digital tools are changing the traditional organizational structure and ways of working. After the COVID-19 pandemic, the labor market has to move toward an inclusive digital transformation that braces the business systems. This paper is an attempt to explore the effect of digitalization on employment in Gulf Cooperation Council (GCC) countries and compare them to some selected advanced countries. The methodology focuses on the second-generation unit root tests and the Auto Regressive Distributed Lagged model for the period 2000-2020. The findings show a negative and significant impact of ICT on employment in the industrial and services sectors for GCC countries with a moderate adjustment speed toward the long-run equilibrium. This result is explained by the shortage of skilled workers in GCC countries compared to advanced countries, where the findings show a positive and significant effect of ICT technologies on total employment, especially in the industrial sector. The adjustment speed toward the long run is significantly higher in advanced countries than in GCC countries.

Bousrih Jihen, Elhaj Manal, Hassan Fatma

2022

GCC countries, advanced countries, digitalization, employment rate, information and communication technologies

General General

Retraction Note to: Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning.

In The Journal of supercomputing

[This retracts the article DOI: 10.1007/s11227-020-03586-3.].

Ramanathan Shalini, Ramasundaram Mohan

2022-Nov-21

General General

PulDi-COVID: Chronic Obstructive Pulmonary (Lung) Diseases With COVID-19 Classification Using Ensemble Deep Convolutional Neural Network From Chest X-Ray Images To Minimize Severity And Mortality Rates.

In Biomedical signal processing and control

BACKGROUND : and ObjectivIn the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures.

METHODS : Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several tranfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes.

RESULTS : PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases.

CONCLUSION : The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

Bhosale Yogesh H, Sridhar Patnaik K

2022-Nov-30

Biomedical engineering, COVID-19, Chronic Pulmonary disease, Convolution neural networks (CNN), Diagnosis & Classification, Ensemble deep learning, Transfer learning

Public Health Public Health

Identification of hospitalized mortality of patients with COVID-19 by machine learning models based on blood inflammatory cytokines.

In Frontiers in public health

Coronavirus disease 2019 (COVID-19) spread worldwide and presented a significant threat to people's health. Inappropriate disease assessment and treatment strategies bring a heavy burden on healthcare systems. Our study aimed to construct predictive models to assess patients with COVID-19 who may have poor prognoses early and accurately. This research performed a retrospective analysis on two cohorts of patients with COVID-19. Data from the Barcelona cohort were used as the training set, and data from the Rotterdam cohort were used as the validation set. Cox regression, logistic regression, and different machine learning methods including random forest (RF), support vector machine (SVM), and decision tree (DT) were performed to construct COVID-19 death prognostic models. Based on multiple clinical characteristics and blood inflammatory cytokines during the first day of hospitalization for the 138 patients with COVID-19, we constructed various models to predict the in-hospital mortality of patients with COVID-19. All the models showed outstanding performance in identifying high-risk patients with COVID-19. The accuracy of the logistic regression, RF, and DT models is 86.96, 80.43, and 85.51%, respectively. Advanced age and the abnormal expression of some inflammatory cytokines including IFN-α, IL-8, and IL-6 have been proven to be closely associated with the prognosis of patients with COVID-19. The models we developed can assist doctors in developing appropriate COVID-19 treatment strategies, including allocating limited medical resources more rationally and early intervention in high-risk groups.

Yu Zhixiang, Li Xiayin, Zhao Jin, Sun Shiren

2022

COVID-19, inflammatory cytokines, machine learning, outcome, prognostic models

Public Health Public Health

COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave.

In Frontiers in public health

INTRODUCTION : The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported.

METHODS : In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks.

RESULTS : We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model.

DISCUSSION : We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.

Fuente David, Hervás David, Rebollo Miguel, Conejero J Alberto, Oliver Nuria

2022

Bayesian statistical model, COVID-19, SARS-CoV-2, biomedical data science, cluster, epidemiological analysis, outbreak modeling

General General

Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers.

In Scientific reports ; h5-index 158.0

Technology is playing an important role is healthcare particularly as it relates to disease prevention and detection. This is evident in the COVID-19 era as different technologies were deployed to test, detect and track patients and ensure COVID-19 protocol compliance. The White Spot Disease (WSD) is a very contagious disease caused by virus. It is widespread among shrimp farmers due to its mode of transmission and source. Considering the growing concern about the severity of the disease, this study provides a predictive model for diagnosis and detection of WSD among shrimp farmers using visualization and machine learning algorithms. The study made use of dataset from Mendeley repository. Machine learning algorithms; Random Forest classification and CHAID were applied for the study, while Python was used for implementation of algorithms and for visualization of results. The results achieved showed high prediction accuracy (98.28%) which is an indication of the suitability of the model for accurate prediction of the disease. The study would add to growing knowledge about use of technology to manage White Spot Disease among shrimp farmers and ensure real-time prediction during and post COVID-19.

Edeh Michael Onyema, Dalal Surjeet, Obagbuwa Ibidun Christiana, Prasad B V V Siva, Ninoria Shalini Zanzote, Wajid Mohd Anas, Adesina Ademola Olusola

2022-Dec-03

General General

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.

In Artificial intelligence in medicine ; h5-index 34.0

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Reis Hatice Catal, Turk Veysel

2022-Dec

COVID-DSNet, Chest CT-scan images, Chest X-ray images, Depthwise separable convolution, SARS-CoV-2

General General

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

In Artificial intelligence in medicine ; h5-index 34.0

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

Ray Sumanta, Lall Snehalika, Mukhopadhyay Anirban, Bandyopadhyay Sanghamitra, Schönhuth Alexander

2022-Dec

COVID-19, Host directed therapy, Molecular interaction network, Node2Vec, Variational graph autoEncoder

General General

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review.

In Artificial intelligence in medicine ; h5-index 34.0

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.

Motwani Anand, Shukla Piyush Kumar, Pawar Mahesh

2022-Dec

Big data, Chronic diseases, Cloud computing, Cognitive computing, Data analytics, Edge computing, Internet-of-things, Machine learning, Remote patient monitoring, Smart healthcare monitoring, Ubiquitous computing

General General

Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Due to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety.

METHODS : The eXtreme Gradient Boosting (XGBoost1) model was applied to a dataset including data on 29,841 participants that was collected during the COVID-19 pandemic. Discriminant analyses on groups of participants with depression (DE2), anxiety (AN3), comorbid depression and anxiety (DA4), and healthy controls (HC5), were performed. All variables were selected according to their importance for classification. Further, analyses were performed with selected features to determine the best variable combination.

RESULTS : The mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86).

CONCLUSIONS : Resilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.

Tian Zhanxiao, Qu Wei, Zhao Yanli, Zhu Xiaolin, Wang Zhiren, Tan Yunlong, Jiang Ronghuan, Tan Shuping

2022-Nov-30

Anxiety, COVID-19 pandemic, Depression, Machine learning, Resilience, Social support

Ophthalmology Ophthalmology

Transforming ophthalmology in the digital century-new care models with added value for patients.

In Eye (London, England) ; h5-index 41.0

Ophthalmology faces many challenges in providing effective and meaningful eye care to an ever-increasing group of people. Even health systems that have so far been able to cope with the quantitative patient increase, due to their funding and the availability of highly qualified professionals, and improvements in practice routine efficiency, will be pushed to their limits. Further pressure on care will also be caused by new active substances for the largest group of patients with AMD, the so-called dry form. Treatment availability for this so far untreated group will increase the volume of patients 2-3 times. Without the adaptation of the care structures, this quantitative and qualitative expansion in therapy will inevitably lead to an undersupply.There is increasing scientific evidence that significant efficiency gains in the care of chronic diseases can be achieved through better networking of stakeholders in the healthcare system and greater patient involvement. Digitalization can make an important contribution here. Many technological solutions have been developed in recent years and the time is now ready to exploit this potential. The exceptional setting during the SARS-CoV-2 pandemic has shown many that new technology is available safely, quickly, and effectively. The emergency has catalyzed innovation processes and shown for post-pandemic time after that we are equipped to tackle the challenges in ophthalmic healthcare - ultimately for the benefit of patients and society.

Faes Livia, Maloca Peter M, Hatz Katja, Wolfensberger Thomas J, Munk Marion R, Sim Dawn A, Bachmann Lucas M, Schmid Martin K

2022-Dec-03

Radiology Radiology

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.

In Scientific reports ; h5-index 158.0

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

Kato Sota, Oda Masahiro, Mori Kensaku, Shimizu Akinobu, Otake Yoshito, Hashimoto Masahiro, Akashi Toshiaki, Hotta Kazuhiro

2022-Dec-02

General General

A lightweight network for COVID-19 detection in X-ray images.

In Methods (San Diego, Calif.)

The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11×11 or 3×3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method.

Shi Yong, Tang Anda, Xiao Yang, Niu Lingfeng

2022-Nov-29

COVID-19 detection, network pruning, neural network

General General

Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.

In IEEE journal of translational engineering in health and medicine

Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.

Mahmud Talha Ibn, Imran Sheikh Asif, Shahnaz Celia

2022

Saturated oxygen, attention, deep learning, excitation, feature map

General General

Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study.

In BMC medical education

BACKGROUND : Education and training are needed for nursing students using artificial intelligence-based educational programs. However, few studies have assessed the effect of using chatbots in nursing education.

OBJECTIVES : This study aimed to develop and examine the effect of an artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring in nursing college students during non-face-to-face classes during the COVID-19 pandemic.

DESIGN : This quasi-experimental study used a nonequivalent control group non-synchronized pretest-posttest design.

METHODS : The participants were 61 junior students from a nursing college located in G province of South Korea. Data were collected between November 3 and 16, 2021, and analyzed using independent t-tests.

RESULTS : The experimental group-in which the artificial intelligence chatbot program was applied-did not show statistically significant differences in knowledge (t = -0.58, p = .567), clinical reasoning competency (t = 0.75, p = .455), confidence (t = 1.13, p = .264), and feedback satisfaction (t = 1.72, p = .090), compared with the control group; however, its participants' interest in education (t = 2.38, p = .020) and self-directed learning (t = 2.72, p = .006) were significantly higher than those in the control group.

CONCLUSION : The findings of our study highlighted the potential of artificial intelligence chatbot programs as an educational assistance tool to promote nursing college students' interest in education and self-directed learning. Moreover, such programs can be effective in enhancing nursing students' skills in non-face-to face-situations caused by the ongoing COVID-19 pandemic.

Han Jeong-Won, Park Junhee, Lee Hanna

2022-Dec-01

Artificial intelligence, Chatbot program, Clinical reasoning, Data processing, Education, Nursing

Public Health Public Health

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

In Nature medicine ; h5-index 170.0

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Zhang Hao, Zang Chengxi, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Zhang Yiye, Nordvig Anna S, Schenck Edward J, Shenkman Elizabeth A, Rothman Russell L, Block Jason P, Lyman Kristin, Weiner Mark G, Carton Thomas W, Wang Fei, Kaushal Rainu

2022-Dec-01

Public Health Public Health

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

In Frontiers in public health

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Botz Jonas, Wang Danqi, Lambert Nicolas, Wagner Nicolas, Génin Marie, Thommes Edward, Madan Sumit, Coudeville Laurent, Fröhlich Holger

2022

agent-based-modeling, artificial intelligence, compartmental models, machine learning, pandemic

Public Health Public Health

Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants.

In Frontiers in public health

The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.

Grüne Barbara, Kugler Sabine, Ginzel Sebastian, Wolff Anna, Buess Michael, Kossow Annelene, Küfer-Weiß Annika, Rüping Stefan, Neuhann Florian

2022

SARS-CoV-2, classification, digital symptom diaries, health department, machine learning, prevalent virus variants, symptom combinations

Pathology Pathology

Computational approaches for network-based integrative multi-omics analysis.

In Frontiers in molecular biosciences

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Agamah Francis E, Bayjanov Jumamurat R, Niehues Anna, Njoku Kelechi F, Skelton Michelle, Mazandu Gaston K, Ederveen Thomas H A, Mulder Nicola, Chimusa Emile R, ‘t Hoen Peter A C

2022

data integration, machine learning, multi-modal network, multi-omics, network causal inference, network diffusion/propagation

General General

Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7.

In BJR open

OBJECTIVE : We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants.

METHODS : We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software.

RESULTS : The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; p < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [interquartile range (IQR):6.0-34.2%] vs 6.6% [IQR:1.2-18.3%]; p < .001). In the age-specific analysis, in patients <60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0-0.7%] vs 0.1% [IQR:0.0-0.2%]; p < .001), and severe COVID-19 was more prevalent (11.5% vs  4.9%; p = .032). Mortality rate was similar in all age groups.

CONCLUSION : Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups.

ADVANCES IN KNOWLEDGE : Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.

Simon Judit, Grodecki Kajetan, Cadet Sebastian, Killekar Aditya, Slomka Piotr, Zara Samuel James, Zsarnóczay Emese, Nardocci Chiara, Nagy Norbert, Kristóf Katalin, Vásárhelyi Barna, Müller Veronika, Merkely Béla, Dey Damini, Maurovich-Horvat Pál

2022

General General

Next-generation proteomics of serum extracellular vesicles combined with single-cell RNA sequencing identifies MACROH2A1 associated with refractory COVID-19.

In Inflammation and regeneration

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic is widespread; however, accurate predictors of refractory cases have not yet been established. Circulating extracellular vesicles, involved in many pathological processes, are ideal resources for biomarker exploration.

METHODS : To identify potential serum biomarkers and examine the proteins associated with the pathogenesis of refractory COVID-19, we conducted high-coverage proteomics on serum extracellular vesicles collected from 12 patients with COVID-19 at different disease severity levels and 4 healthy controls. Furthermore, single-cell RNA sequencing of peripheral blood mononuclear cells collected from 10 patients with COVID-19 and 5 healthy controls was performed.

RESULTS : Among the 3046 extracellular vesicle proteins that were identified, expression of MACROH2A1 was significantly elevated in refractory cases compared to non-refractory cases; moreover, its expression was increased according to disease severity. In single-cell RNA sequencing of peripheral blood mononuclear cells, the expression of MACROH2A1 was localized to monocytes and elevated in critical cases. Consistently, single-nucleus RNA sequencing of lung tissues revealed that MACROH2A1 was highly expressed in monocytes and macrophages and was significantly elevated in fatal COVID-19. Moreover, molecular network analysis showed that pathways such as "estrogen signaling pathway," "p160 steroid receptor coactivator (SRC) signaling pathway," and "transcriptional regulation by STAT" were enriched in the transcriptome of monocytes in the peripheral blood mononuclear cells and lungs, and they were also commonly enriched in extracellular vesicle proteomics.

CONCLUSIONS : Our findings highlight that MACROH2A1 in extracellular vesicles is a potential biomarker of refractory COVID-19 and may reflect the pathogenesis of COVID-19 in monocytes.

Kawasaki Takahiro, Takeda Yoshito, Edahiro Ryuya, Shirai Yuya, Nogami-Itoh Mari, Matsuki Takanori, Kida Hiroshi, Enomoto Takatoshi, Hara Reina, Noda Yoshimi, Adachi Yuichi, Niitsu Takayuki, Amiya Saori, Yamaguchi Yuta, Murakami Teruaki, Kato Yasuhiro, Morita Takayoshi, Yoshimura Hanako, Yamamoto Makoto, Nakatsubo Daisuke, Miyake Kotaro, Shiroyama Takayuki, Hirata Haruhiko, Adachi Jun, Okada Yukinori, Kumanogoh Atsushi

2022-Nov-30

COVID-19, Exosome, Liquid biopsy, MACROH2A1, Multi-omics, SARS-CoV-2

General General

Predicting Immune Escape with Pretrained Protein Language Model Embeddings

bioRxiv Preprint

Assessing the severity of new pathogenic variants requires an understanding of which mutations enable escape of the human immune response. Even single point mutations to an antigen can cause immune escape and infection by disrupting antibody binding. Recent work has modeled the effect of single point mutations on proteins by leveraging the information contained in large-scale, pretrained protein language models (PLMs). PLMs are often applied in a zero-shot setting, where the effect of each mutation is predicted based on the output of the language model with no additional training. However, this approach cannot appropriately model immune escape, which involves the interaction of two proteins--antibody and antigen--instead of one protein and requires making different predictions for the same antigenic mutation in response to different antibodies. Here, we explore several methods for predicting immune escape by building models on top of embeddings from PLMs. We evaluate our methods on a SARS-CoV-2 deep mutational scanning dataset and show that our embedding-based methods significantly outperform zero-shot methods, which have almost no predictive power. We also highlight insights gained into how best to use embeddings from PLMs to predict escape. Despite these promising results, simple statistical and machine learning baseline models that do not use pretraining perform comparably, showing that computationally expensive pretraining approaches may not be beneficial for escape prediction. Furthermore, all models perform relatively poorly, indicating that future work is necessary to improve escape prediction with or without pretrained embeddings.

Swanson, K.; Chang, H.; Zou, J.

2022-12-02

Radiology Radiology

Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees.

In Journal of infection in developing countries

INTRODUCTION : Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail.

METHODOLOGY : DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society.

RESULTS : Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features.

CONCLUSIONS : DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well.

ADVANCES IN KNOWLEDGE : DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

Dong Dawei, Luo Zujin, Zheng Yue, Liang Ying, Zhao Pengfei, Feng Linlin, Wang Dawei, Cao Ying, Zhao Zhenhao, Ma Yingmin

2022-Nov-29

COVID-19, Deep learning, asymptomatic cases, diagnostic systems, performance evaluation

General General

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

In PloS one ; h5-index 176.0

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

Rai Shesh N, Das Samarendra, Pan Jianmin, Mishra Dwijesh C, Fu Xiao-An

2022

General General

Highly Adsorptive Au-TiO2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols.

In ACS applied materials & interfaces ; h5-index 147.0

Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO2 nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO2 SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands. The ultrasensitive Au-TiO2 nanocomposites also demonstrate the successful detection of SARS-CoV-2 spike proteins in artificial respiratory aerosols at a 100 pM concentration level. The deep learning-based autoencoder, followed by the partial ablation of nondiscriminant SERS features of spike proteins, allows a quantitative assay of the 101-104 pfu/mL SARS-CoV-2 lysates (comparable to 19-29 PCR cyclic threshold from COVID-19 patients) in aerosols with an accuracy of over 98%. The Au-TiO2 SERS face mask provides a platform for breath biopsy for the detection of various biomarkers in respiratory aerosols.

Hwang Charles S H, Lee Sangyeon, Lee Sejin, Kim Hanjin, Kang Taejoon, Lee Doheon, Jeong Ki-Hun

2022-Nov-30

SARS-CoV-2, breath biopsy, machine-learning, nanocomposite, plasmonics, surface-enhanced Raman spectroscopy

General General

Emerging 0D, 1D, 2D, and 3D nanostructures for efficient point-of-care biosensing.

In Biosensors & bioelectronics: X

The recent COVID-19 infection outbreak has raised the demand for rapid, highly sensitive POC biosensing technology for intelligent health and wellness. In this direction, efforts are being made to explore high-performance nano-systems for developing novel sensing technologies capable of functioning at point-of-care (POC) applications for quick diagnosis, data acquisition, and disease management. A combination of nanostructures [i.e., 0D (nanoparticles & quantum dots), 1D (nanorods, nanofibers, nanopillars, & nanowires), 2D (nanosheets, nanoplates, nanopores) & 3D nanomaterials (nanocomposites and complex hierarchical structures)], biosensing prototype, and micro-electronics makes biosensing suitable for early diagnosis, detection & prevention of life-threatening diseases. However, a knowledge gap associated with the potential of 0D, 1D, 2D, and 3D nanostructures for the design and development of efficient POC sensing is yet to be explored carefully and critically. With this focus, this review highlights the latest engineered 0D, 1D, 2D, and 3D nanomaterials for developing next-generation miniaturized, portable POC biosensors development to achieve high sensitivity with potential integration with the internet of medical things (IoMT, for miniaturization and data collection, security, and sharing), artificial intelligence (AI, for desired analytics), etc. for better diagnosis and disease management at the personalized level.

Byakodi Manisha, Shrikrishna Narlawar Sagar, Sharma Riya, Bhansali Shekhar, Mishra Yogendra, Kaushik Ajeet, Gandhi Sonu

2022-Nov-25

0D to 3D nanomaterials, Biosensors, Efficient diagnostics, Personalized health management, Point-of-care testing, Wearable

General General

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning.

In Applied soft computing

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

Duong Linh T, Nguyen Phuong T, Iovino Ludovico, Flammini Michele

2022-Nov-24

AI Diagnosis systems, COVID-19, Chest X-ray image, Expert systems, Lung CT images

General General

Contextual counters and multimodal Deep Learning for activity-level traffic classification of mobile communication apps during COVID-19 pandemic.

In Computer networks

The COVID-19 pandemic has reshaped Internet traffic due to the huge modifications imposed to lifestyle of people resorting more and more to collaboration and communication apps to accomplish daily tasks. Accordingly, these dramatic changes call for novel traffic management solutions to adequately countermeasure such unexpected and massive changes in traffic characteristics. In this paper, we focus on communication and collaboration apps whose traffic experienced a sudden growth during the last two years. Specifically, we consider nine apps whose traffic we collect, reliably label, and publicly release as a new dataset (MIRAGE-COVID-CCMA-2022) to the scientific community. First, we investigate the capability of state-of-art single-modal and multimodal Deep Learning-based classifiers in telling the specific app, the activity performed by the user, or both. While we highlight that state-of-art solutions reports a more-than-satisfactory performance in addressing app classification (96%-98% F-measure), evident shortcomings stem out when tackling activity classification (56%-65% F-measure) when using approaches that leverage the transport-layer payload and/or per-packet information attainable from the initial part of the biflows. In line with these limitations, we design a novel set of inputs (namely Context Inputs) providing clues about the nature of a biflow by observing the biflows coexisting simultaneously. Based on these considerations, we propose Mimetic-All a novel early traffic classification multimodal solution that leverages Context Inputs as an additional modality, achieving ≥ 82 % F-measure in activity classification. Also, capitalizing the multimodal nature of Mimetic-All, we evaluate different combinations of the inputs. Interestingly, experimental results witness that Mimetic-ConSeq-a variant that uses the Context Inputs but does not rely on payload information (thus gaining greater robustness to more opaque encryption sub-layers possibly going to be adopted in the future)-experiences only ≈ 1 % F-measure drop in performance w.r.t. Mimetic-All and results in a shorter training time.

Guarino Idio, Aceto Giuseppe, Ciuonzo Domenico, Montieri Antonio, Persico Valerio, Pescapè Antonio

2022-Dec-24

COVID-19, Collaboration apps, Communication apps, Contextual counters, Deep Learning, Encrypted traffic, Multimodal techniques, Traffic classification

General General

Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data.

In Medical journal of the Islamic Republic of Iran

Background: The new coronavirus has been spreading since the beginning of 2020, and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose the COVID-19 epidemic. This study was conducted to use Machine Learning (ML) algorithms for the early detection of COVID-19 in patients. Methods: This retrospective study used data from hospitals affiliated with Shiraz University of Medical Sciences in Iran. This dataset was collected in the period March to October 2020 andcontained 10055 cases with 63 features. We selected and compared six algorithms: C4.5, support vector machine (SVM), Naive Bayes, logistic Regression (LR), Random Forest, and K-Nearest Neighbor algorithm using Rapid Miner software. The performance of algorithms was measured using evaluation metrics, such as precision, recall, accuracy, and f-measure. Results: The results of the study show that among the various used classification methods in the diagnosis of coronavirus, SVM (93.41% accuracy) and C4.5 (91.87% accuracy) achieved the highest performance. According to the C4.5 decision tree, "contact with a person who has COVID-19" was considered the most important diagnostic criterion based on the Gini index. Conclusion: We found that ML approaches enable a reasonable level of accuracy in the diagnosis of COVID-19.

Yazdani Azita, Zahmatkeshan Maryam, Ravangard Ramin, Sharifian Roxana, Shirdeli Mohammad

2022

Artificial Intelligence, COVID-19, Classification, Data mining, Machine Learning

General General

Disease-related compound identification based on deeping learning method.

In Scientific reports ; h5-index 158.0

Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

Yang Bin, Bao Wenzheng, Wang Jinglong, Chen Baitong, Iwamori Naoki, Chen Jiazi, Chen Yuehui

2022-Nov-29

General General

Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers

ArXiv Preprint

Antimicrobial resistance is one of the biggest health problem, especially in the current period of COVID-19 pandemic. Due to the unique membrane-destruction bactericidal mechanism, antimicrobial peptide-mimetic copolymers are paid more attention and it is urgent to find more potential candidates with broad-spectrum antibacterial efficacy and low toxicity. Artificial intelligence has shown significant performance on small molecule or biotech drugs, however, the higher-dimension of polymer space and the limited experimental data restrict the application of existing methods on copolymer design. Herein, we develop a universal random copolymer inverse design system via multi-model copolymer representation learning, knowledge distillation and reinforcement learning. Our system realize a high-precision antimicrobial activity prediction with few-shot data by extracting various chemical information from multi-modal copolymer representations. By pre-training a scaffold-decorator generative model via knowledge distillation, copolymer space are greatly contracted to the near space of existing data for exploration. Thus, our reinforcement learning algorithm can be adaptive for customized generation on specific scaffolds and requirements on property or structures. We apply our system on collected antimicrobial peptide-mimetic copolymers data, and we discover candidate copolymers with desired properties.

Tianyu Wu, Yang Tang

2022-11-30

Internal Medicine Internal Medicine

The prognostic utility of serum thyrotropin in hospitalized Covid-19 patients: statistical and machine learning approaches.

In Endocrine

PURPOSE : To assess the prognostic value of serum TSH in Greek patients with COVID-19 and compare it with that of commonly used prognostic biomarkers.

METHODS : Retrospective study of 128 COVID-19 in patients with no history of thyroid disease. Serum TSH, albumin, CRP, ferritin, and D-dimers were measured at admission. Outcomes were classified as "favorable" (discharge from hospital) and "adverse" (intubation or in-hospital death of any cause). The prognostic performance of TSH and other indices was assessed using binary logistic regression, machine learning classifiers, and ROC curve analysis.

RESULTS : Patients with adverse outcomes had significantly lower TSH compared to those with favorable outcomes (0.61 versus 1.09 mIU/L, p < 0.001). Binary logistic regression with sex, age, TSH, albumin, CRP, ferritin, and D-dimers as covariates showed that only albumin (p < 0.001) and TSH (p = 0.006) were significantly predictive of the outcome. Serum TSH below the optimal cut-off value of 0.5 mIU/L was associated with an odds ratio of 4.13 (95% C.I.: 1.41-12.05) for adverse outcome. Artificial neural network analysis showed that the prognostic importance of TSH was second only to that of albumin. However, the prognostic accuracy of low TSH was limited, with an AUC of 69.5%, compared to albumin's 86.9%. A Naïve Bayes classifier based on the combination of serum albumin and TSH levels achieved high prognostic accuracy (AUC 99.2%).

CONCLUSION : Low serum TSH is independently associated with adverse outcome in hospitalized Greek patients with COVID-19 but its prognostic utility is limited. The integration of serum TSH into machine learning classifiers in combination with other biomarkers enables outcome prediction with high accuracy.

Pappa E, Gourna P, Galatas G, Manti M, Romiou A, Panagiotou L, Chatzikyriakou R, Trakas N, Feretzakis G, Christopoulos C

2022-Nov-29

Artificial intelligence, Bayes classifier, COVID-19, Machine learning, Non-thyroidal illness syndrome, Thyroid stimulating hormone

General General

On the Design of Communication-Efficient Federated Learning for Health Monitoring

ArXiv Preprint

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.

Dong Chu, Wael Jaafar, Halim Yanikomeroglu

2022-11-30

General General

Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.

In Computers, informatics, nursing : CIN

Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences. Using the Twitter public streaming application programming interface, we collected tweets containing certain stress-related keywords (eg, "chronic," "constant," "stress") and then filtered the data using pre-defined text patterns. We manually annotated tweets with (without) self-report of chronic stress as positive (negative). We trained multiple classifiers and tested them via accuracy and F1 score. We annotated 4195 tweets (1560 positives, 2635 negatives), achieving an inter-annotator agreement of 0.83 (Cohen's kappa). The classifier based on Bidirectional Encoder Representation from Transformers performed the best (accuracy of 83.6% [81.0-86.1]), outperforming the second best-performing classifier (support vector machines: 76.4% [73.5-79.3]). The past tweets from the authors of positive tweets contained useful information, including sources and health impacts of chronic stress. Our study demonstrates that users' self-reported chronic stress experiences can be automatically identified on Twitter, which has a high potential for surveillance and large-scale intervention.

Yang Yuan-Chi, Xie Angel, Kim Sangmi, Hair Jessica, Al-Garadi Mohammed, Sarker Abeed

2022-Nov-28

General General

Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos

ArXiv Preprint

Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements. In the real-world long-term health monitoring scenario, the distance of the participants and their head movements usually vary by time, resulting in the inaccurate rPPG measurement due to the varying face resolution and complex motion artifacts. Different from the previous rPPG models designed for a constant distance between camera and participants, in this paper, we propose two plug-and-play blocks (i.e., physiological signal feature extraction block (PFE) and temporal face alignment block (TFA)) to alleviate the degradation of changing distance and head motion. On one side, guided with representative-area information, PFE adaptively encodes the arbitrary resolution facial frames to the fixed-resolution facial structure features. On the other side, leveraging the estimated optical flow, TFA is able to counteract the rPPG signal confusion caused by the head movement thus benefit the motion-robust rPPG signal recovery. Besides, we also train the model with a cross-resolution constraint using a two-stream dual-resolution framework, which further helps PFE learn resolution-robust facial rPPG features. Extensive experiments on three benchmark datasets (UBFC-rPPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. One highlight is that with PFE and TFA, the off-the-shelf spatio-temporal rPPG models can predict more robust rPPG signals under both varying face resolution and severe head movement scenarios. The codes are available at https://github.com/LJW-GIT/Arbitrary_Resolution_rPPG.

Jianwei Li, Zitong Yu, Jingang Shi

2022-11-30

General General

Reactive-diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China.

In Physica A

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive-diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

Li Ruqi, Song Yurong, Wang Haiyan, Jiang Guo-Ping, Xiao Min

2022-Nov-21

City clusters, Human mobility networks, Intervention, Reactive-diffusion epidemic model, Threshold behavior

Surgery Surgery

The optimal use of colon capsule endoscopes in clinical practice.

In Therapeutic advances in chronic disease

Colon capsule endoscopy (CCE) has been available for nearly two decades but has grappled with being an equal diagnostic alternative to optical colonoscopy (OC). Due to the COVID-19 pandemic, CCE has gained more foothold in clinical practice. In this cutting-edge review, we aim to present the existing knowledge on the pros and cons of CCE and discuss whether the modality is ready for a larger roll-out in clinical settings. We have included clinical trials and reviews with the most significant impact on the current position of CCE in clinical practice and discuss the challenges that persist and how they could be addressed to make CCE a more sustainable imaging modality with an adenoma detection rate equal to OC and a low re-investigation rate by a proper preselection of suitable populations. CCE is embedded with a very low risk of severe complications and can be performed in the patient's home as a pain-free procedure. The diagnostic accuracy is found to be equal to OC. However, a significant drawback is low completion rates eliciting a high re-investigation rate. Furthermore, the bowel preparation before CCE is extensive due to the high demand for clean mucosa. CCE is currently not suitable for large-scale implementation in clinical practice mainly due to high re-investigation rates. By a better preselection before CCE and the implantation of artificial intelligence for picture and video analysis, CCE could be the alternative to OC needed to move away from in-hospital services and relieve long-waiting lists for OC.

Bjørsum-Meyer Thomas, Koulaouzidis Anastasios, Baatrup Gunnar

2022

artificial intelligence, capsule endoscopy, colonic disease, endoscopy, routine diagnostic test, wireless capsule endoscopy

General General

Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic.

In Socio-economic planning sciences

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the public catering system under greater pressure, revealing weaknesses within the system and generation of food waste due to rapidly changing consumption patterns. In times of crisis, it is especially important to conserve resources and allocate existing resources to areas where they can be of most use, but this poses significant challenges. This study evaluated the potential of a forecasting model to predict guest attendance during the start and throughout the pandemic. This was done by collecting data on guest attendance in Swedish school and preschool catering establishments before and during the pandemic, and using a machine learning approach to predict future guest attendance based on historical data. Comparison of various learning methods revealed that random forest produced more accurate forecasts than a simple artificial neural network, with conditional mean absolute prediction error of < 0.15 for the trained dataset. Economic savings were obtained by forecasting compared with a no-plan scenario, supporting selection of the random forest approach for effective forecasting of meal planning. Overall, the results obtained using forecasting models for meal planning in times of crisis confirmed their usefulness. Continuous use can improve estimates for the test period, due to the agile and flexible nature of these models. This is particularly important when guest attendance is unpredictable, so that production planning can be optimized to reduce food waste and contribute to a more sustainable and resilient food system.

Malefors Christopher, Secondi Luca, Marchetti Stefano, Eriksson Mattias

2022-Aug

Food waste school kitchens forecasting random-forest system optimization

General General

Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization.

In AI & society

COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803.

Jojoa Mario, Eftekhar Parvin, Nowrouzi-Kia Behdin, Garcia-Zapirain Begonya

2022-Nov-21

Deep learning, DistilBERT, Natural language processing, Sentiment analysis, Transformer

General General

Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images.

In New generation computing

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., "COVID-19", "Pneumonia", and "Normal". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.

Chatterjee Sankhadeep, Maity Soumyajit, Bhattacharjee Mayukh, Banerjee Soumen, Das Asit Kumar, Ding Weiping

2022-Nov-19

COVID-19, Class imbalance, Oversampling, Undersampling, Variational autoencoder

General General

Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.

In New generation computing

In the past few years, most of the work has been done around the classification of covid-19 using different images like CT-scan, X-ray, and ultrasound. But none of that is capable enough to deal with each of these image types on a single common platform and can identify the possibility that a person is suffering from COVID or not. Thus, we realized there should be a platform to identify COVID-19 in CT-scan and X-ray images on the fly. So, to fulfill this need, we proposed an AI model to identify CT-scan and X-ray images from each other and then use this inference to classify them of COVID positive or negative. The proposed model uses the inception architecture under the hood and trains on the open-source extended covid-19 dataset. The dataset consists of plenty of images for both image types and is of size 4 GB. We achieved an accuracy of 100%, average macro-Precision of 100%, average macro-Recall of 100%, average macro f1-score of 100%, and AUC score of 99.6%. Furthermore, in this work, cloud-based architecture is proposed to massively scale and load balance as the Number of user requests rises. As a result, it will deliver a service with minimal latency to all users.

Dubey Ankit Kumar, Mohbey Krishna Kumar

2022-Nov-20

Area under curve, Artificial intelligence, COVID 19, Computed tomography, Inception, Transfer learning

General General

Artificial intelligence-based internet hospital pharmacy services in China: Perspective based on a case study.

In Frontiers in pharmacology

Background: Recently, internet hospitals have been emerging in China, saving patients time and money during the COVID-19 pandemic. In addition, pharmacy services that link doctors and patients are becoming essential in improving patient satisfaction. However, the existing internet hospital pharmacy service mode relies primarily on manual operations, making it cumbersome, inefficient, and high-risk. Objective: To establish an internet hospital pharmacy service mode based on artificial intelligence (AI) and provide new insights into pharmacy services in internet hospitals during the COVID-19 pandemic. Methods: An AI-based internet hospital pharmacy service mode was established. Initially, prescription rules were formulated and embedded into the internet hospital system to review the prescriptions using AI. Then, the "medicine pick-up code," which is a Quick Response (QR) code that represents a specific offline self-pick-up order, was created. Patients or volunteers could pick up medications at an offline hospital or drugstore by scanning the QR code through the window and wait for the dispensing machine or pharmacist to dispense the drugs. Moreover, the medication consultation function was also operational. Results: The established internet pharmacy service mode had four major functional segments: online drug catalog search, prescription preview by AI, drug dispensing and distribution, and AI-based medication consultation response. The qualified rate of AI preview was 83.65%. Among the 16.35% inappropriate prescriptions, 49% were accepted and modified by physicians proactively and 51.00% were passed after pharmacists intervened. The "offline self-pick-up" mode was preferred by 86% of the patients for collecting their medication in the internet hospital, which made the QR code to be fully applied. A total of 426 medication consultants were served, and 48.83% of them consulted outside working hours. The most frequently asked questions during consultations were about the internet hospital dispensing process, followed by disease diagnosis, and patient education. Therefore, an AI-based medication consultation was proposed to respond immediately when pharmacists were unavailable. Conclusion: The established AI-based internet hospital pharmacy service mode could provide references for pharmacy departments during the COVID-19 pandemic. The significance of this study lies in ensuring safe/rational use of medicines and raising pharmacists' working efficiency.

Bu Fengjiao, Sun Hong, Li Ling, Tang Fengmin, Zhang Xiuwen, Yan Jingchao, Ye Zhengqiang, Huang Taomin

2022

artificial intelligence, internet hospital, medication pick-up code, online medication consultation, prescription preview

General General

Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting.

In Computational intelligence and neuroscience

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios.

Muñoz-Organero Mario

2022

General General

An ensemble prediction model for COVID-19 mortality risk.

In Biology methods & protocols

BACKGROUND : It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts.

METHODS : We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function, and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features.

RESULTS : Finally, we identified 14 key clinical features whose combination reached a good predictive performance of area under the receiver operating characteristic curve 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15 790 patients.

CONCLUSIONS : Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.

Li Jie, Li Xin, Hutchinson John, Asad Mohammad, Liu Yinghui, Wang Yadong, Wang Edwin

2022

COVID-19, SARS-CoV-2, cohort studies, mortality prediction, prognosis

General General

A semi-supervised Bayesian mixture modelling approach for joint batch correction and classification

bioRxiv Preprint

Systematic differences between batches of samples present significant challenges when analysing biological data. Such batch effects are well-studied and are liable to occur in any setting where multiple batches are assayed. Many existing methods for accounting for these have focused on high-dimensional data such as RNA-seq and have assumptions that reflect this. Here we focus on batch-correction in low-dimensional classification problems. We propose a semi-supervised Bayesian generative classifier based on mixture models that jointly predicts class labels and models batch effects. Our model allows observations to be probabilistically assigned to classes in a way that incorporates uncertainty arising from batch effects. By simultaneously inferring the classification and the batch-correction our method is more robust to dependence between batch and class than pre-processing steps such as ComBat. We explore two choices for the within-class densities: the multivariate normal and the multivariate t. A simulation study demonstrates that our method performs well compared to popular off-the-shelf machine learning methods and is also quick; performing 15,000 iterations on a dataset of 750 samples with 2 measurements each in 11.7 seconds for the MVN mixture model and 14.7 seconds for the MVT mixture model. We further validate our model on gene expression data where cell type (class) is known and simulate batch effects. We apply our model to two datasets generated using the enzyme-linked immunosorbent assay (ELISA), a spectrophotometric assay often used to screen for antibodies. The examples we consider were collected in 2020 and measure seropositivity for SARS-CoV-2. We use our model to estimate seroprevalence in the populations studied. We implement the models in C++ using a Metropolis-within-Gibbs algorithm, available in the R package batchmix. Scripts to recreate our analysis are at https://github.com/stcolema/BatchClassifierPaper.

Coleman, S.; Nicholls, K. C.; Castro Dopico, X.; Karlsson Hedestam, G. B.; Kirk, P. D.; Wallace, C.

2022-11-29

Public Health Public Health

Machine learning based regional epidemic transmission risks precaution in digital society.

In Scientific reports ; h5-index 158.0

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Shi Zhengyu, Qian Haoqi, Li Yao, Wu Fan, Wu Libo

2022-Nov-28

Public Health Public Health

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

In PloS one ; h5-index 176.0

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

Hajdu Nandor, Schmidt Kathleen, Acs Gergely, Röer Jan P, Mirisola Alberto, Giammusso Isabella, Arriaga Patrícia, Ribeiro Rafael, Dubrov Dmitrii, Grigoryev Dmitry, Arinze Nwadiogo C, Voracek Martin, Stieger Stefan, Adamkovic Matus, Elsherif Mahmoud, Kern Bettina M J, Barzykowski Krystian, Ilczuk Ewa, Martončik Marcel, Ropovik Ivan, Ruiz-Fernandez Susana, Baník Gabriel, Ulloa José Luis, Aczel Balazs, Szaszi Barnabas

2022

Cardiology Cardiology

Advances in Cardiac Electrophysiology.

In Circulation. Arrhythmia and electrophysiology

Despite the global COVID-19 pandemic, during the past 2 years, there have been numerous advances in our understanding of arrhythmia mechanisms and diagnosis and in new therapies. We increased our understanding of risk factors and mechanisms of atrial arrhythmias, the prediction of atrial arrhythmias, response to treatment, and outcomes using machine learning and artificial intelligence. There have been new technologies and techniques for atrial fibrillation ablation, including pulsed field ablation. There have been new randomized trials in atrial fibrillation ablation, giving insight about rhythm control, and long-term outcomes. There have been advances in our understanding of treatment of inherited disorders such as catecholaminergic polymorphic ventricular tachycardia. We have gained new insights into the recurrence of ventricular arrhythmias in the setting of various conditions such as myocarditis and inherited cardiomyopathic disorders. Novel computational approaches may help predict occurrence of ventricular arrhythmias and localize arrhythmias to guide ablation. There are further advances in our understanding of noninvasive radiotherapy. We have increased our understanding of the role of His bundle pacing and left bundle branch area pacing to maintain synchronous ventricular activation. There have also been significant advances in the defibrillators, cardiac resynchronization therapy, remote monitoring, and infection prevention. There have been advances in our understanding of the pathways and mechanisms involved in atrial and ventricular arrhythmogenesis.

Piccini Jonathan P, Russo Andrea M, Sharma Parikshit S, Kron Jordana, Tzou Wendy, Sauer William, Park David S, Birgersdotter-Green Ulrika, Frankel David S, Healey Jeff S, Hummel John, Koruth Jacob, Linz Dominik, Mittal Suneet, Nair Devi G, Nattel Stanley, Noseworthy Peter A, Steinberg Benjamin A, Trayanova Natalia A, Wan Elaine Y, Wissner Erik, Zeitler Emily P, Wang Paul J

2022-Nov-28

arrhythmias, atrial fibrillation, cardiac electrophysiology, implantable defibrillators, ventricular tachycardia

Cardiology Cardiology

Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance.

METHODS : We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March-November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles).

RESULTS : Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763-0.731 [RF-L1]); Brier scores: 0.184-0.197 [LR-L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events.

CONCLUSIONS : Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic.

TRIAL REGISTRATION NUMBER : NCT04659187.

Sievering Aaron W, Wohlmuth Peter, Geßler Nele, Gunawardene Melanie A, Herrlinger Klaus, Bein Berthold, Arnold Dirk, Bergmann Martin, Nowak Lorenz, Gloeckner Christian, Koch Ina, Bachmann Martin, Herborn Christoph U, Stang Axel

2022-Nov-28

COVID-19, Clinical decision-making, Critical event prediction, Machine learning, Predictive models

Public Health Public Health

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.

In Experimental biology and medicine (Maywood, N.J.)

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Shaban-Nejad Arash, Michalowski Martin, Bianco Simone, Brownstein John S, Buckeridge David L, Davis Robert L

2022-Nov-25

AI governance, COVID-19, Health AI, artificial intelligence, ethical AI, human-centered AI, machine learning, multimodal AI

General General

Epidemiology and clinical features of SARS-CoV-2 infection in hospitalized children across four waves in Hungary: A retrospective, comparative study from March 2020 to December 2021.

In Health science reports

BACKGROUND AND AIMS : From 2019 till the present, infections induced by the novel coronavirus and its mutations have posed a new challenge for healthcare. However, comparative studies on pediatric infections throughout waves are few. During four different pandemic waves, we intended to investigate the clinical and epidemiological characteristic of the pediatric population hospitalized for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus infection.

METHODS : Between March 2020 and December 2021, we performed our retrospective research on children infected with the SARS-CoV-2 virus at the University of Szeged. We analyzed the data of all patients who required hospitalization due to positive results of SARS-CoV-2 tests (Nucleic Acid Amplification Test or rapid antigen test). Data analysis included demographic data, medical history, clinical findings, length of hospitalization, and complications, using medical records.

RESULTS : In this study, data from 358 coronavirus-infected children were analyzed. The most affected age group was children over 1 month and under 1 year (30.2%). The highest number of cases was recorded in the fourth wave (53.6%). Fever (65.6%), cough (51.4%), nasal discharge (35.3%), nausea and vomiting (31.3%), and decreased oral intake (28.9%) were the most common symptoms. The most common complications were dehydration (50.5%), pneumonia (14.9%), and bronchitis/bronchiolitis (14.5%). Based on RR values, there are considerable differences in the prevalence of the symptoms and complications between the different age groups and waves. Cox proportional hazard model analyzes showed that fever and tachypnoea had a relevant effect on days to recovery.

CONCLUSIONS : We found trends similar to those previously published, overall statistics. The proportion of children requiring hospitalization varied from wave to wave, with the fourth wave affecting the Hungarian child population the most. Our findings suggest that hospitalization time is unrelated to age, but that certain symptoms (fever and tachypnoea) are associated with longer hospitalization. The onset of certain symptoms may differ by age group.

Takács Andrea T, Bukva Mátyás, Gavallér Gabriella, Kapus Katalin, Rózsa Mária, Bán-Gagyi Boglárka, Sinkó Mária, Szűcs Dániel, Terhes Gabriella, Bereczki Csaba

2022-Nov

COVID‐19, SARS‐CoV‐2 infection, children, hospitalization wave

Public Health Public Health

Prediction of Global Psychological Stress and Coping Induced by the COVID-19 Outbreak: A Machine Learning Study.

In Alpha psychiatry

BACKGROUND : Artificial intelligence and machine learning have enormous potential to deal efficiently with a wide range of issues that traditional sciences may be unable to address. Neuroscience, particularly psychiatry, is one of the domains that could potentially benefit from artificial intelligence and machine learning. This study aims to predict Stress and assess Coping with stress mechanisms during the COVID-19 pandemic and, therefore, help establish a successful intervention to manage distress.

METHODS : COVIDiSTRESS global survey data was used in this study and comprised 70 652 respondents after pre-processing. Binary classification is performed for predicting Stress and Coping with stress, while 2 ensemble machine learning algorithms, deep super learner and cascade deep forest, and state-of-the-art methods are explored for classification. Correlation attribute evaluation is used for feature significance. Statistical analysis, such as Cronbach's alpha, demographic statistics, Pearson's correlation coefficient, independent sample t-test, and 95% CI, is also performed.

RESULTS : Globally, females, the younger population, and those in COVID-19 risk groups are observed to possess higher levels of stress. Trust, Loneliness, and Distress are found to be the primary predictors of Stress, whereas the significant predictors for coping with stress are identified as Social Provision, Extroversion, and Agreeableness. Deep super learner and cascade deep forest outperformed the state-of-the-art methods with an accuracy of up to 88.42%.

CONCLUSIONS : By comparing different classifiers, we can conclude that multi-layer ensemble outperforms all. Another aim of this study, is the ability to regulate demographic and negative psychological states with a goal of medical interventions and to work towards building multiple coping strategies to reduce stress and promote resilience and recovery from COVID-19.

Prerna Tigga Neha, Garg Shruti

2022-Jul

COVID-19, coping, machine learning, public health, stress

General General

COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes.

In Biomedical optics express

We present an automated method for COVID-19 screening using the intra-patient population distributions of bio-optical attributes extracted from digital holographic microscopy reconstructed red blood cells. Whereas previous approaches have aimed to identify infection by classifying individual cells, here, we propose an approach to incorporate the attribute distribution information from the population of a given human subjects' cells into our classification scheme and directly classify subjects at the patient level. To capture the intra-patient distribution information in a generalized way, we propose an approach based on the Bag-of-Features (BoF) methodology to transform histograms of bio-optical attribute distributions into feature vectors for classification via a linear support vector machine. We compare our approach with simpler classifiers directly using summary statistics such as mean, standard deviation, skewness, and kurtosis of the distributions. We also compare to a k-nearest neighbor classifier using the Kolmogorov-Smirnov distance as a distance metric between the attribute distributions of each subject. We lastly compare our approach to previously published methods for classification of individual red blood cells. In each case, the methodology proposed in this paper provides the highest patient classification performance, correctly classifying 22 out of 24 individuals and achieving 91.67% classification accuracy with 90.00% sensitivity and 92.86% specificity. The incorporation of distribution information for classification additionally led to the identification of a singular temporal-based bio-optical attribute capable of highly accurate patient classification. To the best of our knowledge, this is the first report of a machine learning approach using the intra-patient probability distribution information of bio-optical attributes obtained from digital holographic microscopy for disease screening.

O’Connor Timothy, Javidi Bahram

2022-Oct-01

General General

Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images.

In Computers in biology and medicine

Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.

Ukwuoma Chiagoziem C, Qin Zhiguang, Agbesi Victor K, Cobbinah Bernard M, Yussif Sophyani B, Abubakar Hassan S, Lemessa Bona D

2022-Nov-18

Attention mechanism, COVID-19 detection, Chest X-rays images, Deep learning, Feature extraction, Global second-order pooling

Public Health Public Health

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

In Healthcare (Basel, Switzerland)

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Al-Garadi Mohammed Ali, Yang Yuan-Chi, Sarker Abeed

2022-Nov-12

COVID-19, deep learning, health applications, machine learning, natural language processing

General General

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.

In Bioengineering (Basel, Switzerland)

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model's ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

Ukwuoma Chiagoziem C, Qin Zhiguang, Heyat Md Belal Bin, Akhtar Faijan, Smahi Abla, Jackson Jehoiada K, Furqan Qadri Syed, Muaad Abdullah Y, Monday Happy N, Nneji Grace U

2022-Nov-18

COVID-19, artificial intelligence, automatic detection, chest X-rays images, epidemic, feature extraction, lung disease, pneumonia

General General

Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout.

In Bioengineering (Basel, Switzerland)

Machine learning models are renowned for their high dependency on a large corpus of data in solving real-world problems, including the recent COVID-19 pandemic. In practice, data acquisition is an onerous process, especially in medical applications, due to lack of data availability for newly emerged diseases and privacy concerns. This study introduces a data synthesization framework (sRD-GAN) that generates synthetic COVID-19 CT images using a novel stacked-residual dropout mechanism (sRD). sRD-GAN aims to alleviate the problem of data paucity by generating synthetic lung medical images that contain precise radiographic annotations. The sRD mechanism is designed using a regularization-based strategy to facilitate perceptually significant instance-level diversity without content-style attribute disentanglement. Extensive experiments show that sRD-GAN can generate exceptional perceptual realism on COVID-19 CT images examined by an experiment radiologist, with an outstanding Fréchet Inception Distance (FID) of 58.68 and Learned Perceptual Image Patch Similarity (LPIPS) of 0.1370 on the test set. In a benchmarking experiment, sRD-GAN shows superior performance compared to GAN, CycleGAN, and one-to-one CycleGAN. The encouraging results achieved by sRD-GAN in different clinical cases, such as community-acquired pneumonia CT images and COVID-19 in X-ray images, suggest that the proposed method can be easily extended to other similar image synthetization problems.

Lee Kin Wai, Chin Renee Ka Yin

2022-Nov-16

COVID-19, chest computed tomography, generative adversarial networks, image synthesis

General General

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review.

In Small methods

Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at-home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone-tethered optical platform through portable, automated, and deep-learning-enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone-based microscopes and lens-free holographic microscopy are introduced, and deep learning-based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID-19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability.

Kim Kisoo, Lee Won Gu

2022-Nov-24

Smartphone-Tethered Optical Platform, deep learning-enhanced microscopic imaging, lens-free holographic imaging, personalized diagnostics, portable COVID screening system, smartphone-based microscopy

General General

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

In Network (Bristol, England)

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

Suba Saravanan, Muthulakshmi M

2022-Nov-24

AI tools deep learning approaches, COVID-19, chest radiography images, machine learning approaches

Public Health Public Health

COVID-19 smart surveillance: Examination of Knowledge of Apps and mobile thermometer detectors (MTDs) in a high-risk society.

In Digital health

BACKGROUND : Technological innovations gained momentum and supported COVID-19 intelligence surveillance among high-risk populations globally. We examined technology surveillance using mobile thermometer detectors (MTDs), knowledge of App, and self-efficacy as a means of sensing body temperature as a measure of COVID-19 risk mitigation. In a cross-sectional survey, we explored COVID-19 risk mitigation, mobile temperature detectable by network syndromic surveillance mobility, detachable from clinicians, and laboratory diagnoses to elucidate the magnitude of community monitoring.

MATERIALS AND METHODS : In a cross-sectional survey, we create in-depth comprehension of risk mitigation, mobile temperature Thermometer detector, and other variables for surveillance and monitoring among 850 university students and healthcare workers. An applied structural equation model was adopted for analysis with Amos v.24. We established that mobile usability knowledge of APP could effectively aid in COVID-19 intelligence risk mitigation. Moreover, both self-efficacy and mobile temperature positively strengthened data visualization for public health decision-making.

RESULTS : The algorithms utilize a validated point-of-center test to ascertain the HealthCode scanning system for a positive or negative COVID-19 notification. The MTD is an alternative personal self-testing procedure used to verify temperature rates based on previous SARS-CoV-2 and future mobility digital health. Personal self-care of MTD mobility and knowledge of mHealth apps can specifically manage COVID-19 mitigation in high or low terrestrial areas. We found mobile usability, mobile self-efficacy, and app knowledge were statistically significant to COVID-19 mitigation. Additionally, interaction strengthened the positive relationship between self-efficacy and COVID-19. Data aggregation is entrusted with government database agencies, using natural language processing and machine learning mechanisms to validate and analyze.

CONCLUSION : The study shows that temperature thermometer detectors, mobile usability, and knowledge of App enhanced COVID-19 risk mitigation in a high or low-risk environment. The standardizing dataset is necessary to ensure privacy and security preservation of data ethics.

Sayibu Muhideen, Chu Jianxun, Tosin Yinka Akintunde, Rufai Olayemi Hafeez, Shahani Riffat, Jin M A

2022

COVID-19 surveillance, knowledge of app, mobile intelligence, mobile thermometer detectors (MTD), risk mitigation

Public Health Public Health

Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy.

In Epidemiolgia (Basel, Switzerland)

Vaccination strategies to lessen the impact of the spread of a disease are fundamental to public health authorities and policy makers. The socio-economic benefit of full return to normalcy is the core of such strategies. In this paper, a COVID-19 vaccination model with efficacy rate is developed and analyzed. The epidemiological parameters of the model are learned via a feed-forward neural network. A hybrid approach that combines residual neural network with variants of recurrent neural network is implemented and analyzed for reliable and accurate prediction of daily cases. The error metrics and a k-fold cross validation with random splitting reveal that a particular type of hybrid approach called residual neural network with gated recurrent unit is the best hybrid neural network architecture. The data-driven simulations confirm the fact that the vaccination rate with higher efficacy lowers the infectiousness and basic reproduction number. As a study case, COVID-19 data for the state of Tennessee in USA is used.

Torku Thomas K, Khaliq Abdul Q M, Furati Khaled M

2021-Nov-30

COVID-19, RNN, ResNet, data-driven, deep learning, k-fold cross validation, vaccination strategy

General General

Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction.

In eLife

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8-87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5-91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Mollentze Nardus, Keen Deborah, Munkhbayar Uuriintuya, Biek Roman, Streicker Daniel G

2022-Nov-23

ACE2, SARS-CoV-2, ecology, host range, infectious disease, microbiology, viruses

General General

Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.

In Biomedical signal processing and control

Segmentation of COVID-19 infection is a challenging task due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, especially for small infection regions. COV-TransNet is presented to achieve high-precision segmentation of COVID-19 infection regions in this paper. The proposed segmentation network is composed of the auxiliary branch and the backbone branch. The auxiliary branch network adopts transformer to provide global information, helping the convolution layers in backbone branch to learn specific local features better. A multi-scale feature attention module is introduced to capture contextual information and adaptively enhance feature representations. Specially, a high internal resolution is maintained during the attention calculation process. Moreover, feature activation module can effectively reduce the loss of valid information during sampling. The proposed network can take full advantage of different depth and multi-scale features to achieve high sensitivity for identifying lesions of varied sizes and locations. We experiment on several datasets of the COVID-19 lesion segmentation task, including COVID-19-CT-Seg, UESTC-COVID-19, MosMedData and COVID-19-MedSeg. Comprehensive results demonstrate that COV-TransNet outperforms the existing state-of-the-art segmentation methods and achieves better segmentation performance for multi-scale lesions.

Peng Yanjun, Zhang Tong, Guo Yanfei

2023-Feb

Attention mechanism, COVID-19, CT images, Deep learning, Semantic segmentation, Transformer

Ophthalmology Ophthalmology

Ischemic stroke of unclear aetiology: a case-by-case analysis and call for a multi-professional predictive, preventive and personalised approach.

In The EPMA journal

Due to the reactive medical approach applied to disease management, stroke has reached an epidemic scale worldwide. In 2019, the global stroke prevalence was 101.5 million people, wherefrom 77.2 million (about 76%) suffered from ischemic stroke; 20.7 and 8.4 million suffered from intracerebral and subarachnoid haemorrhage, respectively. Globally in the year 2019 - 3.3, 2.9 and 0.4 million individuals died of ischemic stroke, intracerebral and subarachnoid haemorrhage, respectively. During the last three decades, the absolute number of cases increased substantially. The current prevalence of stroke is 110 million patients worldwide with more than 60% below the age of 70 years. Prognoses by the World Stroke Organisation are pessimistic: globally, it is predicted that 1 in 4 adults over the age of 25 will suffer stroke in their lifetime. Although age is the best known contributing factor, over 16% of all strokes occur in teenagers and young adults aged 15-49 years and the incidence trend in this population is increasing. The corresponding socio-economic burden of stroke, which is the leading cause of disability, is enormous. Global costs of stroke are estimated at 721 billion US dollars, which is 0.66% of the global GDP. Clinically manifested strokes are only the "tip of the iceberg": it is estimated that the total number of stroke patients is about 14 times greater than the currently applied reactive medical approach is capable to identify and manage. Specifically, lacunar stroke (LS), which is characteristic for silent brain infarction, represents up to 30% of all ischemic strokes. Silent LS, which is diagnosed mainly by routine health check-up and autopsy in individuals without stroke history, has a reported prevalence of silent brain infarction up to 55% in the investigated populations. To this end, silent brain infarction is an independent predictor of ischemic stroke. Further, small vessel disease and silent lacunar brain infarction are considered strong contributors to cognitive impairments, dementia, depression and suicide, amongst others in the general population. In sub-populations such as diabetes mellitus type 2, proliferative diabetic retinopathy is an independent predictor of ischemic stroke. According to various statistical sources, cryptogenic strokes account for 15 to 40% of the entire stroke incidence. The question to consider here is, whether a cryptogenic stroke is fully referable to unidentifiable aetiology or rather to underestimated risks. Considering the latter, translational research might be of great clinical utility to realise innovative predictive and preventive approaches, potentially benefiting high risk individuals and society at large. In this position paper, the consortium has combined multi-professional expertise to provide clear statements towards the paradigm change from reactive to predictive, preventive and personalised medicine in stroke management, the crucial elements of which are:Consolidation of multi-disciplinary expertise including family medicine, predictive and in-depth diagnostics followed by the targeted primary and secondary (e.g. treated cancer) prevention of silent brain infarctionApplication of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transitionApplication of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patternsApplication of innovative screening programmes which adequately consider the needs of young populations.

Golubnitschaja Olga, Potuznik Pavel, Polivka Jiri, Pesta Martin, Kaverina Olga, Pieper Claus C, Kropp Martina, Thumann Gabriele, Erb Carl, Karabatsiakis Alexander, Stetkarova Ivana, Polivka Jiri, Costigliola Vincenzo

2022-Nov-17

Blood pressure, Blood–brain barrier breakdown, Body mass index, COVID-19, Cancer, Coagulation, Connective tissue impairments, Diabetes comorbidities, Endothelial dysfunction, Endothelin-1, Flammer Syndrome phenotype, Health policy, Health risk assessment, Health-to-disease transition, Hypoxia-reperfusion, Individualised protection, Ischemic stroke, Lacunar stroke, Mental health, Metastasis, Normal-tension glaucoma, Optic nerve degeneration, Paradigm change, Pre-pregnancy check-up, Predictive preventive personalised medicine (PPPM / 3PM), Primary care, Pro-inflammation, Retinal microvascular abnormalities, Screening, Secondary care, Silent brain infarct, Small vessel disease, Stress, Sub-optimal health, Systemic effects, Thromboembolism, Vascular stiffness, Vasospasm, Young populations

General General

Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19.

In Swarm and evolutionary computation

The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex. Moreover, the actual demands are often obtained late, and hence the time duration for solution calculation and mask delivery is often very limited. Based on our practical experience of medical mask delivery in response to COVID-19 in China, we present a hybrid machine learning and heuristic optimization method, which uses a deep learning model to predict the demand of each region, schedules first-echelon vehicles to pre-distribute the predicted number of masks from depot(s) to regional facilities in advance, reassigns demand points among different regions to balance the deviations of predicted demands from actual demands, and finally routes second-echelon vehicles to efficiently deliver masks to the demand points in each region. For the subproblems of demand point reassignment and two-batch routing whose complexities are significantly lower, we propose variable neighborhood tabu search heuristics to efficiently solve them. Application of the proposed method in emergency mask delivery in three megacities in China during the peak of COVID-19 demonstrated its significant performance advantages over other methods without pre-distribution or reassignment. We also discuss key success factors and lessons learned to facilitate the extension of our method to a wider range of problems.

Chen Xin, Yan Hong-Fang, Zheng Yu-Jun, Karatas Mumtaz

2022-Nov-16

Heuristic optimization, Machine learning, Pre-distribution, Tabu search, Variable neighborhood, Vehicle routing

General General

Future forecasting prediction of Covid-19 using hybrid deep learning algorithm.

In Multimedia tools and applications

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Yenurkar Ganesh, Mal Sandip

2022-Nov-18

As well as mayfly optimization (MO) algorithm, Corona disease, Feature extraction, Feature selection, GoogleNet, Hybrid deep learning model, ResNet

General General

ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection.

In Information sciences

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.

Sakr Ahmed S, Pławiak Paweł, Tadeusiewicz Ryszard, Pławiak Joanna, Sakr Mohamed, Hammad Mohamed

2023-Jan

CNN, COVID-19, Deep learning, ECG, End-to-end

General General

Explaining COVID-19 diagnosis with Taylor decompositions.

In Neural computing & applications

The COVID-19 pandemic has devastated the entire globe since its first appearance at the end of 2019. Although vaccines are now in production, the number of contaminations remains high, thus increasing the number of specialized personnel that can analyze clinical exams and points out the final diagnosis. Computed tomography and X-ray images are the primary sources for computer-aided COVID-19 diagnosis, but we still lack better interpretability of such automated decision-making mechanisms. This manuscript presents an insightful comparison of three approaches based on explainable artificial intelligence (XAI) to light up interpretability in the context of COVID-19 diagnosis using deep networks: Composite Layer-wise Propagation, Single Taylor Decomposition, and Deep Taylor Decomposition. Two deep networks have been used as the backbones to assess the explanation skills of the XAI approaches mentioned above: VGG11 and VGG16. We hope that such work can be used as a basis for further research on XAI and COVID-19 diagnosis for each approach figures its own positive and negative points.

Hassan Mohammad Mehedi, AlQahtani Salman A, Alelaiwi Abdulhameed, Papa João P

2022-Nov-17

COVID-19, Deep Taylor expansion, Explainable artificial intelligence, Machine learning

General General

Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes

bioRxiv Preprint

The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem -- identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders -- and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function.

McCorkindale, W. J.; Ahel, I.; Barr, H.; Correy, G. J.; Fraser, J. S.; London, N.; Schuller, M.; Shurrush, K.; Lee, A. A.

2022-11-24

Public Health Public Health

Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study.

In BMJ open

OBJECTIVE : To develop a vocal biomarker for fatigue monitoring in people with COVID-19.

DESIGN : Prospective cohort study.

SETTING : Predi-COVID data between May 2020 and May 2021.

PARTICIPANTS : A total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone's operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.

PRIMARY AND SECONDARY OUTCOME MEASURES : Four machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models' calibrations.

RESULTS : The final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p<0.001). We developed four models for Android female, Android male, iOS female and iOS male users with a weighted AUC of 86%, 82%, 79%, 85% and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue.

CONCLUSIONS : This study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.

TRIAL REGISTRATION NUMBER : NCT04380987.

Elbéji Abir, Zhang Lu, Higa Eduardo, Fischer Aurélie, Despotovic Vladimir, Nazarov Petr V, Aguayo Gloria, Fagherazzi Guy

2022-Nov-22

COVID-19, Health informatics, Public health

Pathology Pathology

Risk Stratification of COVID-19 Using Routine Laboratory Tests: A Machine Learning Approach.

In Infectious disease reports

The COVID-19 pandemic placed significant stress on an already overburdened health system. The diagnosis was based on detection of a positive RT-PCR test, which may be delayed when there is peak demand for testing. Rapid risk stratification of high-risk patients allows for the prioritization of resources for patient care. The study aims were to classify patients as severe or not severe based on outcomes using machine learning on routine laboratory tests. Data were extracted for all individuals who had at least one SARS-CoV-2 PCR test conducted via the NHLS between the periods of 1 March 2020 to 7 July 2020. Exclusion criteria: those 18 years, and those with indeterminate PCR tests. Results for 15437 patients (3301 positive and 12,136 negative) were used to fit six machine learning models, namely the logistic regression (LR) (the base model), decision trees (DT), random forest (RF), extreme gradient boosting (XGB), convolutional neural network (CNN) and self-normalising neural network (SNN). Model development was carried out by splitting the data into training and testing set of a ratio 70:30, together with a 10-fold cross-validation re-sampling technique. For risk stratification, admission to high care or ICU was the outcome for severe disease. Performance of the models varied: sensitivity was best for RF at 75% and accuracy of 75% for CNN. The area under the curve ranged from 57% for CNN to 75% for RF. RF and SNN were the best-performing models. Machine Learning (ML) can be incorporated into the laboratory information system and offers promise for early identification and risk stratification of COVID-19 patients, particularly in areas of resource-poor settings.

Mlambo Farai, Chironda Cyril, George Jaya

2022-Nov-21

COVID-19, laboratory tests, machine learning, risk stratification

Public Health Public Health

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions.

In Infectious disease reports

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., "neutral" sentiment was present in most of the Tweets. It was followed by "negative" and "positive" sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

Thakur Nirmalya

2022-Nov-14

big data, data analysis, data mining, dataset, machine learning, monkeypox, natural language processing, social media, tweets, twitter

General General

Online Dynamic Reliability Evaluation of Wind Turbines based on Drone-assisted Monitoring

ArXiv Preprint

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach.

Sohag Kabir, Koorosh Aslansefat, Prosanta Gope, Felician Campean, Yiannis Papadopoulos

2022-11-23

General General

Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection.

In Briefings in bioinformatics

Exiting computational models for drug-target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2.

Zhang Li, Wang Chun-Chun, Chen Xing

2022-Nov-19

SARS-CoV-2, computational model, drug–target binding affinity, molecule representation block, multi-head attention, skip connection

Radiology Radiology

RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

ArXiv Preprint

Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in generating high-quality images. Medical imaging data is fundamentally different to natural images, and the language used to succinctly capture relevant details in medical data uses a different, narrow but semantically rich, domain-specific vocabulary. Not surprisingly, multi-modal models trained on natural image-text pairs do not tend to generalize well to the medical domain. Developing generative imaging models faithfully representing medical concepts while providing compositional diversity could mitigate the existing paucity of high-quality, annotated medical imaging datasets. In this work, we develop a strategy to overcome the large natural-medical distributional shift by adapting a pre-trained latent diffusion model on a corpus of publicly available chest x-rays (CXR) and their corresponding radiology (text) reports. We investigate the model's ability to generate high-fidelity, diverse synthetic CXR conditioned on text prompts. We assess the model outputs quantitatively using image quality metrics, and evaluate image quality and text-image alignment by human domain experts. We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language. Fine-tuning this model on a fixed training set and using it as a data augmentation method, we measure a 5% improvement of a classifier trained jointly on synthetic and real images, and a 3% improvement when trained on a larger but purely synthetic training set. Finally, we observe that this fine-tuning distills in-domain knowledge in the text-encoder and can improve its representation capabilities of certain diseases like pneumothorax by 25%.

Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari

2022-11-23

Public Health Public Health

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana.

In Health and technology

Background : COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors.

Methods : Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively.

Results : The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively.

Conclusions : Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.

Muhammad L J, Haruna Ahmed Abba, Sharif Usman Sani, Mohammed Mohammed Bappah

2022

COVID-19, Deep Learning, Forecasting Model, Infection, Omicron Variant of Concern

General General

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic.

In Health and technology

Introduction : Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors.

Materials and methods : Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES).

Results : Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively.

Conclusion : This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them.

Oyewola David Opeoluwa, Dada Emmanuel Gbenga, Misra Sanjay

2022

COVID-19, Elastic net (ENET), Gaussian process (GAUSS), Machine learning, Spikes and slab (SPIKES), Vaccination

Public Health Public Health

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study.

In JMIR infodemiology

Background : There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making.

Objective : The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic.

Methods : A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter's advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized.

Results : We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people's mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government.

Conclusions : Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people's mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Marshall Christopher, Lanyi Kate, Green Rhiannon, Wilkins Georgina C, Pearson Fiona, Craig Dawn

COVID-19, Twitter, artificial intelligence, lockdown, machine learning, mental health, natural language processing, sentiment, soft intelligence

Pathology Pathology

The need for measurement science in digital pathology.

In Journal of pathology informatics ; h5-index 23.0

Background : Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology.

Methods : With an aim to support the UK's efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities.

Findings : The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a "must-have" for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce.

Conclusions : Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.

Romanchikova Marina, Thomas Spencer, Dexter Alex, Shaw Mike, Partarrieau Ignacio, Smith Nadia, Venton Jenny, Adeogun Michael, Brettle David, Turpin Robert James

2022-Nov-10

Artificial intelligence, Calibration, DICOM, Digital pathology, FAIR principles, Machine learning, Metadata, Metrology, Standards, Whole slide imaging

General General

Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.

In Frontiers in medicine

Background : Factors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic.

Methods : A retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery.

Results : Of the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6-10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003-1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004-1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194-0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016-0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202-0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939-4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63-0.861; p < 0.001), age (30-70) (OR = 0.738, 95% CI = 0.594-0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292-0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12-1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075-2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306-2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction.

Conclusion : Age and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Xu Yu, Ye Wei, Song Qiuyue, Shen Linlin, Liu Yu, Guo Yuhang, Liu Gang, Wu Hongmei, Wang Xia, Sun Xiaorong, Bai Li, Luo Chunmei, Liao Tongquan, Chen Hao, Song Caiping, Huang Chunji, Wu Yazhou, Xu Zhi

2022

COVID-19, Fangcang shelter, machine learning model, omicron, vaccination

General General

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging.

In Frontiers in medicine

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

Nawaz Marriam, Nazir Tahira, Javed Ali, Malik Khalid Mahmood, Saudagar Abdul Khader Jilani, Khan Muhammad Badruddin, Abul Hasanat Mozaherul Hoque, AlTameem Abdullah, AlKhathami Mohammed

2022

COVID-19, ECG, Efficient-ECGNet, computer vision, deep learning, medical imaging

General General

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare.

In Digital communications and networks

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steganalysis of 50% embedded stego instances using a classifier based on the Gaussian membership function.

Yazdanpanah Saeid, Chaeikar Saman Shojae, Jolfaei Alireza

2022-Nov-14

Audio security, Audio signal processing, Data hiding, Healthcare data, IoT security

Public Health Public Health

Managing healthcare supply chain through Artificial Intelligence (AI): A study of critical success factors.

In Computers & industrial engineering

Healthcare is one of the most critical sectors due to its importance in handling public health. With the outbreak of various diseases, more recently during Covid-19, this sector has gained further attention. The pandemic has exposed vulnerabilities in the healthcare supply chain (HSC). Recent advancements like the adoption of various advanced technologies viz. AI and Industry 4.0 in the healthcare supply chain are turning out to be game-changers. This study focuses on identifying critical success factors (CSFs) for AI adoption in HSC in the emerging economy context. Rough SWARA is used for ranking CSFs of AI adoption in HSC. Results indicate that technological (TEC) factors are the most influential factor that impacts the adoption of AI in HSC in the context of emerging economies, followed by institutional or environmental (INT), human (HUM), and organizational (ORG) dimensions.

Kumar Ashwani, Mani Venkatesh, Jain Vranda, Gupta Himanshu, Venkatesh V G

2022-Nov-15

Artificial Intelligence, Healthcare, SWARA, supply chain

General General

Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.

In Journal of the American Medical Informatics Association : JAMIA

Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period.

Parikh Ravi B, Zhang Yichen, Kolla Likhitha, Chivers Corey, Courtright Katherine R, Zhu Jingsan, Navathe Amol S, Chen Jinbo

2022-Nov-21

SARS-CoV-2, algorithm drift, cancer, machine learning, mortality

Public Health Public Health

SARS-CoV-2 seroprevalence, cumulative infections, and immunity to symptomatic infection - A multistage national household survey and modelling study, Dominican Republic, June-October 2021.

In Lancet regional health. Americas

Background : Population-level SARS-CoV-2 immunological protection is poorly understood but can guide vaccination and non-pharmaceutical intervention priorities. Our objective was to characterise cumulative infections and immunological protection in the Dominican Republic.

Methods : Household members ≥5 years were enrolled in a three-stage national household cluster serosurvey in the Dominican Republic. We measured pan-immunoglobulin antibodies against the SARS-CoV-2 spike (anti-S) and nucleocapsid glycoproteins, and pseudovirus neutralising activity against the ancestral and B.1.617.2 (Delta) strains. Seroprevalence and cumulative prior infections were weighted and adjusted for assay performance and seroreversion. Binary classification machine learning methods and pseudovirus neutralising correlates of protection were used to estimate 50% and 80% protection against symptomatic infection.

Findings : Between 30 Jun and 12 Oct 2021 we enrolled 6683 individuals from 3832 households. We estimate that 85.0% (CI 82.1-88.0) of the ≥5 years population had been immunologically exposed and 77.5% (CI 71.3-83) had been previously infected. Protective immunity sufficient to provide at least 50% protection against symptomatic SARS-CoV-2 infection was estimated in 78.1% (CI 74.3-82) and 66.3% (CI 62.8-70) of the population for the ancestral and Delta strains respectively. Younger (5-14 years, OR 0.47 [CI 0.36-0.61]) and older (≥75-years, 0.40 [CI 0.28-0.56]) age, working outdoors (0.53 [0.39-0.73]), smoking (0.66 [0.52-0.84]), urban setting (1.30 [1.14-1.49]), and three vs no vaccine doses (18.41 [10.69-35.04]) were associated with 50% protection against the ancestral strain.

Interpretation : Cumulative infections substantially exceeded prior estimates and overall immunological exposure was high. After controlling for confounders, markedly lower immunological protection was observed to the ancestral and Delta strains across certain subgroups, findings that can guide public health interventions and may be generalisable to other settings and viral strains.

Funding : This study was funded by the US CDC.

Nilles Eric J, Paulino Cecilia Then, de St Aubin Michael, Restrepo Angela Cadavid, Mayfield Helen, Dumas Devan, Finch Emilie, Garnier Salome, Etienne Marie Caroline, Iselin Louisa, Duke William, Jarolim Petr, Oasan Timothy, Yu Jingyou, Wan Huahua, Peña Farah, Iihoshi Naomi, Abdalla Gabriela, Lopez Beatriz, Cruz Lucia de la, Henríquez Bernarda, Espinosa-Bode Andres, Puello Yosanly Cornelio, Durski Kara, Baldwin Margaret, Baez Amado Alejandro, Merchant Roland C, Barouch Dan H, Skewes-Ramm Ronald, Gutiérrez Emily Zielinski, Kucharski Adam, Lau Colleen L

2022-Dec

Public Health Public Health

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels.

In Neural computing & applications

Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

Barua Prabal Datta, Aydemir Emrah, Dogan Sengul, Erten Mehmet, Kaysi Feyzi, Tuncer Turker, Fujita Hamido, Palmer Elizabeth, Acharya U Rajendra

2022-Nov-13

Favipiravir pattern, Molecular graph-based feature extraction, Specific language impairment, Vowel-based disease diagnosis

General General

Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak.

In Neural computing & applications

Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.

Varshney Deepika, Vishwakarma Dinesh Kumar

2022-Nov-13

COVID-19, Fake news detection, Information pollution

General General

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.

In Health data science

Background : During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.

Methods : COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.

Results : We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.

Conclusions : The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

Zhang Senqi, Sun Li, Zhang Daiwei, Li Pin, Liu Yue, Anand Ajay, Xie Zidian, Li Dongmei

2022

Public Health Public Health

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

In Frontiers in public health

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Hamza Ameer, Attique Khan Muhammad, Wang Shui-Hua, Alhaisoni Majed, Alharbi Meshal, Hussein Hany S, Alshazly Hammam, Kim Ye Jin, Cha Jaehyuk

2022

Bayesian optimization, corona virus, deep learning, fusion, hyperparameters, multi-filters contrast enhancement

General General

How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic.

In Annals of operations research

Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.

Kar Arpan Kumar, Tripathi Shalini Nath, Malik Nishtha, Gupta Shivam, Sivarajah Uthayasankar

2022-Nov-07

Consumer buying behavior, Consumer spending, Fake news, Hoarding, Supply chain disruptions

General General

Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston.

In Transportation

Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scarcely known how prices evolve at any given time. This research analyzes ride-hailing fares before and during the COVID-19 pandemic, focusing on applications of time series forecasting and machine learning models that may be useful for transport policy purposes. The Lyft Application Programming Interface was used to collect data on Lyft ride supply in Atlanta and Boston over 2 years (2019 and 2020). The Facebook Prophet model was used for long-term prediction to analyze the trends and global evolution of Lyft fares, while the Random Forest model was used for short-term prediction of ride-hailing fares. The results indicate that ride-hailing fares are affected during the COVID-19 pandemic, with values in the year 2020 being lower than those predicted by the models. The effects of fare peaks, uncontrollable events, and the impact of COVID-19 cases are also investigated. This study comes up with crucial policy recommendations for the ride-hailing market to better understand, regulate and integrate these services.

Silveira-Santos Tulio, González Ana Belén Rodríguez, Rangel Thais, Pozo Rubén Fernández, Vassallo Jose Manuel, Díaz Juan José Vinagre

2022-Nov-10

COVID-19, Dynamic Pricing, Machine Learning, Ride-Hailing, Time Series Forecasting, Transport Policy

General General

Face-mask-aware Facial Expression Recognition based on Face Parsing and Vision Transformer.

In Pattern recognition letters

As wearing face masks is becoming an embedded practice due to the COVID-19 pandemic, facial expression recognition (FER) that takes face masks into account is now a problem that needs to be solved. In this paper, we propose a face parsing and vision Transformer-based method to improve the accuracy of face-mask-aware FER. First, in order to improve the precision of distinguishing the unobstructed facial region as well as those parts of the face covered by a mask, we re-train a face-mask-aware face parsing model, based on the existing face parsing dataset automatically relabeled with a face mask and pixel label. Second, we propose a vision Transformer with a cross attention mechanism-based FER classifier, capable of taking both occluded and non-occluded facial regions into account and reweigh these two parts automatically to get the best facial expression recognition performance. The proposed method outperforms existing state-of-the-art face-mask-aware FER methods, as well as other occlusion-aware FER methods, on two datasets that contain three kinds of emotions (M-LFW-FER and M-KDDI-FER datasets) and two datasets that contain seven kinds of emotions (M-FER-2013 and M-CK+ datasets).

Yang Bo, Wu Jianming, Ikeda Kazushi, Hattori Gen, Sugano Masaru, Iwasawa Yusuke, Matsuo Yutaka

2022-Dec

41A05, 41A10, 65D05, 65D17, Covid-19, Deep learning, Face mask, Face parsing, Facial expression recognition, Vision transformer

Ophthalmology Ophthalmology

The Role of Technology in Ophthalmic Surgical Education During COVID-19.

In Current surgery reports

Purpose of Review : To describe the effect of COVID-19 on ophthalmic training programs and to review the various roles of technology in ophthalmology surgical education including virtual platforms, novel remote learning curricula, and the use of surgical simulators.

Recent Findings : COVID-19 caused significant disruption to in-person clinical and surgical patient encounters. Ophthalmology trainees worldwide faced surgical training challenges due to social distancing restrictions, trainee redeployment, and reduction in surgical case volume. Virtual platforms, such as Zoom and Microsoft Teams, were widely used during the pandemic to conduct remote teaching sessions. Novel virtual wet lab and dry lab curricula were developed. Training programs found utility in virtual reality surgical simulators, such as the Eyesi, to substitute experience lost from live patient surgical cases.

Summary : Although several of these described technologies were incorporated into ophthalmology surgical training programs prior to COVID-19, the pandemic highlighted the importance of developing a formal surgical curriculum that can be delivered virtually. Novel telementoring, collaboration between training institutions, and hybrid formats of didactic and practical training sessions should be continued. Future research should investigate the utility of augmented reality and artificial intelligence for trainee learning.

Hu Katherine S, Pettey Jeff, SooHoo Jeffrey R

2022-Nov-14

Ophthalmology training, Remote learning, Surgical simulators, Virtual education, Wet lab curriculum

General General

ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.

In Pattern recognition letters

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.

Umer Muhammad, Sadiq Saima, Karamti Hanen, Abdulmajid Eshmawi Ala’, Nappi Michele, Usman Sana Muhammad, Ashraf Imran

2022-Dec

COVID-19, Ensemble model, Health informatics, Neuroinformatics, Sentiment analysis

General General

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

In Expert systems with applications

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Nguyen Long H, Pham Nhat Truong, Do Van Huong, Nguyen Liu Tai, Nguyen Thanh Tin, Nguyen Hai, Nguyen Ngoc Duy, Nguyen Thanh Thi, Nguyen Sy Dzung, Bhatti Asim, Lim Chee Peng

2023-Mar-01

COVID-19, Deep learning, Delta variant, EfficientNet, Log-Mel spectrogram, Machine vision, Neural network, PANNs, Recorded cough sounds, Remote detection, SARS-CoV-2 infections, Self-testing service, Sound classification, Speedy detection, Wavegram

General General

Cross-cultural factors influencing the adoption of virtual reality for practical learning.

In Universal access in the information society

Education is one area that was significantly affected by the COVID-19 pandemic with much of the education being transferred online. Many subjects that require hands-on experimental experience suffer when taught online. Education is also one area that many believe can benefit from the advances in virtual reality (VR) technology, particularly for remote, online learning. Furthermore, because the technology shows overall good results with hands-on experiential learning education, one possible way to overcome online education barriers is with the use of VR applications. Given that VR has yet to make significant inroads in education, it is essential to understand what factors will influence this technology's adoption and acceptance. In this work, we explore factors influencing the adoption of VR for hands-on practical learning around the world based on the Unified Theory of Acceptance and Use of Technology and three additional constructs. We also performed a cross-cultural analysis to examine the model fit for developed and developing countries and regions. Moreover, through open-ended questions, we gauge the overall feeling people in these countries have regarding VR for practical learning and how it compares with regular online learning.

Monteiro Diego, Ma Teng, Li Yue, Pan Zhigeng, Liang Hai-Ning

2022-Nov-15

COVID-19, Cross-cultural, Practical learning, Survey, Technology acceptance, Training, Virtual reality

General General

Is indoor and outdoor greenery associated with fewer depressive symptoms during COVID-19 lockdowns? A mechanistic study in Shanghai, China.

In Building and environment

Increasing numbers of studies have observed that indoor and outdoor greenery are associated with fewer depressive symptoms during COVID-19 lockdowns. However, most of these studies examined direct associations without sufficient attention to underlying pathways. Furthermore, few studies have combined different types of indoor and outdoor greenery to examine their effects on the alleviation of depressive symptoms. The present study hypothesized that indoor and outdoor exposure to greenery increased the perceived restorativeness of home environments, which, in turn, reduced loneliness, COVID-related fears, and, ultimately, depressive symptoms. To test our hypotheses, we conducted an online survey with 386 respondents in Shanghai, China, from April to May 2022, which corresponded to strict citywide lockdowns that resulted from the outbreak of the Omicron variant. Indoor greenery measures included the number of house plants, gardening activities, and digital nature exposure as well as semantic image segmentation applied to photographs from the most viewed windows to quantify indoor exposure to outdoor trees and grass. Outdoor greenery measures included total vegetative cover (normalized difference vegetation index [NDVI]) within a 300 m radius from the home and perceived quality of the community's greenery. Associations between greenery and depressive symptoms/clinical levels of depression, as measured by the Patient Health Questionnaire-9 (PHQ-9), were examined using generalized linear and logistic regression models. Structural equation modeling (SEM) was used to test pathways between greenery exposure, restorativeness, loneliness, fear of COVID-19, and depressive symptoms. The results showed that: 1) indoor and outdoor greenery were associated with fewer depressive symptoms; 2) greenery could increase the restorativeness of the home environment, which, in turn, was associated with fewer COVID-related mental stressors (i.e., loneliness and fear of COVID-19), and ultimately depressive symptoms; and 3) gender, education, and income did not modify associations between greenery and depressive symptoms. These findings are among the first to combine objective and subjective measures of greenery within and outside of the home and document their effects on mental health during lockdowns. Comprehensive enhancements of greenery in living environments could be nature-based solutions for mitigating COVID-19 related mental stressors.

Zhang Jinguang, Browning Matthew H E M, Liu Jie, Cheng Yingyi, Zhao Bing, Dadvand Payam

2023-Jan

Fear of COVID-19, Loneliness, Machine learning, Nature exposure, Visual access

General General

Time-delayed modelling of the COVID-19 dynamics with a convex incidence rate.

In Informatics in medicine unlocked

COVID-19 pandemic represents an unprecedented global health crisis which has an enormous impact on the world population and economy. Many scientists and researchers have combined efforts to develop an approach to tackle this crisis and as a result, researchers have developed several approaches for understanding the COVID-19 transmission dynamics and the way of mitigating its effect. The implementation of a mathematical model has proven helpful in further understanding the behaviour which has helped the policymaker in adopting the best policy necessary for reducing the spread. Most models are based on a system of equations which assume an instantaneous change in the transmission dynamics. However, it is believed that SARS-COV-2 have an incubation period before the tendency of transmission. Therefore, to capture the dynamics adequately, there would be a need for the inclusion of delay parameters which will account for the delay before an exposed individual could become infected. Hence, in this paper, we investigate the SEIR epidemic model with a convex incidence rate incorporated with a time delay. We first discussed the epidemic model as a form of a classical ordinary differential equation and then the inclusion of a delay to represent the period in which the susceptible and exposed individuals became infectious. Secondly, we identify the disease-free together with the endemic equilibrium state and examine their stability by adopting the delay differential equation stability theory. Thereafter, we carried out numerical simulations with suitable parameters choice to illustrate the theoretical result of the system and for a better understanding of the model dynamics. We also vary the length of the delay to illustrate the changes in the model as the delay parameters change which enables us to further gain an insight into the effect of the included delay in a dynamical system. The result confirms that the inclusion of delay destabilises the system and it forces the system to exhibit an oscillatory behaviour which leads to a periodic solution and it further helps us to gain more insight into the transmission dynamics of the disease and strategy to reduce the risk of infection.

Babasola Oluwatosin, Kayode Oshinubi, Peter Olumuyiwa James, Onwuegbuche Faithful Chiagoziem, Oguntolu Festus Abiodun

2022

34D20, 37N25, 39A60, 92B05, COVID-19, Convex incidence rate, Delay differential equation, SEIR epidemic model, Stability

Public Health Public Health

Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network.

In Cognitive computation

COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people's emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people's responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people's attitudes towards vaccines, we can obtain people's attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people's negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people's attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people's attitudes towards the vaccine and those towards the vaccination.

Qiu Dong, Yu Yang, Chen Lei

2022-Nov-16

COVID-19 vaccines, Fuzzy convolutional neural network, Fuzzy emotion vector, Fuzzy logic, Sentiment analysis

General General

Enhanced Framework for COVID-19 Prediction with Computed Tomography Scan Images using Dense Convolutional Neural Network and Novel Loss Function.

In Computers & electrical engineering : an international journal

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

Motwani Anand, Shukla Piyush Kumar, Pawar Mahesh, Kumar Manoj, Ghosh Uttam, Numay Waleed Al, Nayak Soumya Ranjan

2022-Nov-14

COVID-19, Chest CT-images, Classification, Deep Learning, Dense-Convolutional Neural Network, Loss function, Optimization, Prediction, SARS-CoV-2

General General

Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.

In Journal of healthcare engineering

COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.

Qureshi Moiz, Daniyal Muhammad, Tawiah Kassim

2022

General General

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm.

In Machine learning with applications

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Seker Mert, Männistö Anssi, Iosifidis Alexandros, Raitoharju Jenni

2022-Nov-09

Human Pose Estimation, Performance Evaluation, Person detection, Proxemics, Social distance estimation, Test Benchmark

General General

An automated multi-web platform voting framework to predict misleading information proliferated during COVID-19 outbreak using ensemble method.

In Data & knowledge engineering

The spreading of misleading information on social web platforms has fuelled massive panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. Previous studies mainly relied on a specific web platform to collect crucial evidence to detect fake content. The analysis identifies that retrieving clues from two or more different sources/web platforms gives more reliable prediction and confidence concerning a specific claim. This study proposed a novel multi-web platform voting framework that incorporates 4 sets of novel features: content, linguistic, similarity, and sentiments. The features have been gathered from each web-platforms to validate the news. To validate the fact/claim, a unique source platform is designed to collect relevant clues/headlines from two web platforms (YouTube, Google) based on specific queries and extracted features concerning each clue/headline. The proposed idea is to incorporate a unique platform to assist researchers in gathering relevant and vital evidence from diverse web platforms. After evaluation and validation, it has been identified that the built model is quite intelligent, gives promising results, and effectively predicts misleading information. The model correctly detected about 98% of the COVID misinformation on the constraint Covid-19 fake news dataset. Furthermore, it is observed that it is efficient to gather clues from multiple web platforms for more reliable predictions to validate the news. The suggested work depicts numerous practical applications for health policy-makers and practitioners that could be useful in safeguarding and implicating awareness among society from misleading information dissemination during this pandemic.

Varshney Deepika, Vishwakarma Dinesh Kumar

2022-Nov-11

COVID-19, Fake news, Google, Machine learning, Misleading information, Multi-web platforms, YouTube

General General

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model.

In Journal of chemical information and modeling

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.

Hu Fan, Wang Dongqi, Huang Huazhen, Hu Yishen, Yin Peng

2022-Nov-19

oncology Oncology

Assessment of Oncology Patient Engagement and Interest in Virtual Mind-Body Programming: Moving Toward Personalization of Virtual Care.

In JCO oncology practice

PURPOSE : Accelerated by the COVID-19 pandemic, the virtual platform has become a prominent medium to deliver mind-body therapies, but the extent to which patients engage in virtual mind-body programming remains unclear. This study aims to assess oncology patient engagement in a virtual mind-body program.

METHODS : We surveyed oncology patients enrolled in a live-streamed (synchronous) virtual mind-body program in May 2021. Patients self-reported engagement by weekly attendance. We applied multivariate regression to identify associations of engagement with sociodemographic and clinical factors. As an exploratory analysis, we used machine learning to partition engagement subgroups to determine preferential interest in prerecorded (asynchronous) mind-body therapy videos.

RESULTS : Among 148 patients surveyed (response rate: 21.4%), majority were female (94.5%), White (83.1%), age 65 years or older (64.9%), retired (64.2%), and in survivorship (61.8%). Patient engagement ranged from 1 to 13 classes/week (mean [standard deviation]: 4.23 [2.56]) and was higher for female (β, .82; 95% CI, 0.01 to 1.62), non-White (β, .63; 95% CI, 0.13 to 1.13), and retired patients (β, .50; 95% CI, 0.12 to 0.88). The partition model identified three engagement subgroups: employed (low engagers), retired White (intermediate engagers), and retired non-White (high engagers). Particularly, low engagers had preferential interest in meditation videos (odds ratio, 2.85; 95% CI, 1.24 to 6.54), and both low and high engagers had preferential interest in Tai Chi videos (odds ratio, 2.26; 95% CI, 1.06 to 4.82).

CONCLUSION : In this cross-sectional study among oncology patients, engagement in virtual mind-body programming was higher for female, non-White, and retired patients. Our findings suggest the need for both synchronous and asynchronous mind-body programming to meet the diverse needs of oncology patients.

Hung Tony K W, Latte-Naor Shelly, Li Yuelin, Kuperman Gilad J, Seluzicki Christina, Pendleton Eva, Pfister David G, Mao Jun J

2022-Nov-18

General General

Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students' mental health.

In PloS one ; h5-index 176.0

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and the increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.

Rezapour Mostafa, Elmshaeuser Scott K

2022

Radiology Radiology

Utilizing an organizational development framework as a road map for creating a technology-driven agile curriculum in predoctoral dental education.

In Journal of dental education

The landscape of dental education is undergoing a paradigm shift from both the learner's and teacher's perspectives. Evolving technologies, including artificial intelligence, virtual reality, augmented reality, and mixed reality, are providing synergistic opportunities to create new and exciting educational platforms. The evolution of these platforms will likely play a significant role in dental education. This is especially true in the wake of calamities like the COVID-19 pandemic during which educational activities had to be shutdown or moved online. This experience demonstrated that it is prudent to develop curricula that are both agile and efficient via creating hybrid courses that provide effective learning experiences regardless of the mode of delivery. Although there is growing interest in incorporating technology into dental education, there are few examples of how to actually manage the implementation of technology into the curriculum. In this paper, we provide a road map for incorporating technology into the dental curriculum to create agility and discuss challenges and possible solutions.

Tadinada Aditya, Gul Gulsun, Godwin Lauren, Al Sakka Yacoub, Crain Geralyn, Stanford Clark M, Johnson Jeffrey

2022-Nov-18

computer-assisted instruction, curriculum innovation, dental education, institutional/organizational development, patient simulation

General General

Perceived usefulness of COVID-19 tools for contact tracing among contact tracers in Korea.

In Epidemiology and health

Objectives : In Korea, contact tracing for coronavirus disease 2019 is conducted using the information from credit card records, handwritten visitor logs, KI-Pass (QR code), and Safe Call after an interview. We aimed to assess the usefulness of these tools for contact tracing.

Methods : The 2 months (July to September 2021) long anonymous online survey was conducted. Contact tracers from throughout Korea were included as the participants. The questionnaire consisted of 4 parts: 1) demographic characteristics, 2) usefulness of each tool for contact tracing, 3) order in which information is checked during contact tracing, and 4) match rate between tools for contact tracing, screening test rate, response rate, and helpfulness (rated on a Likert scale).

Results : A total of 190 individuals participated in the survey. When asked to rate the usefulness of each tool for contact tracing on a Likert scale, most respondents (86%) provided positive response for "credit card records", while the most common response for "handwritten visitor logs" was negative. The actual helpfulness of positive response was KI-Pass (91%), Credit card records (83%), Safe Call (78%), and Handwritten visitor logs (22%).

Conclusion : Over 80% of participants provided positive responses to credit card records, KI-Pass, and Safe Call data, while approximately 50% provided negative responses regarding the usefulness of handwritten visitor logs. Our findings highlight the need to unify systems for contact tracing performed after an interview to increase their convenience for contact tracers, as well as the need to improve tools that utilize handwritten visitor logs for digitally vulnerable groups.

Gong Seonyeong, Moon Jong Youn, Jung Jaehun

2022-Nov-15

COVID-19, Contact tracing, Entry log, KI-Pass

General General

Cell-type annotation with accurate unseen cell-type identification using multiple references

bioRxiv Preprint

Automated cell-type annotation using a well-annotated single-cell RNA-sequencing (scRNA-seq) reference relies on the diversity of cell types in the reference. However, for technical and biological reasons, new query data of interest may contain unseen cell types that are missing from the reference. When annotating new query data, identifying the unseen cell type is fundamental not only to improve annotation accuracy but also to new biological discoveries. Here, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the help of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric defined from three complementary aspects to identify unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for cell-type annotation and unseen cell-type identification on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets.

Yixuan, X.; Mengguo, W.; Luonan, C.; Xiaofei, Z.

2022-11-18

General General

Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants.

In Computers in biology and medicine

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape evolution in virus-human protein-protein binding, which is crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topological change and homotopic shape evolution of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36% more infectious than BA.2 and are projected to become new dominant variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes. Our key projection about BA4 and BA.5's dominance made on May 1, 2022 (see arXiv:2205.00532) became a reality in late June 2022.

Chen Jiahui, Qiu Yuchi, Wang Rui, Wei Guo-Wei

2022-Nov-02

Deep learning, Evolution, Infectivity, Persistent Laplacian, SARS-CoV-2

General General

Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research.

In Interactive journal of medical research

The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.

Fischer Aurelie, Elbeji Abir, Aguayo Gloria, Fagherazzi Guy

2022-Nov-15

COVID-19, COVID-19 symptoms, Long COVID, artificial intelligence, digital health, digital health monitoring, digital health solution, health care application, health monitoring, health technology, remote monitoring, remote patient monitoring, vocal biomarker, voice, voice-based technology

Radiology Radiology

Cardiovascular CT, MRI, and PET/CT in 2021: Review of Key Articles.

In Radiology ; h5-index 91.0

This review focuses on three key noninvasive cardiac imaging modalities-cardiac CT angiography (CTA), MRI, and PET/CT-and summarizes key publications in 2021 relevant to radiologists in clinical practice. Although this review focuses primarily on articles published in Radiology, important studies from other major journals are included to highlight "must-know" articles in the field of cardiovascular imaging. Cardiac CTA has been established as the first-line test for patients with stable chest pain and no known coronary artery disease, and its value remains central to the assessment of surgical or transcatheter aortic valve replacement. Artificial intelligence continues to evolve in a number of applications in cardiovascular disease. In cardiac MRI studies, 2021 has seen an emphasis on nonischemic cardiomyopathies, valvular heart disease, and COVID-19 disease cardiac manifestations and the authors highlight the key articles on these topics. A section featuring the increasing role of cardiac PET/CT in the assessment of cardiac sarcoidosis and prosthetic valves is also provided.

Tzimas Georgios, Ryan David T, Murphy David J, Leipsic Jonathon A, Dodd Jonathan D

2022-Nov-15

General General

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers.

In Journal of general internal medicine ; h5-index 57.0

BACKGROUND : COVID-19 symptom reports describe varying levels of disease severity with differing periods of recovery and symptom trajectories. Thus, there are a multitude of disease and symptom characteristics clinicians must navigate and interpret to guide care.

OBJECTIVE : To find natural groups of patients with similar constellations of post-acute sequelae of COVID-19 (PASC) symptoms.

DESIGN : Cohort SETTING: Outpatient COVID-19 recovery clinic with patient referrals from 160 primary care clinics serving 36 counties in Texas.

PATIENTS : Adult patients seeking COVID-19 recovery clinic care between November 15, 2020, and July 31, 2021, with laboratory-confirmed mild (not hospitalized), moderate (hospitalized), or severe (hospitalized with critical care) COVID-19.

MAIN MEASURES : Demographics, COVID illness onset, and duration of persistent PASC symptoms via semi-structured medical assessments.

KEY RESULTS : Four hundred forty-one patients (mean age 51.5 years; 295 [66.9%] women; 99 [22%] Hispanic, and 170 [38.5%] non-White, racial minority) met inclusion criteria. Using a k-medoids algorithm, we found that PASC symptoms cluster into two distinct groups: neuropsychiatric (N = 186) (e.g., subjective cognitive dysfunction) and pulmonary (N = 255) (e.g., dyspnea, cough). The neuropsychiatric cluster had significantly higher incidences of otolaryngologic (X2 = 14.3, p < 0.001), gastrointestinal (X2 = 6.90, p = 0.009), neurologic (X2 = 441, p < 0.001), and psychiatric sequelae (X2 = 40.6, p < 0.001) with more female (X2 = 5.44, p = 0.020) and younger age (t = 2.39, p = 0.017) patients experiencing longer durations of PASC symptoms before seeking care (t = 2.44, p = 0.015). Patients in the pulmonary cluster were more often hospitalized for COVID-19 (X2 = 3.98, p = 0.046) and had significantly higher comorbidity burden (U = 20800, p = 0.019) and pulmonary sequelae (X2 = 13.2, p < 0.001).

CONCLUSIONS : Health services clinic data from a large integrated health system offers insights into the post-COVID symptoms associated with care seeking for sequelae that are not adequately managed by usual care pathways (self-management and primary care clinic visits). These findings can inform machine learning algorithms, primary care management, and selection of patients for earlier COVID-19 recovery referral.

TRIAL REGISTRATION : N/A.

Danesh Valerie, Arroliga Alejandro C, Bourgeois James A, Boehm Leanne M, McNeal Michael J, Widmer Andrew J, McNeal Tresa M, Kesler Shelli R

2022-Nov-14

General General

A Machine Learning Approach to Identify Predictors of Severe COVID-19 Outcome in Patients With Rheumatoid Arthritis.

In Pain physician ; h5-index 45.0

BACKGROUND : Rheumatoid arthritis (RA) patients have a lowered immune response to infection, potentially due to the use of corticosteroids and immunosuppressive drugs. Predictors of severe COVID-19 outcomes within the RA population have not yet been explored in a real-world setting.

OBJECTIVES : To identify the most influential predictors of severe COVID-19 within the RA population.

STUDY DESIGN : Retrospective cohort study.

SETTING : Research was conducted using Optum's de-identified Clinformatics® Data Mart Database (2000-2021Q1), a US commercial claims database.

METHODS : We identified adult patients with index COVID-19 (ICD-10-CM diagnosis code U07.1) between March 1, 2020, and December 31, 2020. Patients were required to have continuous enrollment and have evidence of one inpatient or 2 outpatient diagnoses of RA in the 365 days prior to index. RA patients with COVID-19 were stratified by outcome (mild vs severe), with severe cases defined as having one of the following within 60 days of COVID-19 diagnosis: death, treatment in the intensive care unit (ICU), or mechanical ventilation. Baseline demographics and clinical characteristics were extracted during the 365 days prior to index COVID-19 diagnosis. To control for improving treatment options, the month of index date was included as a potential independent variable in all models. Data were partitioned (80% train and 20% test), and a variety of machine learning algorithms (logistic regression, random forest, support vector machine [SVM], and XGBoost) were constructed to predict severe COVID-19, with model covariates ranked according to importance.

RESULTS : Of 4,295 RA patients with COVID-19 included in the study, 990 (23.1%) were classified as severe. RA patients with severe COVID-19 had a higher mean age (mean [SD] = 71.6 [10.3] vs 63.4 [13.7] years, P < 0.001) and Charlson Comorbidity Index (CCI) (3.8 [2.4] vs 2.4 [1.8], P < 0.001) than those with mild cases. Males were more likely to be a severe case than mild (29.1% vs 18.5%, P < 0.001). The top 15 predictors from the best performing model (XGBoost, AUC = 75.64) were identified. While female gender, commercial insurance, and physical therapy were inversely associated with severe COVID-19 outcomes, top predictors included a March index date, older age, more inpatient visits at baseline, corticosteroid or gamma-aminobutyric acid analog (GABA) use at baseline or the need for durable medical equipment (i.e., wheelchairs), as well as comorbidities such as congestive heart failure, hypertension, fluid and electrolyte disorders, lower respiratory disease, chronic pulmonary disease, and diabetes with complication.

LIMITATIONS : The cohort meeting our eligibility criteria is a relatively small sample in the context of machine learning. Additionally, diagnoses definitions rely solely on ICD-10-CM codes, and there may be unmeasured variables (such as labs and vitals) due to the nature of the data. These limitations were carefully considered when interpreting the results.

CONCLUSIONS : Predictive baseline comorbidities and risk factors can be leveraged for early detection of RA patients at risk of severe COVID-19 outcomes. Further research should be conducted on modifiable factors in the RA population, such as physical therapy.

Burns Sara M, Woodworth TIffany S, Icten Zeynep, Honda Trenton, Manjourides Justin

2022-Nov

** RA, SARS-CoV-2, corticosteroid use\r, machine learning, physical therapy, predictive modeling, real-world data, real-world evidence, rheumatoid arthritis, COVID-19**

General General

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images.

In Critical reviews in biomedical engineering

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.

Kalpana Gotlur, Durga A Kanaka, Karuna G

2022

General General

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19.

In Neural computing & applications

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Wu Binrong, Wang Lin, Tao Rui, Zeng Yu-Rong

2022-Nov-04

COVID-19, Deep learning, Interpretable tourism demand forecasting, Variational mode decomposition

General General

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.

In Neural computing & applications

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

Celard P, Iglesias E L, Sorribes-Fdez J M, Romero R, Vieira A Seara, Borrajo L

2022-Nov-04

Artificial neural networks, Computer vision, Convolutional neural networks, Generative adversarial networks, Medical imaging, Variational autoencoders

Public Health Public Health

Practice of big data and artificial intelligence in epidemic surveillance and containment.

In Intelligent medicine

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment, and these are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarizes the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis in epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Jiao Zengtao, Ji Hanran, Yan Jun, Qi Xiaopeng

2022-Nov-05

Artificial intelligence, Big data, Early warning, Epidemic prevention and control, Epidemiological investigation

General General

Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience.

In Procedia CIRP

The COVID-19 pandemic and crises like the Ukraine-Russia war have led to numerous restrictions for industrial manufacturing due to interrupted supply chains, staff absences due to illness or quarantine measures, and order situations that changed significantly at short notice. These influences have exposed that it is crucial to address the issue of manufacturing resilience in the context of current disruptions. This can be plausibly guaranteed by subjecting the ML model of a manufacturing system to attacks deliberately designed to fool its prediction. Such attacks can provide useful insights into properties that can increase resilience of manufacturing systems.

Mukherjee Avik, Glatt Moritz, Mustafa Waleed, Kloft Marius, Aurich Jan C

2022

adverserial attacks, adverserial training, deep neural networks, discrete-event simulation environment, machine learning, manufacturing system, resilience, supply network

General General

Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML

ArXiv Preprint

Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation regarding inflammation, hemostasis and vascular function in COVID-19 by performing a time-varying observational analysis of over 4000 patients hospitalized for COVID-19 in a New York City hospital system from March 2020 to August 2021. To perform this analysis, we apply tree-based generalized additive models with temporal interactions which recover discontinuous risk changes caused by discrete protocols changes. We find that the biomarkers of thrombosis increasingly predicted mortality from March 2020 to August 2021, while the association between biomarkers of inflammation and thrombosis weakened. Beyond COVID-19, this presents a straightforward methodology to estimate unknown and discontinuous time-varying effects.

Benjamin Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana

2022-11-15

General General

CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia from Chest X-ray Images.

In IEEE journal of biomedical and health informatics

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. The Average Accuracy, Sensitivity, Specificity, PPV and F1-score of models in the COVID-19 pneumonia detection reach 0.992, 0.998, 0.985 and 0.989, respectively. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

Zhang Xin, Han Liangxiu, Sobeih Tam, Han Lianghao, Dempsey Nina, Lechareas Symeon, Tridente Ascanio, Chen Haoming, White Stephen, Zhang Daoqiang

2022-Nov-09

General General

LitCovid in 2022: an information resource for the COVID-19 literature.

In Nucleic acids research ; h5-index 217.0

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

Chen Qingyu, Allot Alexis, Leaman Robert, Wei Chih-Hsuan, Aghaarabi Elaheh, Guerrerio John J, Xu Lilly, Lu Zhiyong

2022-Nov-09

General General

Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach.

In Heliyon

The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior. As the government of different countries has been implementing safety protocols to mitigate the spread of the virus, people became apprehensive about traveling and going out. This paved way for the emergence of third-party logistics (3PL). Statistics have proven the rapid escalation regarding the use of 3PL in various countries. This study utilized Artificial Neural Network and Random Forest Classifier to validate and justify the factors that affect consumer intention in selecting a 3PL service provider during the COVID-19 pandemic integrating the Service Quality Dimensions and Pro-Environmental Theory of Planned Behavior. The findings of this study revealed that attitude is the most significant factor that affects the consumers' behavioral intention. Other factors such as customer satisfaction, customer perceived value, perceived environmental concern, assurance, responsiveness, empathy, reliability, tangibility, perceived behavioral control, subjective norm, and perceived authority support, are all contributing factors that affect behavioral intention. Machine learning algorithms, specifically ANN and RFC, resulted to be reliable in predicting factors as they obtained accuracy rates of 98.56% and 93%. Results presented that consumers' attitude, satisfaction, perceived value, assurance by the 3PL, and perceived environmental concerns were highly influential in choosing a 3PL package carrier. It was seen that people would be encouraged to use 3PL service providers if they demonstrate availability and environmental concerns in catering to the customers' needs. Subsequently, 3PL providers must assure safety and convenience before, during, and after providing the service to ensure continuous patronage of consumers. This is considered to be the first study that utilized a machine learning ensemble to measure behavioral intention for the logistic sector. The framework, analysis tools, and findings of this study could be extended and applied among other behavioral intentions regarding transportation worldwide. Managerial insights among service providers are discussed.

German Josephine D, Ong Ardvin Kester S, Perwira Redi Anak Agung Ngurah, Robas Kirstien Paola E

2022-Nov

Artificial neural network, Behavioral intention, Random forest classifier, Third party logistics

Public Health Public Health

COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach.

In JMIR infodemiology

Background : Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19-related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19.

Objective : The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action.

Methods : A total of 646,885,238 COVID-19-related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets.

Results : In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action.

Conclusions : During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals.

Ke Si Yang, Neeley-Tass E Shannon, Barnes Michael, Hanson Carl L, Giraud-Carrier Christophe, Snell Quinn

COVID-19, Health Belief Model, Twitter, content analysis, deep learning, health belief, infodemic, infodemiology, machine learning, mask, misinformation, vaccination, vaccine data set

General General

Improved Deep Convolutional Neural Networks using Chimp Optimization Algorithm for Covid19 Diagnosis from the X-Ray Images.

In Expert systems with applications

Applying Deep Learning (DL) in radiological images (i.e., chest X-Rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. Following that, two publicly accessible datasets termed COVID-Xray-5k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2s-12c-2s and i-8c-2s-16c-2s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89 %. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

Cai Chengfeng, Gou Bingchen, Khishe Mohammad, Mohammadi Mokhtar, Rashidi Shima, Moradpour Reza, Mirjalili Seyedali

2022-Nov-04

COVID-19 diagnosis, Chest X-Rays, Chimp Optimization Algorithm, Convolutional Neural Networks, Deep Learning

General General

Preparations for the Assessment of COVID-19 Infection and Long-Term Cardiovascular Risk.

In Korean circulation journal

Studies showing that coronavirus disease 2019 (COVID-19) is associated with an increased risk of cardiovascular disease continue to be published. However, studies on how long the overall cardiovascular risk increases after COVID-19 and the magnitude of its long-term effects have only been confirmed recently. This is partly because the distinction between cardiovascular risk as an acute complication of COVID-19 or post-acute cardiovascular manifestations is ambiguous. Long-COVID has arisen as an important topic in the second half of the pandemic. This term indicates that symptoms persist for more than two 2 months; following three months of SARS-CoV-2 infection and cannot be explained by other medical conditions. Despite the agreement of these international organizations and experts, it is difficult to define whether there is sufficient medical evidence to prove the existence of long-COVID. However, the Korean government and Korea Disease Control and Prevention Agency (KDCA) are preparing a new platform to assess the long-term impact of COVID-19. Using this data, a prospective cohort of 10,000 confirmed COVID-19 cases will be established. This cohort will be linked with claims data from the National Health Insurance Services (NHIS) and it is expected that increased real-world evidence of long-COVID will be accumulated.

Jung Jaehun

2022-Nov

COVID-19, COVID-19 vaccination, Cardiovascular risk, Long-COVID

General General

Intelligent COVID-19 screening platform based on breath analysis.

In Journal of breath research

BACKGROUND : The spread of COVID-19 results in an increasing incidence and mortality. The typical diagnosis technique for SARS-CoV-2 infection is RT-PCR, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic.

METHODS : Here we constructed an intelligent system based on the VOC biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p<0.001) between the COVID-19 group and the control group. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection.

RESULTS : The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and an F1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network, k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model.

CONCLUSION : The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.

Xue Cuili, Xu Xiaohong, Liu Zexi, Zhang Yuna, Xu Yuli, Niu Jiaqi, Jin Han, Xiong Wujun, Cui Daxiang

2022-Nov-08

COVID-19 diagnosis, Portable GC-MS, breath analysis, support vector machine, volatile organic compound

Public Health Public Health

Exploring public opinion about telehealth during COVID-19 by social media analytics.

In Journal of telemedicine and telecare ; h5-index 28.0

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

Pool Javad, Namvar Morteza, Akhlaghpour Saeed, Fatehi Farhad

2022-Dec

COVID-19, Telehealth, machine learning, social media analytics, telemedicine

General General

Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps.

OBJECTIVE : This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training.

METHODS : Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a "Personalized Advantage Index" (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control.

RESULTS : A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit.

CONCLUSIONS : Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.

Webb Christian A, Hirshberg Matthew J, Davidson Richard J, Goldberg Simon B

2022-Nov-08

machine learning, meditation, mobile phone, mobile technology, precision medicine, prediction, smartphone app

General General

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model.

In Current psychology (New Brunswick, N.J.)

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.

Pan Wenhao, Han Yingying, Li Jinjin, Zhang Emily, He Bikai

2022-Nov-03

COVID-19 pandemic, Fine-grained sentiment lexicon, Positive energy, Social media analysis

General General

IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey.

In IEEE access : practical innovations, open solutions

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Anjum Nasreen, Alibakhshikenari Mohammad, Rashid Junaid, Jabeen Fouzia, Asif Amna, Mohamed Ehab Mahmoud, Falcone Francisco

2022

COVID-19 pandemic, Internet of Things (IoTs), artificial intelligence (AI), coronavirus, machine learning algorithms

General General

The paradigm and future value of the metaverse for the intervention of cognitive decline.

In Frontiers in public health

Cognitive decline is a gradual neurodegenerative process that is affected by genetic and environmental factors. The doctor-patient relationship in the healthcare for cognitive decline is in a "shallow" medical world. With the development of data science, virtual reality, artificial intelligence, and digital twin, the introduction of the concept of the metaverse in medicine has brought alternative and complementary strategies in the intervention of cognitive decline. This article technically analyzes the application scenarios and paradigms of the metaverse in medicine in the field of mental health, such as hospital management, diagnosis, prediction, prevention, rehabilitation, progression delay, assisting life, companionship, and supervision. The metaverse in medicine has made primary progress in education, immersive consultation, dental disease, and Parkinson's disease, bringing revolutionary prospects for non-pharmacological complementary treatment of cognitive decline and other mental problems. In particular, with the demand for non-face-to-face communication generated by the global COVID-19 epidemic, the needs for uncontactable healthcare service for the elderly have increased. The paradigm of self-monitoring, self-healing, and healthcare experienced by the elderly through the metaverse in medicine, especially from meta-platform, meta-community, and meta-hospital, will be generated, which will reconstruct the service modes for the elderly people. The future map of the metaverse in medicine is huge, which depends on the co-construction of community partners.

Zhou Hao, Gao Jian-Yi, Chen Ying

2022

“Alzheimers disease”, cognitive decline, digital twin, mental health, metaverse in medicine, virtual reality

General General

Interpreting global variations in the toll of COVID-19: The case for context and nuance in hypothesis generation and testing.

In Frontiers in public health

Key points : As of January 2022, the COVID-19 pandemic was on-going, affecting populations worldwide. The potential risks of the Omicron variant (and future variants) still remain an area of active investigation. Thus, the ultimate human toll of SARS-CoV-2, and, by extension, the variations in that toll among diverse populations, remain unresolved. Nonetheless, an extensive literature on causal factors in the observed patterns of COVID-19 morbidity and cause-specific mortality has emerged-particularly at the aggregate level of analysis. This article explores potential pitfalls in the attribution of COVID outcomes to specific factors in isolation by examining a diverse set of potential factors and their interactions.

Methods : We sourced published data to establish a global database of COVID-19 outcomes for 68 countries and augmented these with an array of potential explanatory covariates from a diverse set of sources. We sought population-level aggregate factors from both health- and (traditionally) non-health domains, including: (a) Population biomarkers (b) Demographics and infrastructure (c) Socioeconomics (d) Policy responses at the country-level. We analyzed these data using (OLS) regression and more flexible non-parametric methods such as recursive partitioning, that are useful in examining both potential joint factor contributions to variations in pandemic outcomes, and the identification of possible interactions among covariates across these domains.

Results : Using the national obesity rates of 68 countries as an illustrative predictor covariate of COVID-19 outcomes, we observed marked inconsistencies in apparent outcomes by population. Importantly, we also documented important variations in outcomes, based on interactions of health factors with covariates in other domains that are traditionally not related to biomarkers. Finally, our results suggest that single-factor explanations of population-level COVID-19 outcomes (e.g., obesity vs. cause-specific mortality) appear to be confounded substantially by other factors.

Conclusions/implications : Our methods and findings suggest that a full understanding of the toll of the COVID-19 pandemic, as would be central to preparing for similar future events, requires analysis within and among diverse variable domains, and within and among diverse populations. While this may seem apparent, the bulk of the recent literature on the pandemic has focused on one or a few of these drivers in isolation. Hypothesis generation and testing related to pandemic outcomes will benefit from accommodating the nuance of covariate interactions, in an epidemiologic context. Finally, our results add to the literature on the ecological fallacy: the attempt to infer individual drivers and outcomes from the study of population-level aggregates.

Stein Roger M, Katz David L

2022

COVID, health economics, health policy, lifestyle factors, machine learning, obesity, pandemic, statistical methods

General General

Ensemble learning-based feature selection for phosphorylation site detection.

In Frontiers in genetics ; h5-index 62.0

SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.

Liu Songbo, Cui Chengmin, Chen Huipeng, Liu Tong

2022

SARS-cov-2, ensemble learning (EN), feature selection (FS), marchine-learning, phosphorylation site

General General

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.

In Chaos, solitons, and fractals

In this work, we propose a new mathematical modelling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Khan Junaid Iqbal, Ullah Farman, Lee Sungchang

2022-Oct-31

COVID-19, Compartment model, Control theory, Deep learning

Public Health Public Health

Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection.

In International journal of applied earth observation and geoinformation : ITC journal

Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common approach to securing clean water supplies for this purpose is via wastewater treatment. To date, an effective method of detecting wastewater treatment plants (WWTP) accurately and automatically via remote sensing is unavailable. In this paper, we provide a solution to this task by proposing a novel joint deep learning (JDL) method that consists of a fine-tuned object detection network and a multi-task residual attention network (RAN). By leveraging OpenStreetMap (OSM) and multimodal remote sensing (RS) data, our JDL method is able to simultaneously tackle two different tasks: land use land cover (LULC) and WWTP classification. Moreover, JDL exploits the complementary effects between these tasks for a performance gain. We train JDL using 4,187 WWTP features and 4,200 LULC samples and validate the performance of the proposed method over a selected area around Stuttgart with 723 WWTP features and 1,200 LULC samples to generate an LULC classification map and a WWTP detection map. Extensive experiments conducted with different comparative methods demonstrate the effectiveness and efficiency of our JDL method in automatic WWTP detection in comparison with single-modality/single-task or traditional survey methods. Moreover, lessons learned pave the way for future works to simultaneously and effectively address multiple large-scale mapping tasks (e.g., both mapping LULC and detecting WWTP) from multimodal RS data via deep learning.

Li Hao, Zech Johannes, Hong Danfeng, Ghamisi Pedram, Schultz Michael, Zipf Alexander

2022-Jun

GeoAI, OpenStreetMap, SDG 6, multi-task learning, multimodal, object detection, volunteered geographic information, wastewater treatment

General General

Socially facilitative robots for older adults to alleviate social isolation: A participatory design workshop approach in the US and Japan.

In Frontiers in psychology ; h5-index 92.0

Social technology can improve the quality of older adults' social lives and mitigate negative mental and physical health outcomes associated with loneliness, but it should be designed collaboratively with this population. In this paper, we used participatory design (PD) methods to investigate how robots might be used as social facilitators for middle-aged and older adults (age 50+) in both the US and Japan. We conducted PD workshops in the US and Japan because both countries are concerned about the social isolation of these older adults due to their rapidly aging populations. We developed a novel approach to participatory design of future technologies that spends 2/3 of the PD session asking participants about their own life experiences as a foundation. This grounds the conversation in reality, creates rapport among the participants, and engages them in creative critical thinking. Then, we build upon this foundation, pose an abstract topic, and ask participants to brainstorm on the topic based on their previous discussion. In both countries, participants were eager to actively discuss design ideas for socially facilitative robots and imagine how they might improve their social lives. US participants suggested design ideas for telepresence robots, social distancing robots, and social skills artificial intelligence programs, while Japanese participants suggested ideas for pet robots, robots for sharing experiences, and easy-to-operate instructor robots. Comparing these two countries, we found that US participants saw robots as tools to help facilitate their social connections, while Japanese participants envisioned robots to function as surrogate companions for their parents and distract them from loneliness when they were unavailable. With this paper, we contribute to the literature in two main ways, presenting: (1) A novel approach to participatory design of future technologies that grounds participants in their everyday experience, and (2) Results of the study indicating how middle-aged and older adults from the US and Japan wanted technologies to improve their social lives. Although we conducted the workshops during the COVID-19 pandemic, many findings generalized to other situations related to social isolation, such as older adults living alone.

Fraune Marlena R, Komatsu Takanori, Preusse Harrison R, Langlois Danielle K, Au Rachel H Y, Ling Katrina, Suda Shogo, Nakamura Kiko, Tsui Katherine M

2022

Japan, US, cross-cultural study, experience-grounded participatory design, human-robot interaction, older adults, social isolation, social robots

Surgery Surgery

"The show must go on": Aftermath of Covid-19 on anesthesiology residency programs.

In Saudi journal of anaesthesia

COVID-19 has caused tectonic changes in the personal and professional lives of anesthesiologists and, among several aspects, anesthesiology residency and sub-specialty training has also undergone an unforeseen overhaul. We read the articles published on the impact of COVID-19 on training of anesthesiologists and set out to extract and narrate all the significant observations. At the outset, we begin by explaining how this pandemic posed a threat to the safety of the residents and mitigating measures like PPE and barriers that have now become 'the new normal'. Sub-specialties like critical care, cardiac anesthesia, pain and palliative care have also faced difficulty in imparting training due to an initial dearth in elective surgery case load but have adapted innovative measures to overcome that. Initially, conducting thesis and research became difficult due to problems in achieving the desires sample size needed to get significant results, but this pandemic has emerged as a dynamic laboratory where topics like 'psychological impact of COVID-19' and 'development of artificial intelligence models in COVID -19 ICUs' came into the fore. Pattern of examination has also become virtual and webinars showed how knowledge, with the right medium, has the potential of global outreach. As the pandemic took a toll on the mental health of the residents, attention was paid to this previously neglected aspect and ensuring their emotional well-being became a priority to avoid the issue of burn-out. We comment on how what initially was considered a scary problem, actually paved way for growth. It brought attention to safety, innovation, new tools for training, finding solutions within constraints, continuing developing our residents into future leaders who were also trained for mitigating disasters. Changes like online education, research on socio-economic impact, priority to mental health and artificial intelligence are here to stay and by imbibing it, we ensure that 'the show must go on'.

Jaju Rishabh, Saxena Medhavi, Paliwal Naveen, Bihani Pooja, Tharu Vidya

Academic, Covid-19, anesthesia, burnout, resident training

Public Health Public Health

COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification.

In Computational intelligence and neuroscience

The spread of COVID-19 has affected more than 200 countries and has caused serious public health concerns. The infected cases are on the increase despite the effectiveness of the vaccines. An efficient and quick surveillance system for COVID-19 can help healthcare decision-makers to contain the virus spread. In this study, we developed a novel framework using machine learning (ML) models capable of detecting COVID-19 accurately at an early stage. To estimate the risks, many models use social networking sites (SNSs) in tracking the disease outbreak. Twitter is one of the SNSs that is widely used to create an efficient resource for disease real-time analysis and can provide an early warning for health officials. We introduced a pipeline framework of outbreak prediction that incorporates a first-step hybrid method of word embedding for tweet classification. In the second step, we considered the classified tweets with external features such as vaccine rate associated with infected cases passed to machine learning algorithms for daily predictions. Thus, we applied different machine learning models such as the SVM, RF, and LR for classification and the LSTM, Prophet, and SVR for prediction. For the hybrid word embedding techniques, we applied TF-IDF, FastText, and Glove and a combination of the three features to enhance the classification. Furthermore, to improve the forecast performance, we incorporated vaccine data as input together with tweets and confirmed cases. The models' performance is more than 80% accurate, which shows the reliability of the proposed study.

Didi Y, Walha A, Ben Halima M, Wali A

2022

General General

Dual-Proxy Modeling for Masked Face Recognition.

In Procedia computer science

With the recent worldwide COVID-19 pandemic, almost everyone wears a mask daily, leading to severe degradation in the accuracy of conventional face recognition systems. Several works improve the performance of masked faces by adopting synthetic masked face images for training. However, such methods often cause performance degradation on unmasked faces, raising the contradiction between the face recognition system's accuracy on unmasked and masked faces. In this paper, we propose a dual-proxy face recognition training method to improve masked faces' performance while maintaining unmasked faces' performance. Specifically, we design two fully-connected layers as the unmasked and masked feature space proxies to alleviate the significant difference between the two data distributions. The cross-space constraints are adopted to ensure the intra-class compactness and inter-class discrepancy. Extensive experiments on popular unmasked face benchmarks and masked face benchmarks, including real-world mask faces and the generated mask faces, demonstrate our method's superiority over the state-of-the-art methods on masked faces without incurring a notable accuracy degradation on unmasked faces.

Shuhui Wang, Xiaochen Mao

2022

Dual-Proxy, Masked face recognition, deep learning, neural network

General General

Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

ArXiv Preprint

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempPNet interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good - collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression prediction from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation of prediction outcomes and making informed interventions.

Jiaheng Xie, Xiaohang Zhao, Xiang Liu, Xiao Fang

2022-11-08

General General

Insight into the nonlinear effect of COVID-19 on well-being in China: Commuting, a vital ingredient.

In Journal of transport & health

Background : COVID-19 had a devastating impact on people's work, travel, and well-being worldwide. As one of the first countries to be affected by the virus and develop relatively well-executed pandemic control, China has witnessed a significant shift in people's well-being and habits, related to both commuting and social interaction. In this context, what factors and the extent to which they contribute to well-being are worth exploring.

Methods : Through a questionnaire survey within mainland China, 688 valid sheets were collected, capturing various aspects of individuals' life, including travel, and social status. Focusing on commuting and other factors, a Gradient Boosting Decision Tree (GBDT) model was developed based on 300 sheets reporting working trips, to analyze the effects on well-being. Two indicators, i.e., the Relative Importance (RI) and Partial Dependency Plot (PDP), were used to quantify and visualize the effects of the explanatory factors and the synergy among them.

Results : Commuting characteristics are the most critical ingredients, followed by social interactions to explain subjective well-being. Commuting stress poses the most substantial effect. Less stressful commuting trips can solidly improve overall well-being. Better life satisfaction is linked with shorter confinement periods and increased restriction levels. Meanwhile, the switch from in-person to online social interactions had less impact on young people's life satisfaction. Older people were unsatisfied with this change, which had a significant negative impact on their life satisfaction.

Conclusions : From the synergy of commuting stress and commuting time on well-being, the effect of commuting time on well-being is mediated by commuting stress in the case of China. Even if one is satisfied with online communication, the extent of enhancement on well-being is minimal, for it still cannot replace face-to-face interaction. The findings can be beneficial in improving the overall well-being of society during the pandemic and after the virus has been eradicated.

Dong Yinan, Sun Yilin, D Waygood E Owen, Wang Bobin, Huang Pei, Naseri Hamed

2022-Oct-31

COVID-19, Commuting behavior, Machine learning, Social interaction, Well-being

General General

A Critical Review of Global Digital Divide and the Role of Technology in Healthcare.

In Cureus

Healthcare and technology, the fusion of these two distinct sciences can be traced back to the Vedic era. Regrettably, while it is evident that the journey of advancements in knowledge and innovation leading to the advent of technology to better the health of mankind is not a recent one, owing to inexistent means of transfer of knowledge, these contraptions stayed mostly localized to the regions of their inventors. This article seeks to review the vital role that technology has in bettering the health status of the global community and the challenges associated with healthcare technologies like inequity in connectivity, affordability, and accessibility. Technology and artificial intelligence are integrated to the best of the health systems across the world but these advancements are not accessible to a considerable part of the global population. While affordability, the absence of a steady internet supply, and the lack of a device to use the technology are the major impediments causing this digital divide, cultural factors and health literacy also contribute to this scenario. Nevertheless, access to the internet has been recognized as a basic need by all governments around the globe. The COVID-19 pandemic shook the health systems of developed and developing countries alike and has made every administration feel the urgency in making healthcare more accessible. Having seamless internet coverage and setups to make telemedicine or online consultations possible, can contribute significantly in paving the path to making our societies prosperous and healthier. With the world's consensus about this goal, efforts now should be focused on research and development for making these technologies more affordable and accessible without compromising their utility.

Reddy Himabindu, Joshi Shiv, Joshi Abhishek, Wagh Vasant

2022-Sep

digital divide, digital health, e-health, healthcare access, healthcare technology, inequity

Surgery Surgery

Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China.

In Digital health

Background : Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China.

Methods : Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization.

Results : Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in predicting infection level, while AUC=0.8436 and Recall (Weighted avg) = 0.8486 in predicting mortality ratio. This study also identifies two sets of essential clinical features. One is C-reactive protein (CRP) or high sensitivity C-reactive protein (hs-CRP) and the other is chest tightness, age, and pleural effusion.

Conclusions : Two homogeneous ensemble models are proposed to predict infection levels and mortality of COVID-19 patients in China. New findings of clinical features for benefiting the machine learning models are reported. The evaluation of an actual dataset collected from January 3 to March 8, 2020 demonstrates the effectiveness of the models by comparing them with state-of-the-art models in prediction.

Wang Jiafeng, Zhou Xianlong, Hou Zhitian, Xu Xiaoya, Zhao Yueyue, Chen Shanshan, Zhang Jun, Shao Lina, Yan Rong, Wang Mingshan, Ge Minghua, Hao Tianyong, Tu Yuexing, Huang Haijun

COVID-19, Ensemble model, electronic health records, machine learning, prediction models

Radiology Radiology

Mental health and chest CT scores mediate the relationship between COVID-19 vaccination status and seroconversion time: A cross-sectional observational study in B.1.617.2 (Delta) infection patients.

In Frontiers in public health

Background : The coronavirus disease (COVID-19) pandemic, which has been ongoing for more than 2 years, has become one of the largest public health issues. Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is one of the most important interventions to mitigate the COVID-19 pandemic. Our objective is to investigate the relationship between vaccination status and time to seroconversion.

Methods : We conducted a cross-sectional observational study during the SARS-CoV-2 B.1.617.2 outbreak in Jiangsu, China. Participants who infected with the B.1.617.2 variant were enrolled. Cognitive performance, quality of life, emotional state, chest computed tomography (CT) score and seroconversion time were evaluated for each participant. Statistical analyses were performed using one-way ANOVA, univariate and multivariate regression analyses, Pearson correlation, and mediation analysis.

Results : A total of 91 patients were included in the analysis, of whom 37.3, 25.3, and 37.3% were unvaccinated, partially vaccinated, and fully vaccinated, respectively. Quality of life was impaired in 30.7% of patients, especially for mental component summary (MCS) score. Vaccination status, subjective cognitive decline, and depression were risk factors for quality-of-life impairment. The chest CT score mediated the relationship of vaccination status with the MCS score, and the MCS score mediated the relationship of the chest CT score with time to seroconversion.

Conclusion : Full immunization course with an inactivated vaccine effectively lowered the chest CT score and improved quality of life in hospitalized patients. Vaccination status could influence time to seroconversion by affecting CT score and MCS score indirectly. Our study emphasizes the importance of continuous efforts in encouraging a full vaccination course.

Zhang Wen, Chen Qian, Dai Jinghong, Lu Jiaming, Li Jie, Yi Yongxiang, Fu Linqing, Li Xin, Liu Jiani, Liufu Jinlong, Long Cong, Zhang Bing

2022

B.1.617.2 Delta variant, COVID-19, SARS-CoV-2, mental health, seroconversion time, vaccination

General General

CELLS: A Parallel Corpus for Biomedical Lay Language Generation

ArXiv Preprint

Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

Yue Guo, Wei Qiu, Gondy Leroy, Sheng Wang, Trevor Cohen

2022-11-07

General General

Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs.

In Journal of medical radiation sciences

INTRODUCTION : Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images.

METHODS : In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance.

RESULTS : The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90).

CONCLUSIONS : We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.

Nakashima Maoko, Uchiyama Yoshikazu, Minami Hirotake, Kasai Satoshi

2022-Nov-05

Artificial intelligence, COVID-19, portable chest X-ray, prognosis prediction, radiomics

oncology Oncology

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study.

In Infectious diseases and therapy

INTRODUCTION : In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome.

METHODS : This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications.

RESULTS : We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome.

CONCLUSIONS : Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

Styrzynski Filip, Zhakparov Damir, Schmid Marco, Roqueiro Damian, Lukasik Zuzanna, Solek Julia, Nowicki Jakub, Dobrogowski Milosz, Makowska Joanna, Sokolowska Milena, Baerenfaller Katja

2022-Nov-04

COVID-19 prognosis, Laboratory parameters, Machine learning, Predictive features, SARS-CoV-2 diagnosis

General General

Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks.

In PloS one ; h5-index 176.0

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19.

Nguyen Thao, Pham Hieu H, Le Khiem H, Nguyen Anh-Tu, Thanh Tien, Do Cuong

2022

General General

Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic.

In IEEE reviews in biomedical engineering

Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.

Gendy Maggie Ezzat Gaber, Yuce Mehmet Rasit

2022-Nov-04

Radiology Radiology

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

In Quantitative imaging in medicine and surgery

Background : The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset.

Methods : In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy.

Results : Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2 deep-learning vs. threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2 deep-learning vs. CCS= 0.80, r2 threshold vs. CCS=0.63).

Conclusions : The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Fervers Philipp, Fervers Florian, Jaiswal Astha, Rinneburger Miriam, Weisthoff Mathilda, Pollmann-Schweckhorst Philip, Kottlors Jonathan, Carolus Heike, Lennartz Simon, Maintz David, Shahzad Rahil, Persigehl Thorsten

2022-Nov

Coronavirus disease 2019 (COVID-19), X-ray computed, biomarkers, linear models, pneumonia, tomography

General General

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

In Computers in biology and medicine

BACKGROUND : COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians.

METHOD : Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score.

RESULTS : Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity.

CONCLUSIONS : These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

Hasan M, Bath P A, Marincowitz C, Sutton L, Pilbery R, Hopfgartner F, Mazumdar S, Campbell R, Stone T, Thomas B, Bell F, Turner J, Biggs K, Petrie J, Goodacre S

2022-Aug-28

Artificial neural networks, COVID-19, Emergency services, Extreme gradient boosting, Logistic regression, Stacking ensemble, Support vector machine

General General

D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.

In Computers in biology and medicine

The number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial explosions, making it almost impossible to experimentally determine their binding affinities to human angiotensin-converting enzyme 2 (hACE2). Although computational prediction is an alternative way, there is still no online platform to predict the mutation effect of RBD on the hACE2 binding affinity until now. In this study, we developed a free online platform based on deep learning models, namely D3AI-Spike, for quickly predicting binding affinity between spike RBD mutants and hACE2. The models based on CNN and CNN-RNN methods have the concordance index of around 0.8. Overall, the test results of the models are in agreement with the experimental data. To further evaluate the prediction power of D3AI-Spike, we predicted and experimentally determined the binding affinity of a VUM (variants under monitoring) variant IHU (B.1.640.2), which has fourteen amino acid substitutions, including N501Y and E484K, and 9 deletions located in the spike protein. The predicted average affinity score for wild-type RBD and IHU to hACE2 are 0.483 and 0.438, while the determined Kaff values are 5.39 ± 0.38 × 107 L/mol and 1.02 ± 0.47 × 107 L/mol, respectively, demonstrating the strong predictive power of D3AI-Spike. We think D3AI-Spike will be helpful to the viral transmission prediction for the new emerging SARS-CoV-2 variants. D3AI-Spike is now available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-Spike/index.php.

Han Jiaxin, Liu Tingting, Zhang Xinben, Yang Yanqing, Shi Yulong, Li Jintian, Ma Minfei, Zhu Weiliang, Gong Likun, Xu Zhijian

2022-Oct-25

COVID-19, D3AI-Spike, Deep learning, ELISA, Protein-protein interaction

General General

Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning.

In PloS one ; h5-index 176.0

We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine learning algorithms is difficult due to a couple of reasons: There is limited and error-afflicted initial data in a highly complex biological system combined with the need to make robust, transparent and safe decisions. We demonstrate a suitable method that combines data imputation by a kNN model using a custom distance with state representation by discretization using clustering, and that enables superhuman decision-making using speedy Q-learning in the framework of distributional RL. Compared to clinicians, the recovery rate is increased by more than 3% on the test data set. Our results illustrate how risk-aware RL agents can play a decisive role in critical situations such as the treatment of sepsis patients, a situation acerbated due to the COVID-19 pandemic (Martineau 2020). In addition, we emphasize the tractability of the methodology and the learning behavior while addressing some criticisms of the previous work (Komorowski et al. 2018) on this topic.

Böck Markus, Malle Julien, Pasterk Daniel, Kukina Hrvoje, Hasani Ramin, Heitzinger Clemens

2022

General General

Critical role of information and communication technology in nursing during the COVID-19 pandemic: A qualitative study.

In Journal of nursing management ; h5-index 43.0

AIM : To examine the need for information and communication technology (ICT)-based nursing care in improving patient management during the pandemic.

BACKGROUND : Maintaining traditional approaches to nursing in the ongoing coronavirus disease (COVID-19) pandemic predisposes healthcare systems to a risk of diminished quality of care. Using ICT (real-time videoconferencing, mobile robots, and artificial intelligence) could reduce burnout and infection risks by minimizing face-to-face contact.

METHOD : Qualitative descriptive design with content analysis.

RESULTS : Overall, 24 participants (14 nurses, six medical/nursing informatics experts, and four technology experts) were interviewed. Three main themes were extracted: Emerging challenges for nurses due to COVID-19, impact of new technology on patient and nurse experiences, and concerns with implementation of technology.

CONCLUSION : A significant portion of nurses' work was unrelated to professional nursing, causing burnout. ICT could help reduce nurses' burden by facilitating environmental management, non-contact communication, and providing emotional support for patients.

IMPLICATIONS FOR NURSING MANAGEMENT : Establishing an ICT-based nursing care system that considers the physical environment and communication infrastructure of healthcare institutions, user's digital health literacy, and user safety to effectively manage non-nursing care-related activities and undertake tasks that can be delegated may improve the quality of care for quarantined patients and reduce risk of cross-infection.

Yoo Hye Jin, Lee Hyeongsuk

2022-Nov-03

COVID-19, artificial intelligence, information technology, nursing care, patient isolation

General General

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants.

In bioRxiv : the preprint server for biology

The COVID-19 pandemic has highlighted how viral variants that escape monoclonal antibodies can limit options to control an outbreak. With the emergence of the SARS-CoV-2 Omicron variant, many clinically used antibody drug products lost in vitro and in vivo potency, including AZD7442 and its constituent, AZD1061 [VanBlargan2022, Case2022]. Rapidly modifying such antibodies to restore efficacy to emerging variants is a compelling mitigation strategy. We therefore sought to computationally design an antibody that restores neutralization of BA.1 and BA.1.1 while simultaneously maintaining efficacy against SARS-CoV-2 B.1.617.2 (Delta), beginning from COV2-2130, the progenitor of AZD1061. Here we describe COV2-2130 derivatives that achieve this goal and provide a proof-of-concept for rapid antibody adaptation addressing escape variants. Our best antibody achieves potent and broad neutralization of BA.1, BA.1.1, BA.2, BA.2.12.1, BA.4, BA.5, and BA.5.5 Omicron subvariants, where the parental COV2-2130 suffers significant potency losses. This antibody also maintains potency against Delta and WA1/2020 strains and provides protection in vivo against the strains we tested, WA1/2020, BA.1.1, and BA.5. Because our design approach is computational-driven by high-performance computing-enabled simulation, machine learning, structural bioinformatics and multi-objective optimization algorithms-it can rapidly propose redesigned antibody candidates aiming to broadly target multiple escape variants and virus mutations known or predicted to enable escape.

Desautels Thomas A, Arrildt Kathryn T, Zemla Adam T, Lau Edmond Y, Zhu Fangqiang, Ricci Dante, Cronin Stephanie, Zost Seth J, Binshtein Elad, Scheaffer Suzanne M, Engdahl Taylor B, Chen Elaine, Goforth John W, Vashchenko Denis, Nguyen Sam, Weilhammer Dina R, Lo Jacky Kai-Yin, Rubinfeld Bonnee, Saada Edwin A, Weisenberger Tracy, Lee Tek-Hyung, Whitener Bradley, Case James B, Ladd Alexander, Silva Mary S, Haluska Rebecca M, Grzesiak Emilia A, Bates Thomas W, Petersen Brenden K, Thackray Larissa B, Segelke Brent W, Lillo Antonietta Maria, Sundaram Shivshankar, Diamond Michael S, Crowe James E, Carnahan Robert H, Faissol Daniel M

2022-Oct-24

General General

A novel deep learning-based method for COVID-19 pneumonia detection from CT images.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists.

AIMS : This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia.

METHODS : The total study population was 437. The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists.

RESULTS : In the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed significantly better than all four radiologists in terms of time consumption (35 min vs. 75 min, 93 min, 79 min, and 82 min).

CONCLUSION : The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.

Luo Ju, Sun Yuhao, Chi Jingshu, Liao Xin, Xu Canxia

2022-Nov-02

Artificial intelligence, COVID-19, CT image, Community acquired pneumonia, Deep learning

Public Health Public Health

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

In Science translational medicine ; h5-index 138.0

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Cano-Gamez Eddie, Burnham Katie L, Goh Cyndi, Allcock Alice, Malick Zunaira H, Overend Lauren, Kwok Andrew, Smith David A, Peters-Sengers Hessel, Antcliffe David, McKechnie Stuart, Scicluna Brendon P, van der Poll Tom, Gordon Anthony C, Hinds Charles J, Davenport Emma E, Knight Julian C, Webster Nigel, Galley Helen, Taylor Jane, Hall Sally, Addison Jenni, Roughton Sian, Tennant Heather, Guleri Achyut, Waddington Natalia, Arawwawala Dilshan, Durcan John, Short Alasdair, Swan Karen, Williams Sarah, Smolen Susan, Mitchell-Inwang Christine, Gordon Tony, Errington Emily, Templeton Maie, Venatesh Pyda, Ward Geraldine, McCauley Marie, Baudouin Simon, Higham Charley, Soar Jasmeet, Grier Sally, Hall Elaine, Brett Stephen, Kitson David, Wilson Robert, Mountford Laura, Moreno Juan, Hall Peter, Hewlett Jackie, McKechnie Stuart, Garrard Christopher, Millo Julian, Young Duncan, Hutton Paula, Parsons Penny, Smiths Alex, Faras-Arraya Roser, Soar Jasmeet, Raymode Parizade, Thompson Jonathan, Bowrey Sarah, Kazembe Sandra, Rich Natalie, Andreou Prem, Hales Dawn, Roberts Emma, Fletcher Simon, Rosbergen Melissa, Glister Georgina, Cuesta Jeronimo Moreno, Bion Julian, Millar Joanne, Perry Elsa Jane, Willis Heather, Mitchell Natalie, Ruel Sebastian, Carrera Ronald, Wilde Jude, Nilson Annette, Lees Sarah, Kapila Atul, Jacques Nicola, Atkinson Jane, Brown Abby, Prowse Heather, Krige Anton, Bland Martin, Bullock Lynne, Harrison Donna, Mills Gary, Humphreys John, Armitage Kelsey, Laha Shond, Baldwin Jacqueline, Walsh Angela, Doherty Nicola, Drage Stephen, Ortiz-Ruiz de Gordoa Laura, Lowes Sarah, Higham Charley, Walsh Helen, Calder Verity, Swan Catherine, Payne Heather, Higgins David, Andrews Sarah, Mappleback Sarah, Hind Charles, Garrard Chris, Watson D, McLees Eleanor, Purdy Alice, Stotz Martin, Ochelli-Okpue Adaeze, Bonner Stephen, Whitehead Iain, Hugil Keith, Goodridge Victoria, Cawthor Louisa, Kuper Martin, Pahary Sheik, Bellingan Geoffrey, Marshall Richard, Montgomery Hugh, Ryu Jung Hyun, Bercades Georgia, Boluda Susan, Bentley Andrew, Mccalman Katie, Jefferies Fiona, Knight Julian, Davenport Emma, Burnham Katie, Maugeri Narelle, Radhakrishnan Jayachandran, Mi Yuxin, Allcock Alice, Goh Cyndi

2022-Nov-02

General General

Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash.

In Computational economics

Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes.

Malladi Rama K

2022-Oct-26

Financial econometrics, Forecasting, Machine learning, Recession, Stock market crash

Internal Medicine Internal Medicine

The Impact of COVID-19 on the Behaviors and Attitudes of Children and Adolescents: A Cross-Sectional Study.

In Cureus

Background and objective Over the past few decades, new infectious diseases have emerged, and these have played a key role in changing behavior and lifestyle in all age groups. More recently, with the emergence of the coronavirus disease 2019 (COVID-19) pandemic, governments around the world have made unprecedented efforts to contain the epidemic by implementing quarantine measures, social distancing, and isolating infected individuals. Social behavioral adaptations (e.g., social distancing, isolation, etc.) impact children's and adolescents' lifestyle activities and lead to increased incidence of psychosocial problems, worsening of preexisting mental illness, and fears of infection, uncertainty, isolation, and stress. In light of this, this study aimed to assess the impact of COVID-19 on the behaviors and lifestyles of the children and adolescent population of Pakistan. Methodology A cross-sectional study was conducted involving 323 children and adolescents by targeting parents of children and adolescents in the age group of 4-18 years living in Pakistan. The study was conducted from April 2021 to September 2021. A well-designed structured questionnaire was used to collect data about the sociodemographic profile, attitudes, and behavioral factors impacted by COVID-19 in children and adolescents. SPSS Statistics version 23 (IBM, Armonk, NY) was used to enter and analyze data. Results Parents or caregivers of a total of 189 male and 134 female children aged between four and 18 years took part in this study. During COVID-19, the consumption of fast food and fried foods by children and adolescents increased significantly. In this study, out of 323 participants, almost all (289, 89.5%) had increased their screen time significantly. Nearly half of the total individuals experienced the feeling of depression and loneliness during the pandemic. Additionally, some children and adolescents felt fearful when leaving home. COVID-19 lockdowns have led to many changes in children's and adolescents' lifestyle habits. They reduced physical contact with others due to the fear of transmission of COVID-19. Based on our findings, the pandemic and its containment strategies have adversely affected the behaviors, lifestyles, and attitudes of children and adolescents. Conclusion Governments around the world have imposed social distancing during the COVID-19 pandemic, leading to adverse short-term and long-term negative mental health issues such as unhappiness, fear, worry, irritability, depressive symptoms, anxiety, and post-traumatic stress disorder (PTSD). Interventions are needed to focus on building resilience in children and adolescents, addressing their fears and concerns through better communication, encouraging routine and physical activity, and taking measures to alleviate loneliness.

Annam Swetha, Fleming Maria F, Gulraiz Azouba, Zafar Muhammad Talha, Khan Saif, Oghomitse-Omene Princess T, Saleemuddin Sana, Patel Parth, Ahsan Zainab, Qavi Muhammad Saqlain S

2022-Sep

adolescent, child and adolescent psychiatry, child attitudes, child behaviour, covid-19, mental health

General General

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

In Annals of operations research

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

Ibrahim Bassam A, Elamer Ahmed A, Abdou Hussein A

2022-Oct-28

Bitcoin, COVID-19, Crude oil, Cryptocurrencies, Machine learning, Neural networks

General General

Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.

In Applied intelligence (Dordrecht, Netherlands)

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors' relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

Wu Binrong, Wang Lin, Zeng Yu-Rong

2022-Oct-27

COVID-19, Deep learning, Interpretable tourism demand forecasting, Text mining

General General

The m7G Modification Level and Immune Infiltration Characteristics in Patients with COVID-19.

In Journal of multidisciplinary healthcare

Purpose : The 7-methylguanosine (m7G)-related genes were used to identify the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19) and to identify possible therapeutic targets.

Patients and Methods : The GSE157103 dataset provides the transcriptional spectrum and clinical information required to analyze the expression of m7G-related genes and the disease subtypes. R language was applied for immune infiltration analysis, functional enrichment analysis, and nomogram model construction.

Results : Most m7G-related genes were up-regulated in COVID-19 and were closely related to immune cell infiltration. Disease subtypes were grouped using a clustering algorithm. It was found that the m7G-cluster B was associated with higher immune infiltration, lower mechanical ventilation, lower intensive care unit (ICU) status, higher ventilator-free days, and lower m7G scores. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between m7G-cluster A and B were enriched in viral infection and immune-related aspects, including COVID-19 infection; Th17, Th1, and Th2 cell differentiation, and human T-cell leukemia virus 1 infection. Finally, through machine learning, six disease characteristic genes, NUDT4B, IFIT5, LARP1, EIF4E, LSM1, and NUDT4, were screened and used to develop a nomogram model to estimate disease risk.

Conclusion : The expression of most m7G genes was higher in COVID-19 patients compared with that in non-COVID-19 patients. The m7G-cluster B showed higher immune infiltration and milder symptoms. The predictive nomogram based on the six m7G genes can be used to accurately assess risk.

Lu Lingling, Zheng Jiaolong, Liu Bang, Wu Haicong, Huang Jiaofeng, Wu Liqing, Li Dongliang

2022

7-methylguanosine, COVID-19, SARS-CoV-2, immune cells, nomogram, risk

General General

A comparison of Covid-19 cases and deaths in Turkey and in other countries.

In Network modeling and analysis in health informatics and bioinformatics

In this study, the characteristics of the Covid-19 pandemic in Turkey are examined in terms of the number of cases and deaths, and a characteristic prediction is made with an approach that employs artificial intelligence. The number of cases and deaths are estimated using the number of tests, the numbers of seriously ill and recovered patients as parameters. The machine learning methods used are linear regression, polynomial regression, support vector regression with different kernel functions, decision tree and artificial neural networks. The obtained results are compared by calculating the coefficient of determination (R 2), and the mean absolute percentage error (MAPE) values. When R 2 and MAPE values are compared, it is seen that the optimal results for cases in Turkey are obtained with the decision tree, for deaths with polynomial regression method. The results reached for the United States of America and Russia are similar and the optimal results are obtained by polynomial regression. However, while the optimal results are obtained by neural networks in the Indian data, linear regression for the cases in the Brazilian data, neural network for the deaths, decision tree for the cases in France, polynomial regression for the deaths, neural network for the cases in the UK data and decision tree for the deaths are the methods that produced the optimal results. These results also give an idea about the similarities and differences of country characteristics.

Çağlar Oğuzhan, Özen Figen

2022

Artificial neural network, Covid-19, Decision tree, Linear regression, Polynomial regression, Support vector regression

General General

A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.

Aslan Muhammet Fatih

2022-Dec-15

AlexNet, COVID-19, Convolutional neural networks, DeepLabV3+, Semantic segmentation, Support vector machine

General General

Predictive models for COVID-19 detection using routine blood tests and machine learning.

In Heliyon

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.

Kistenev Yury V, Vrazhnov Denis A, Shnaider Ekaterina E, Zuhayri Hala

2022-Oct

Blood tests, COVID-19, Machine learning

General General

Defending against adversarial attacks on Covid-19 classifier: A denoiser-based approach.

In Heliyon

Covid-19 has posed a serious threat to the existence of the human race. Early detection of the virus is vital to effectively containing the virus and treating the patients. Profound testing methods such as the Real-time reverse transcription-polymerase chain reaction (RT-PCR) test and the Rapid Antigen Test (RAT) are being used for detection, but they have their limitations. The need for early detection has led researchers to explore other testing techniques. Deep Neural Network (DNN) models have shown high potential in medical image classification and various models have been built by researchers which exhibit high accuracy for the task of Covid-19 detection using chest X-ray images. However, it is proven that DNNs are inherently susceptible to adversarial inputs, which can compromise the results of the models. In this paper, the adversarial robustness of such Covid-19 classifiers is evaluated by performing common adversarial attacks, which include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Using these attacks, it is found that the accuracy of the models for Covid-19 samples decreases drastically. In the medical domain, adversarial training is the most widely explored technique to defend against adversarial attacks. However, using this technique requires replacing the original model and retraining it by including adversarial samples. Another defensive technique, High-Level Representation Guided Denoiser (HGD), overcomes this limitation by employing an adversarial filter which is also transferable across models. Moreover, the HGD architecture, being suitable for high-resolution images, makes it a good candidate for medical image applications. In this paper, the HGD architecture has been evaluated as a potential defensive technique for the task of medical image analysis. Experiments carried out show an increased accuracy of up to 82% in the white box setting. However, in the black box setting, the defense completely fails to defend against adversarial samples.

Kansal Keshav, Krishna P Sai, Jain Parshva B, R Surya, Honnavalli Prasad, Eswaran Sivaraman

2022-Oct

Adversarial attacks, Deep neural network, Denoiser, FGSM, HGD, Machine learning, PGD

General General

Masked Facial Recognition in Security Systems Using Transfer Learning.

In SN computer science

The COVID-19 is a crisis of unprecedented magnitude, which has resulted in countless casualties and security troubles. In view of recent events of corona virus people are required to wear face masks to protect themselves from getting infected. As a result, a good portion of face (nose and mouth) is hidden by the mask and hence the facial recognition becomes difficult. Many organizations use facial recognition as a means of authentication. Researchers focus on developing rapid and efficient solutions to deal with the ongoing coronavirus pandemic by coming up with suggestions for handling the facial recognition problem. This research paper aims to identify the person, while the face is covered with a facial mask with only eyes and forehead being exposed. The first step involves marking the facial region. Next, using the data set, we will implement an object detection model YOLOv3 to identify unmasked and masked faces. The YOLO v3 object detection model is the best performing model with a detection time of 0.012 s, F1 score of 0.90 and mAP score of 0.92. Experimental results on Real-World Masked-Face-Data set show high recognition performance.

Ramgopal M, Roopesh M Sai, Chowdary M Veeranna, Madhav M, Shanmuga K

2023

Convolutional neural networks (CNN), Face recognition, Machine learning (ML), Object detection model, Transfer learning model

Cardiology Cardiology

The Recent Advances of Mobile Healthcare in Cardiology Practice.

In Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH

Background : Digitalization of healthcare led to the optimization of monitoring, diagnostics, and treatment of the range of disorders. Taking into account recent situation with COVID-19 pandemics, digital technologies allowed to improve management of viral infections via remote monitoring and diagnostics of infected patients. Up to date, various mobile health applications (apps) have been proposed, including apps for the patients diagnosed with cardiovascular pathologies.

Objective : The presented review aimed at the analyses of a range of mHealth solutions used to improve primary cardiac care. In addition, we studied the factors driving and hindering the wide introduction of mHealth services in the clinics.

Methods : The work was based on the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. The publication search was carried out using PubMed, Web of Science, Cochrane Library, Scopus, and Google Scholar databases. Studies published during the period from 2014 until January 2022 were selected for the analysis. The evaluation of risk of bias for the included studies was conducted using the Cochrane Collaboration Risk of Bias tool.

Results and Discussion : An overall 5513 studies were assessed for eligibility after which 39 studies were included.. The main trend in the mobile health for cardiological applications is the use of different types of wearable devices and Artificial Intelligence-platforms. In fact, mobile technology allows remotely to monitor, interpret, and analyze biomedical data collected from the patient.

Conclusion : The results of this literature search demonstrated that patients diagnosed with cardiovascular disorders can potentially benefit from the application of mHealth in cardiology. However, despite the proven advantages of mHealth for cardiology, there are many challenges and concerns regarding effectiveness, safety, reliability and the lack of official regulation and guidelines from official organizations. Such issues require solutions and further work towards a wide implementation of mHealth technologies in cardiac practice.

Kulbayeva Shynar, Tazhibayeva Karlygash, Seiduanova Laura, Smagulova Indira, Mussina Aiman, Tanabayeva Shynar, Fakhradiyev Ildar, Saliev Timur

2022-Sep

cardiology, mobile app, mobile applications, telemedicine

General General

Digital phenotyping for classification of anxiety severity during COVID-19.

In Frontiers in digital health

COVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2 and CPSS4, which were collected in May and July of 2020, respectively. The survey data consist of up to 102 questions. This work proposes the use of the survey data characteristics to identify the level of anxiety within the Canadian population during the first- and second-phases of COVID-19 and is validated by using the General Anxiety Disorder (GAD)-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top features to represent user anxiety, and support vector machine (SVM) is used to classify the separation of anxiety severity. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77 ± 0.13 % and 97.35 ± 0.11 % for CPSS2 and CPSS4, respectively. After analysis, we compared the results of the first and second phases of COVID-19 and determined a subset of the features that could be represented as pseudo passive (PP) data. The accurate classification provides a proxy on the potential onsets of anxiety to provide tailored interventions. Future works can augment the proposed PP data for carrying out a more detailed digital phenotyping.

Nguyen Binh, Ivanov Martin, Bhat Venkat, Krishnan Sri

2022

COVID-19, anxiety, digital phenotyping, machine learning, mental health

General General

Learning effective embedding for automated COVID-19 prediction from chest X-ray images.

In Multimedia systems

The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus.

T N Sree Ganesh, Satish Rishi, Sridhar Rajeswari

2022-Oct-26

AlexNet, COVID-19 prediction, Convolution neural network, Medical image classification, Multitask learning, Siamese neural network, Transfer learning, VGG16

General General

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models.

In Scientific African

Covid-19 remains a global pandemic threatening hundreds of countries in the world. The impact of Covid-19 has been felt in almost every aspect of life and it has introduced globally, a new normal of livelihood. This global pandemic has triggered unparalleled global health and economic crisis. Therefore, modelling and forecasting the dynamics of this pandemic is very crucial as it will help in decision making and strategic planning. Nigeria as the most populous country in Africa and most populous black nation in the world has been adversely affected by Covid-19 pandemic. This study models and compares forecasting performance of regression, ARIMA and Machine Learning models in predicting new cases of Covid-19 in Nigeria. The study obtained data on daily new cases of Covid-19 in Nigeria between 27th February, 2020 and 30th November, 2021. Graphical analysis showed that Nigeria had witnessed three waves of Covid-19 with the first wave between 27th February, 2020 and 23rd October, 2020, the second wave between 24th October, 2020 and 20th June, 2021 and the third wave between 21st June, 2021 and 30th November, 2021.The second wave recorded the highest spikes in new cases compared to the first wave and third wave. Result reveals that in terms of forecasting performance, inverse regression model outperformed other regression models considered as it shows lowest RMSE of 0.4130 compared with other regression models. Also, the ARIMA (4, 1, 4) outperformed other ARIMA models as it reveals the highest R2 of 0.856 (85.6%), least RMSE (0.6364), AIC (-8.6024) and BIC (-8.5299). Result reveals that Fine tree which is one of the Machine Learning models is more reliable in forecasting new cases of Covid-19 in Nigeria compared to other models as Fine tree gave the highest R2 of 0.90 (90.0%) and least RMSE of 0.22165. Result of 15 days forecasting indicates that Covid-19 pandemic is not over yet in Nigeria as new cases of Covid-19 is projected to increase on 15/12/2021 with predicted new cases of 988 compared with that of 14/12/2021, where only 729 new cases was predicted. This therefore emphasizes the need to strengthen and maintain the existing Covid-19 preventive measures in Nigeria.

Busari S I, Samson T K

2022-Nov

ARIMA, Forecasting, Machine learning, Time series

General General

Ultrasensitive NO Sensor Based on a Nickel Single-Atom Electrocatalyst for Preliminary Screening of COVID-19.

In ACS sensors

A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. A rapid and economical method for preliminary screening of COVID-19 may help to control the COVID-19 pandemic. Here, we report a nickel single-atom electrocatalyst that can be printed on a paper-printing sensor for preliminary screening of COVID-19 suspects by efficient detection of fractional exhaled nitric oxide (FeNO). The FeNO value is confirmed to be related to COVID-19 in our exploratory clinical study, and a machine learning model that can accurately classify healthy subjects and COVID-19 patients is established based on FeNO and other features. The nickel single-atom electrocatalyst consists of a single nickel atom with N2O2 coordination embedded in porous acetylene black (named Ni-N2O2/AB). A paper-printed sensor was fabricated with the material and showed ultrasensitive response to NO in the range of 0.3-180 ppb. This ultrasensitive sensor could be applied to preliminary screening of COVID-19 in everyday life.

Zhou Wei, Tan Yi, Ma Jing, Wang Xiao, Yang Li, Li Zhen, Liu Chengcheng, Wu Hao, Sun Lei, Deng Weiqiao

2022-Oct-31

COVID-19, Ni-N2O2/AB, mini-exhaled nitric oxide sensor, preliminary screening, single-atom catalysts

General General

Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect.

In Critical reviews in food science and nutrition ; h5-index 70.0

Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.

Yu Huakun, Liu Shuangping, Qin Hui, Zhou Zhilei, Zhao Hongyuan, Zhang Suyi, Mao Jian

2022-Oct-31

Traditional fermented alcoholic beverages, artificial intelligence, big data, fermentation regulation, microbial community

Radiology Radiology

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

In Technology and health care : official journal of the European Society for Engineering and Medicine

BACKGROUND : Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment.

OBJECTIVE : This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features.

METHOD : P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data.

RESULTS : The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers.

CONCLUSION : This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Zhu Fubao, Zhu Zelin, Zhang Yijun, Zhu Hanlei, Gao Zhengyuan, Liu Xiaoman, Zhou Guanbin, Xu Yan, Shan Fei

2022-Oct-21

COVID-19, CT radiomics features, Severity detection, clinical features, imbalance classification

Ophthalmology Ophthalmology

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.

In Frontiers in medicine

Background : Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.

Methods : This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.

Results : One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.

Conclusion : Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Gunasekeran Dinesh V, Zheng Feihui, Lim Gilbert Y S, Chong Crystal C Y, Zhang Shihao, Ng Wei Yan, Keel Stuart, Xiang Yifan, Park Ki Ho, Park Sang Jun, Chandra Aman, Wu Lihteh, Campbel J Peter, Lee Aaron Y, Keane Pearse A, Denniston Alastair, Lam Dennis S C, Fung Adrian T, Chan Paul R V, Sadda SriniVas R, Loewenstein Anat, Grzybowski Andrzej, Fong Kenneth C S, Wu Wei-Chi, Bachmann Lucas M, Zhang Xiulan, Yam Jason C, Cheung Carol Y, Pongsachareonnont Pear, Ruamviboonsuk Paisan, Raman Rajiv, Sakamoto Taiji, Habash Ranya, Girard Michael, Milea Dan, Ang Marcus, Tan Gavin S W, Schmetterer Leopold, Cheng Ching-Yu, Lamoureux Ecosse, Lin Haotian, van Wijngaarden Peter, Wong Tien Y, Ting Daniel S W

2022

artificial intelligence (AI), implementation, ophthalmology, regulation, translation

Public Health Public Health

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.

In Computer communications

SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. It has shattered the world with its effects on different areas as the pandemic hit the world in a number of waves with different variants and mutations. Each variant and mutation have different transmission and infection rates in the human population. More than 609 million people have tested positive and more than 6.5 million people have died due to this disease as per 14th September 2022. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world. In this paper, we aim to give a holistic overview of COVID-19 its variants, game theory perspective, effects on the different social and economic areas, diagnostic advancements, treatment methods. A taxonomy is made for the proper insight of the work demonstrated in the paper. Finally, we discuss the open issues associated with COVID-19 in different fields and futuristic research trends in the area. The main aim of the paper is to provide comprehensive literature that covers all the areas and provide an expert understanding of the COVID-19 techniques and potentially be further utilized to combat the outbreak of COVID-19.

Deepanshi Budhiraja, Ishan Garg, Deepak Kumar, Neeraj Sharma

2022-Oct-26

COVID-19, Deep learning, Game theory, Machine learning, SARS-CoV-2, Variants

General General

Ensemble hybrid model for Hindi COVID-19 text classification with metaheuristic optimization algorithm.

In Multimedia tools and applications

A SARS-CoV-2 virus has spread around the globe since March 2020. Millions of people infected worldwide with coronavirus. People from every country expressed their sentiments about coronavirus on social media. The aim of this work is to determine the general public opinion of Indian Twitter users about coronavirus. The Hindi tweets posted about COVID-19 is used as input data for sentiment analysis. The natural language processing is applied on input data for feature extraction. Further, the optimal features are selected from the pre-processed data using the metaheuristic based Grey wolf optimization technique. Finally, a hybrid of convolution neural network(CNN) and a long short-term memory (LSTM) model pair is employed to categorize the sentiments as positive, negative, and neutral. The outcome of the proposed model is compared with other machine learning techniques, namely, Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, Support vector machine (SVM), CNN, LSTM, LSTM-CNN, and CNN-LSTM. The highest accuracy of 87.75%, 88.41%, 87.89%, 85.54%, 89.11%, 91.46%, 88.72%, 91.54%, and 92.34% is obtained by Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, SVM, CNN, LSTM, LSTM-CNN, and CNN-LSTM, respectively. The proposed ensemble hybrid model gives the highest 95.54%, 91.44%, 89.63%, and 90.87% classification accuracy, precision, recall, and F-score, respectively.

Jain Vipin, Kashyap Kanchan Lata

2022-Oct-24

COVID-19, Deep learning, Ensemble learning, Grey wolf, Optimization, Sentiment

General General

A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection.

In Multimedia tools and applications

COVID-19 is an ongoing pandemic and the WHO recommends at least one-meter social distance, and the use of medical face masks to slow the disease's transmission. This paper proposes an automated approach for detecting social distance and face masks. Thus, it aims to help the reduction of diseases transferred by respiratory droplets such as COVID-19. For this system, a two-cascaded YOLO is used. The first cascade detects humans in the environment and computes the social distance between them. Then, the second cascade detects human faces with or without a mask. Finally, red bounding boxes encircle the people's images that did not follow the rules. Also, in this paper, we propose a two-part feature extraction approach used with YOLO. The first part of the proposed feature extraction method extracts general features using the transfer learning approach. The second part extracts better features specific to the current task using the LBP layer and classification layers. The best average precision for the human detection task was obtained as 66% using Resnet50 in YOLO. The best average precision for the mask detection was obtained as 95% using Darknet19+LBP with YOLO. Also, another popular object detection network, Faster R-CNN, have been used for comparison purpose. The proposed system performed better than the literature in human and mask detection tasks.

Oztel Ismail, Yolcu Oztel Gozde, Akgun Devrim

2022-Oct-21

Covid-19, Deep learning, Face mask detection, Human detection

General General

From luxury to necessity: Progress of touchless interaction technology.

In Technology in society

Touchless Technology is facilitating the move to Zero User Interface(UI) propelled by the COVID-19 pandemic which has accelerated the use of this technology due to hygiene requirements. Zero UI can be defined as a controlled interface that enables user interaction with technology through voice, gestures, hand interaction, eye tracking, and biometrics such as facial recognition and contactless fingerprints. Smart devices, IoT sensors, smart appliances, smart TVs, smart assistants and consumer robotics are predominant examples of devices in which Zero UI is becoming increasingly adopted. These control interfaces include natural interaction modes such as voice or gestures. Touchscreens and shared devices such as kiosks, self-service counters and interactive displays are present in our everyday lives. Each of these interactions however is a concern for consumers in a post-COVID-19 world where hygiene is of utmost importance. The one-stop solution to hygienic interactions includes touchless technology such as voice control, remote mobile screen take over, biometric, and gesture control as Zero User interfaces. With the breakthroughs in image recognition and natural language processing, powered by advanced computer vision and machine learning, "Zero UI" is becoming a new normal. This paper is focusing on the progress of the touchless interaction technology during the COVID-19 pandemic, which actually accelerated development in this concept and moved it from being a luxury to a life necessity.

Iqbal Muhammad Zahid, Campbell Abraham G

2021-Nov

Contactless, Gestures, Human computer interaction, Touchless interaction, Touchless technology, Zero UI, Zero touch

General General

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19.

In Expert systems with applications

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Zhao Songwei, Wang Pengjun, Heidari Ali Asghar, Zhao Xuehua, Chen Huiling

2023-Mar-01

2D histogram, COVID-19, Crow search algorithm, Multi-threshold image segmentation, Optimization, Renyi’s entropy

Radiology Radiology

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.

In International journal of medical sciences

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

Kufel Jakub, Bargieł Katarzyna, Koźlik Maciej, Czogalik Łukasz, Dudek Piotr, Jaworski Aleksander, Cebula Maciej, Gruszczyńska Katarzyna

2022

COVID-19, artificial intelligence, chest X-rays, convolutional neural network, diagnostic imaging

General General

Towards Smart Diagnostic Methods for COVID-19: Review of Deep Learning for Medical Imaging.

In IPEM-translation

The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.

Moghaddam Marjan Jalali, Ghavipour Mina

2022-Oct-26

Artificial Intelligence, CT-Scan, Classification, Segmentation, X-ray

General General

A deep transfer learning-based convolution neural network model for COVID-19 detection using Computed tomography scan images for medical applications.

In Advances in engineering software (Barking, London, England : 1992)

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Kathamuthu Nirmala Devi, Subramaniam Shanthi, Le Q H, Muthusamy Suresh, Panchal Hitesh, Sundararajan Suma Christal Mary, Alruabie Ali Jawad, Zahra Musaddak Maher Abdul

2022-Oct-24

CNN, Covid-19, Deep Learning, DenseNet121, InceptionV3, ResNet-50, Transfer learning, VGG16, VGG19

Radiology Radiology

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

In EBioMedicine

BACKGROUND : Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death.

METHODS : DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N = 80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N = 805; D2, N = 1917; D3, N = 169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity.

FINDINGS : The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93-0.96] on the independent validation cohort (N = 49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR = 1.50, 95% CI [1.20-1.88], P < .001).

INTERPRETATION : The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N = 2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment.

FUNDING : For a full list of funding bodies, please see the Acknowledgements.

Modanwal Gourav, Al-Kindi Sadeer, Walker Jonathan, Dhamdhere Rohan, Yuan Lei, Ji Mengyao, Lu Cheng, Fu Pingfu, Rajagopalan Sanjay, Madabhushi Anant

2022-Oct-26

COVID-19, Hepatic steatosis, NAFLD

General General

Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift.

MATERIALS AND METHODS : We obtained four datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-COVID (Jan 1, 2019-Feb 1, 2020) and COVID-era (May 15, 2020-Feb 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than four hours was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for two experiments: 1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, 2) we evaluated the impact of spatial drift by testing models trained at Location 1 on data from Location 2, and vice versa.

RESULTS : The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at Location 2) to 0.81 (COVID-era at Location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs. 0.78 at Location 1). Models that were transferred from Location 2 to Location 1 performed worse than models trained at Location 1 (0.51 vs. 0.78).

DISCUSSION AND CONCLUSION : Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift.

Smith Ari J, Patterson Brian W, Pulia Michael S, Mayer John, Schwei Rebecca J, Nagarajan Radha, Liao Frank, Shah Manish N, Boutilier Justin J

2022-Oct-29

COVID-19, Emergency medicine, data drift, emergency department boarding, machine learning

Public Health Public Health

An exploration of challenges associated with machine learning for time series forecasting of COVID-19 community spread using wastewater-based epidemiological data.

In The Science of the total environment

Wastewater-based epidemiology (WBE) has gained increasing attention as a complementary tool to conventional surveillance methods with potential for significant resource and labour savings when used for public health monitoring. Using WBE datasets to train machine learning algorithms and develop predictive models may also facilitate early warnings for the spread of outbreaks. The challenges associated with implementing Random Forest (RF) for timeseries forecasting of COVID-19 was evaluated by running RF on WBE datasets across 108 sites in five regions: Scotland, Catalonia, Ohio, the Netherlands, and Switzerland. This method uses measurements of SARS-CoV-2 RNA fragment concentration in samples taken at the inlets of wastewater treatment plants, providing insight into the prevalence of infection in upstream wastewater catchment populations. RF's forecasting performance at each site was quantitatively evaluated by determining mean absolute percentage error (MAPE) values, which was used to highlight challenges affecting future implementations of RF for WBE forecasting efforts. Performance was generally poor using WBE datasets from Catalonia, Scotland, and Ohio with 'reasonable' or better forecasts constituting 0 %, 5 %, and 0 % of these regions' forecasts, respectively. RF's performance was much stronger with WBE data from the Netherlands and Switzerland, which provided 55 % and 45 % 'reasonable' or better forecasts respectively. Sampling frequency and training set size were identified as key factors contributing to accuracy, while inclusion of too many unnecessary variables (or e.g., flow data) was identified as a contributing factor to poor performance. The contribution of catchment population on forecast accuracy was more ambiguous. This study determined that the factors governing RF's forecast performance are complicated and interrelated, which presents challenges for further work in this space. A sufficiently accurate further iteration of the tool discussed within this study would provide significant but varying value for public health departments for monitoring future, or ongoing outbreaks, assisting the implementation of on-time health response measures.

Vaughan Liam, Zhang Muyang, Gu Haoran, Rose Joan, Naughton Colleen, Medema Gertjan, Allan Vajra, Roiko Anne, Blackall Linda, Zamyadi Arash

2022-Oct-25

COVID-19, Machine learning, Time series forecasting, Wastewater-based epidemiology

General General

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews.

OBJECTIVES : The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers.

METHODS : We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms.

RESULTS : The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73).

CONCLUSION : The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.

Ben-Shabat Niv, Sharvit Gal, Meimis Ben, Ben Joya Daniel, Sloma Ariel, Kiderman David, Shabat Aviv, Tsur Avishai M, Watad Abdulla, Amital Howard

2022-Oct-22

Artificial intelligence, Chatbots, Computer-assisted diagnosis, Data-gathering, Diagnosis, Medical interview, Symptom checker, Telemedicine, Triage

General General

Genome-wide detection of human variants that disrupt intronic branchpoints.

In Proceedings of the National Academy of Sciences of the United States of America

Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.

Zhang Peng, Philippot Quentin, Ren Weicheng, Lei Wei-Te, Li Juan, Stenson Peter D, Palacín Pere Soler, Colobran Roger, Boisson Bertrand, Zhang Shen-Ying, Puel Anne, Pan-Hammarström Qiang, Zhang Qian, Cooper David N, Abel Laurent, Casanova Jean-Laurent

2022-Nov

branchpoint, disease genetics, intronic variant, software, splicing

General General

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.

In Contrast media & molecular imaging

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

Sinwar Deepak, Dhaka Vijaypal Singh, Tesfaye Biniyam Alemu, Raghuwanshi Ghanshyam, Kumar Ashish, Maakar Sunil Kr, Agrawal Sanjay

2022

Radiology Radiology

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.

In Frontiers in physiology

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.

Li Frank, Zhang Xuan, Comellas Alejandro P, Hoffman Eric A, Yang Tianbao, Lin Ching-Long

2022

PASC, cluster analysis, computed tomography, contrastive learning, long Covid, post-COVID-19, small airways disease

General General

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors.

In Frontiers in pharmacology

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

Ai Daiqiao, Wu Jingxing, Cai Hanxuan, Zhao Duancheng, Chen Yihao, Wei Jiajia, Xu Jianrong, Zhang Jiquan, Wang Ling

2022

PARP, deep learning, interpretability, multi-task FP-GNN, online webserver

General General

We are not ready yet: limitations of state-of-the-art disease named entity recognizers.

In Journal of biomedical semantics ; h5-index 23.0

BACKGROUND : Intense research has been done in the area of biomedical natural language processing. Since the breakthrough of transfer learning-based methods, BERT models are used in a variety of biomedical and clinical applications. For the available data sets, these models show excellent results - partly exceeding the inter-annotator agreements. However, biomedical named entity recognition applied on COVID-19 preprints shows a performance drop compared to the results on test data. The question arises how well trained models are able to predict on completely new data, i.e. to generalize.

RESULTS : Based on the example of disease named entity recognition, we investigate the robustness of different machine learning-based methods - thereof transfer learning - and show that current state-of-the-art methods work well for a given training and the corresponding test set but experience a significant lack of generalization when applying to new data.

CONCLUSIONS : We argue that there is a need for larger annotated data sets for training and testing. Therefore, we foresee the curation of further data sets and, moreover, the investigation of continual learning processes for machine learning-based models.

Kühnel Lisa, Fluck Juliane

2022-Oct-27

BERT, Manual Curation, Text mining, bioNLP

General General

Vitamin D deficiency and SARS-CoV-2 infection: Big-data analysis from March 2020 to March 2021. D-COVID study

bioRxiv Preprint

Methods: Using big-data analytics and artificial intelligence through the SAVANA Manager clinical platform, we analysed clinical data from patients with COVID-19 atended in a terciary university hospital from March 2020 to March 2021. Results: Of the 143.157 analysed patients, 36.261 subjects had COVID-19 infection (25.33%); during this period; of these 2588 had vitamin D deficiency (7.14%). Among subjects with COVID-19 and vitamin D deficiency, there was a higher proportion of women OR 1.45 [95% CI 1.33-1.57], adults older than 80 years OR 2.63 [95%CI 2.38-2.91], people living in nursing homes OR 2.88 [95%CI 2.95-3.45] and walking dependence OR 3.45 [95%CI 2.85-4.26]. Regarding clinical course, a higher number of subjects with COVID-19 and vitamin D deficiency required hospitalitation OR 2.41 [95%CI 2.22-2-61], intensive unit care (ICU) OR 2.22 [95% CI 1.64-3.02], had a longer mean hospital stay 3.94 (2.29) p=0.02 and higher mortality OR 1.82 [95%CI 1.66-2.01].) Conclusion: Low serum 25 (OH) Vitamin-D level was significantly associated with a worse clinical evolution and prognosis of COVID-19 infection. We found a higher proportion of institutionalised and dependent people over 80 years of age among patients with COVID-19 and vitamin D deficiency.

Anguita, N.

2022-10-28

Public Health Public Health

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients.

In PloS one ; h5-index 176.0

OBJECTIVE(S) : To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs).

DESIGN : A machine learning study within a national ICU COVID-19 registry in Australia.

PARTICIPANTS : Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded.

MAIN OUTCOME MEASURES : Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models.

RESULTS : 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50-69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively.

CONCLUSION : Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.

Karri Roshan, Chen Yi-Ping Phoebe, Burrell Aidan J C, Penny-Dimri Jahan C, Broadley Tessa, Trapani Tony, Deane Adam M, Udy Andrew A, Plummer Mark P

2022

General General

Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation.

In IEEE transactions on medical imaging ; h5-index 74.0

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.

Lyu Fei, Ye Mang, Carlsen Jonathan Frederik, Erleben Kenny, Darkner Sune, Yuen Pong C

2022-Oct-26

Public Health Public Health

Valuation of Costs in Health Economics During Financial and Economic Crises: A Case Study from Lebanon.

In Applied health economics and health policy

In 2019, we embarked on a study on the economic burden of multiple sclerosis (MS) in Lebanon, in collaboration with a premier Lebanese MS center. This coincided with a triple disaster in Lebanon, comprising the drastic economic and financial crisis, the COVID-19 pandemic, and the consequences of the explosion of Beirut's port. Specifically, the economic and financial turmoil made the valuation of costs challenging. Researchers could face similar challenges, particularly in low- and middle-income countries (LMICs) where economic crises and recessions are recurrent phenomena. This paper aims to discuss steps taken to overcome the fluctuation of the prices of resources to get a valid valuation of societal costs during times of a financial and economic crisis. In the absence of local costing data and guidelines for conducting cost-of-illness (COI) studies, this paper provides empirical recommendations on the valuation of costs that are particularly relevant in LMICs. We recommend (1) clear reporting and justification of the country-specific context, year of costing, assumptions, data sources, and valuation methods, as well as the indicators used to adjust cost for inflation during different periods of fluctuation of prices; (2) collecting prices of each resource from multiple and various sources; (3) conducting a sensitivity analysis; and (4) reporting costs in local currency and Purchasing Power Parity dollars (PPP$). Precision and transparency in reporting prices of resources and their sources are markers of the reliability of the COI studies.

Dahham Jalal, Kremer Ingrid, Hiligsmann Mickaël, Hamdan Kamal, Nassereddine Abdallah, Evers Silvia M A A, Rizk Rana

2022-Oct-26

General General

[The role of artificial intelligence in assessing the progression of fibrosing lung diseases].

In Terapevticheskii arkhiv

INTRODUCTION : The widespread use of artificial intelligence (AI) programs during the COVID-19 pandemic to assess the exact volume of lung tissue damage has allowed them to train a large number of radiologists. The simplicity of the program for determining the volume of the affected lung tissue in acute interstitial pneumonia, which has density indicators in the range from -200 HU to -730 HU, which includes the density indicators of "ground glass" and reticulation (the main radiation patterns in COVID-19) allows you to accurately determine the degree of prevalence process. The characteristics of chronic interstitial pneumonia, which are progressive in nature, fit into the same density framework. Аim. To аssess AI's ability to assess the progression of fibrosing lung disease using lung volume counting programs used for COVID-19 and chronic obstructive pulmonary disease.

RESULTS : Retrospective analysis of computed tomography data during follow-up of 75 patients with progressive fibrosing lung disease made it possible to assess the prevalence and growth of interstitial lesions.

CONCLUSION : Using the experience of using AI programs to assess acute interstitial pneumonia in COVID-19 can be applied to chronic interstitial pneumonia.

Speranskaia A A

2022-Mar-15

artificial intelligence, computer tomography, progressive fibrosing interstitial lung diseases

General General

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.

In Journal of imaging

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

Alsaaidah Bayan, Al-Hadidi Moh’d Rasoul, Al-Nsour Heba, Masadeh Raja, AlZubi Nael

2022-Sep-30

COVID-19, CT images, augmentation, deep learning, diagnosis, machine learning, pneumonia

General General

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

In Computational and mathematical methods in medicine

Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.

Zhang Sai, Yuan Guo-Chang

2022

Public Health Public Health

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

In ERJ open research

Background : The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a "silent pneumonia", i.e. pulmonary auscultation that sounds "normal" using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilisation as a screening tool for COVID-19 pneumonia. In this study we hypothesised that COVID-19 pneumonia, "silent" to the human ear using a standard stethoscope, is detectable using a full-spectrum auscultation device that contains a machine-learning analysis.

Methods : Lung sound signals were acquired, using a novel full-spectrum (3-2000 Hz) stethoscope, from 164 COVID-19 pneumonia patients, 61 non-COVID-19 pneumonia patients and 141 healthy subjects. A machine-learning classifier was constructed and the data were classified into three groups: 1) normal lung sounds, 2) COVID-19 pneumonia and 3) non-COVID-19 pneumonia.

Results : Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds compared with only 25% of the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analysing the sound and infrasound data, and they were reduced to 93% and 80%, respectively, without the infrasound data (p<0.01 difference in receiver operating characteristic curves with and without infrasound).

Conclusions : This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19-related pneumonia and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.

Dori Guy, Bachner-Hinenzon Noa, Kasim Nour, Zaidani Haitem, Perl Sivan Haia, Maayan Shlomo, Shneifi Amin, Kian Yousef, Tiosano Tuvia, Adler Doron, Adir Yochai

2022-Oct

General General

BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images.

In Knowledge-based systems

During the past two years, a highly infectious virus known as COVID-19 has been damaging and harming the health of people all over the world. Simultaneously, the number of patients is rising in various countries, with many new cases appearing daily, posing a significant challenge to hospital medical staff. It is necessary to improve the efficiency of virus detection. To this end, we combine modern technology and visual assistance to detect COVID-19. Based on the above facts, for accurate and rapid identification of infected persons, the BND-VGG-19 method was proposed. This method is based on VGG-19 and further incorporates batch normalization and dropout layers between the layers to improve network accuracy. Then, the COVID-19 dataset including viral pneumonia, COVID-19, and normal X-ray images, are used to diagnose lung abnormalities and test the performance of the proposed algorithm. The experimental results show the superiority of BND-VGG-19 with a 95.48% accuracy rate compared with existing COVID-19 diagnostic methods.

Cao Zili, Huang Junjian, He Xing, Zong Zhaowen

2022-Oct-21

COVID-19, Classification, Diagnosis, VGG-19, X-ray image

Public Health Public Health

COVID-19 Vaccination Rates of People Who Use Drugs - Chengdu City, Sichuan Province, China, November 2021 - February 2022.

In China CDC weekly

What is already known about this topic? : Few studies have reported that people who use drugs (PWUDs) have much lower coronavirus disease 2019 (COVID-19) vaccination rates than the general population, especially with no relative information reported in China specifically.

What is added by this report? : This study seminally uncovers that the vaccination rate among PWUDs was about 79.34% in one district of Chengdu City, Sichuan Province, China. Assuming that unvaccinated PWUDs with disease records were really not eligible for vaccination, the vaccination rate goes up to 87.25% among the studied PWUDs. The study implies that PWUDs were not left behind in the vaccination drive against COVID-19 in China.

What are the implications for public health practice? : In pandemics like COVID-19, government leadership and the overall planning and distribution of public health products are critical in achieving national health equity. However, in order to do this as well as avoid discrimination or exclusion among specific portions of the general population, it's necessary to understand the vaccination rates and behaviors of at-risk groups such as PWUD's.

Du Erri, Jiang Pengyu, Zhang Chaowei, Zhang Shan, Yan Xiangyu, Li Yongjie, Jia Zhongwei

2022-Sep-16

COVID-19, people who use drugs, vaccination

General General

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

bioRxiv Preprint

An outbreak of Monkeypox has been reported in 75 countries so far, and it is spreading at a fast pace around the world. The clinical attributes of Monkeypox resemble those of Smallpox, while skin lesions and rashes of Monkeypox often resemble those of other poxes, for example, Chickenpox and Cowpox. These similarities make Monkeypox detection challenging for healthcare professionals by examining the visual appearance of lesions and rashes. Additionally, there is a knowledge gap among healthcare professionals due to the rarity of Monkeypox before the current outbreak. Motivated by the success of artificial intelligence (AI) in COVID-19 detection, the scientific community has shown an increasing interest in using AI in Monkeypox detection from digital skin images. However, the lack of Monkeypox skin image data has been the bottleneck of using AI in Monkeypox detection. Therefore, in this paper, we used a web-scrapping-based Monkeypox, Chickenpox, Smallpox, Cowpox, Measles, and healthy skin image dataset to study the feasibility of using state-of-the-art AI deep models on skin images for Monkeypox detection. Our study found that deep AI models have great potential in the detection of Monkeypox from digital skin images (precision of 85%). However, achieving a more robust detection power requires larger training samples to train those deep models.

Islam, T.; Hussain, M.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-10-27

Internal Medicine Internal Medicine

Natural Language Processing CAM Algorithm Improves Delirium Detection Compared With Conventional Methods.

In American journal of medical quality : the official journal of the American College of Medical Quality

Delirium is known to be underdiagnosed and underdocumented. Delirium detection in retrospective studies occurs mostly by clinician diagnosis or nursing documentation. This study aims to assess the effectiveness of natural language processing-confusion assessment method (NLP-CAM) algorithm when compared to conventional modalities of delirium detection. A multicenter retrospective study analyzed 4351 COVID-19 hospitalized patient records to identify delirium occurrence utilizing three different delirium detection modalities namely clinician diagnosis, nursing documentation, and the NLP-CAM algorithm. Delirium detection by any of the 3 methods is considered positive for delirium occurrence as a comparison. NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%. Increase in age, Charlson comorbidity score, and length of hospitalization had increased delirium detection odds regardless of the detection method. Artificial intelligence-based NLP-CAM algorithm, compared to conventional methods, improved delirium detection from electronic health records and holds promise in delirium diagnostics.

Pagali Sandeep R, Kumar Rakesh, Fu Sunyang, Sohn Sunghwan, Yousufuddin Mohammed

2022-Oct-26

Public Health Public Health

Deep learning modelling of public's sentiments towards temporal evolution of COVID-19 transmission.

In Applied soft computing

Public sentiments towards global pandemics are important for public health assessment and disease control. This study develops a modularized deep learning framework to quantify public sentiments towards COVID-19, followed by leveraging the predicted sentiments to model and forecast the daily growth rate of confirmed COVID-19 cases globally, via a proposed G parameter. In the proposed framework, public sentiments are first modeled via a valence dimensional indicator, instead of discrete schemas, and are classified into 4 primary emotional categories: (a) neutral; (b) negative; (c) positive; (d) ambivalent, by using multiple word embedding models and classifiers for text sentiments analyses and classification. The trained model is subsequently applied to analyze large volumes (millions in quantity) of daily Tweets pertaining to COVID-19, ranging from 22 Jan 2020 to 10 May 2020. The results demonstrate that the global community gradually evokes both positive and negative sentiments towards COVID-19 over time compared to the dominant neural emotion at its inception. The predicted time-series sentiments are then leveraged to train a deep neural network (DNN) to model and forecast the G parameter by achieving the lowest possible mean absolute percentage error (MAPE) score of around 17.0% during the model's testing step with the optimal model configuration.

Wang Ying, Chew Alvin Wei Ze, Zhang Limao

2022-Oct-20

COVID-19 transmission, Deep learning, Global sentiment evolution, Natural language processing, Text sentiment classification, Twitter data

Public Health Public Health

COVICT: an IoT based architecture for COVID-19 detection and contact tracing.

In Journal of ambient intelligence and humanized computing

The world we live in has been taken quite surprisingly by the outbreak of a novel virus namely SARS-CoV-2. COVID-19 i.e. the disease associated with the virus, has not only shaken the world economy due to enforced lockdown but has also saturated the public health care systems of even most advanced countries due to its exponential spread. The fight against COVID-19 pandemic will continue until majority of world's population get vaccinated or herd immunity is achieved. Many researchers have exploited the Artificial intelligence (AI) knacks based IoT architecture for early detection and monitoring of potential COVID-19 cases to control the transmission of the virus. However, the main cause of the spread is that people infected with COVID-19 do not show any symptoms and are asymptomatic but can still transmit virus to the masses. Researcher have introduced contact tracing applications to automatically detect contacts that can be infected by the index case. However, these fully automated contact tracing apps have not been accepted due to issues like privacy and cross-app compatibility. In the current study, an IoT based COVID-19 detection and monitoring system with semi-automated and improved contact tracing capability namely COVICT has been presented with application of real-time data of symptoms collected from individuals and contact tracing. The deployment of COVICT, the prediction of infected persons can be made more effective and contaminated areas can be identified to mitigate the further propagation of the virus by imposing Smart Lockdown. The proposed IoT based architecture can be quite helpful for regulatory authorities for policy making to fight COVID-19.

Wahid Mirza Anas, Bukhari Syed Hashim Raza, Daud Ahmad, Awan Saeed Ehsan, Raja Muhammad Asif Zahoor

2022-Oct-20

COVID-19, Contact tracing, D2D communication, Early identification, Internet of things, Pandemic, Smart lockdown

General General

A machine learning study of COVID-19 serology and molecular tests and predictions.

In Smart health (Amsterdam, Netherlands)

Serology and molecular tests are the two most commonly used methods for rapid COVID-19 infection testing. The two types of tests have different mechanisms to detect infection, by measuring the presence of viral SARS-CoV-2 RNA (molecular test) or detecting the presence of antibodies triggered by the SARS-CoV-2 virus (serology test). A handful of studies have shown that symptoms, combined with demographic and/or diagnosis features, can be helpful for the prediction of COVID-19 test outcomes. However, due to nature of the test, serology and molecular tests vary significantly. There is no existing study on the correlation between serology and molecular tests, and what type of symptoms are the key factors indicating the COVID-19 positive tests. In this study, we propose a machine learning based approach to study serology and molecular tests, and use features to predict test outcomes. A total of 2,467 donors, each tested using one or multiple types of COVID-19 tests, are collected as our testbed. By cross checking test types and results, we study correlation between serology and molecular tests. For test outcome prediction, we label 2,467 donors as positive or negative, by using their serology or molecular test results, and create symptom features to represent each donor for learning. Because COVID-19 produces a wide range of symptoms and the data collection process is essentially error prone, we group similar symptoms into bins. This decreases the feature space and sparsity. Using binned symptoms, combined with demographic features, we train five classification algorithms to predict COVID-19 test results. Experiments show that XGBoost achieves the best performance with 76.85% accuracy and 81.4% AUC scores, demonstrating that symptoms are indeed helpful for predicting COVID-19 test outcomes. Our study investigates the relationship between serology and molecular tests, identifies meaningful symptom features associated with COVID-19 infection, and also provides a way for rapid screening and cost effective detection of COVID-19 infection.

Elkin Magdalyn E, Zhu Xingquan

2022-Oct-20

68T05, 68T50, 92C50, 92C55, 92C60, COVID-19, Classification, Machine Learning, Molecular test, Serology test, Symptoms

Public Health Public Health

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis.

In Medicine

BACKGROUND : To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspected patients with corresponding symptoms.

METHODS : PubMed, Web of Science, Wiley, China National Knowledge Infrastructure, WAN FANG DATA, and Cochrane Library were searched for articles. Three researchers independently screened the literature, extracted the data. Any differences will be resolved by consulting the third author to ensure that a highly reliable and useful research paper is produced. Data were extracted from the final articles, including: authors, country of study, study type, sample size, participant demographics, type and name of AI software, results (accuracy, sensitivity, specificity, ROC, and predictive values), other outcome(s) if applicable.

RESULTS : Among the 3891 searched results, 32 articles describing 51,392 confirmed patients and 7686 non-infected individuals met the inclusion criteria. The pooled sensitivity, the pooled specificity, positive likelihood ratio, negative likelihood ratio and the pooled diagnostic odds ratio (OR) is 0.87(95%CI [confidence interval]: 0.85, 0.89), 0.85(95%CI: 0.82, 0.87), 6.7(95%CI: 5.7, 7.8), 0.14(95%CI: 0.12, 0.16), and 49(95%CI: 38, 65). Further, the AUROC (area under the receiver operating characteristic curve) is 0.94(95%CI: 0.91, 0.96). Secondary outcomes are specific sensitivity and specificity within subgroups defined by different models. Resnet has the best diagnostic performance, which has the highest sensitivity (0.91[95%CI: 0.87, 0.94]), specificity (0.90[95%CI: 0.86, 0.93]) and AUROC (0.96[95%CI: 0.94, 0.97]), according to the AUROC, we can get the rank Resnet > Densenet > VGG > Mobilenet > Inception > Effficient > Alexnet.

CONCLUSIONS : Our study findings show that deep learning models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Wang Qiaolan, Ma Jingxuan, Zhang Luoning, Xie Linshen

2022-Oct-21

General General

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.

In Biomedical signal processing and control

Background and Objective : : The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods : : The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results : : Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion : : Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Bhattacharjya Upasana, Sarma Kandarpa Kumar, Medhi Jyoti Prakash, Choudhury Binoy Kumar, Barman Geetanjali

2022-Oct-18

COVID-19, Consolidation, Crazy –paving, Deep learning, Ground glass opacities, Halo Sign, Machine Learning

Surgery Surgery

Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients.

In Frontiers in medicine

Background : Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.

Methods : We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.

Results : Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.

Conclusion : We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology.

Legouis David, Criton Gilles, Assouline Benjamin, Le Terrier Christophe, Sgardello Sebastian, Pugin Jérôme, Marchi Elisa, Sangla Frédéric

2022

AKI, COVID-19, clustering, critical care, machine learning

General General

Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool.

In International journal of public health

Objectives: Real-time data analysis during a pandemic is crucial. This paper aims to introduce a novel interactive tool called Covid-Predictor-Tracker using several sources of COVID-19 data, which allows examining developments over time and across countries. Exemplified here by investigating relative effects of vaccination to non-pharmaceutical interventions on COVID-19 spread. Methods: We combine >100 indicators from the Global COVID-19 Trends and Impact Survey, Johns Hopkins University, Our World in Data, European Centre for Disease Prevention and Control, National Centers for Environmental Information, and Eurostat using random forests, hierarchical clustering, and rank correlation to predict COVID-19 cases. Results: Between 2/2020 and 1/2022, we found among the non-pharmaceutical interventions "mask usage" to have strong effects after the percentage of people vaccinated at least once, followed by country-specific measures such as lock-downs. Countries with similar characteristics share ranks of infection predictors. Gender and age distribution, healthcare expenditures and cultural participation interact with restriction measures. Conclusion: Including time-aware machine learning models in COVID-19 infection dashboards allows to disentangle and rank predictors of COVID-19 cases per country to support policy evaluation. Our open-source tool can be updated daily with continuous data streams, and expanded as the pandemic evolves.

Balogh Aniko, Harman Anna, Kreuter Frauke

2022

COVID-19 non-pharmaceutical interventions, COVID-19 prediction, COVID-19 virus variants, comparative analyses, interactive visualization, machine learning, social epidemiology, time series cross-validation

General General

Search queries related to COVID-19 based on keyword extraction.

In Procedia computer science

Background : : Pandemic COVID-19 caused an infodemic - massive spread of true and fake information about novel coronavirus. This study aims to present the possibility of using Keyword Extraction as a tool to obtain the most trending search queries related to COVID-19 and analyze the possibility of including their search volume in models for the prediction of fake news.

Methods : : The study used Python implementation of the machine learning-based technique KeyBERT to extract keywords from true and fake news. These keywords were used in the next step to obtain related search queries with Google Trends API.

Results : : Non-parametric Spearman Rank Order Correlation was identified as a statistically positive correlation (p < 0.001) between the occurrence of false news and top query / rising query metrics provided by Google Trends of queries related to extracted keywords pandemic, HIV, lockdown, plague, Michigan, and protest, which proves that search volume can identify fake news.

Conclusions : : Experiments done in this research proved that Keyword Extraction from false news is useful for obtaining related search queries and the top query and rising query metrics can be used to increase the accuracy of fake news prediction models.

Kelebercová Lívia, Munk Michal

2022

Fake News Detection, Google Trends, Keyword Extraction, Natural Language Processing

General General

The Deep Learning-Based Framework for Automated Predicting COVID-19 Severity Score.

In Procedia computer science

With the COVID-19 pandemic sweeping the globe, an increasing number of people are working on pandemic research, but there is less effort on predicting its severity. Diagnostic chest imaging is thought to be a quick and reliable way to identify the severity of COVID-19. We describe a deep learning method to automatically predict the severity score of patients by analyzing chest X-rays, with the goal of collaborating with doctors to create corresponding treatment measures for patients and can also be used to track disease change. Our model consists of a feature extraction phase and an outcome prediction phase. The feature extraction phase uses a DenseNet backbone network to extract 18 features related to lung diseases from CXRs; the outcome prediction phase, which employs the MLP regression model, selects several important features for prediction from the features extracted in the previous phase and demonstrates the effectiveness of our model by comparing it with several commonly used regression models. On a dataset of 2373 CXRs, our model predicts the geographic extent score with 1.02 MAE and the lung opacity score with 0.85 MAE.

Zheng Yongchang, Dong Hongwei

2022

COVID-19, CXRs, deep learning method, estimate severity, feature extraction

General General

Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients.

In Procedia computer science

During 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay.

Olivato Matteo, Rossetti Nicholas, Gerevini Alfonso E, Chiari Mattia, Putelli Luca, Serina Ivan

2022

General General

The Influence of Environmental Factors on the Spread of COVID-19 in Italy.

In Procedia computer science

The aim of this work is to investigate possible relationships between air quality and the spread of the pandemic. We evaluate the performance of machine learning techniques in predicting new cases. Specifically, we describe a cross-correlation analysis on daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained using environmental parameters might provide accurate predictions about the number of infected cases. Our empirical evaluation shows that temperature and ozone are negatively correlated with confirmed cases (therefore, the higher the values of these parameters, the lower the number of infected cases), whereas atmospheric particulate matter and nitrogen dioxide are positively correlated. We developed and compared three different predictive models to test whether these technologies can be useful to estimate the evolution of the pandemic.

Loreggia Andrea, Passarelli Anna, Pini Maria Silvia

2022

Air Quality Effects, COVID-19 Pandemic, Correlation Analysis, Machine Learning

General General

An explainable COVID-19 detection system based on human sounds.

In Smart health (Amsterdam, Netherlands)

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.

Li Huining, Chen Xingyu, Qian Xiaoye, Chen Huan, Li Zhengxiong, Bhattacharjee Soumyadeep, Zhang Hanbin, Huang Ming-Chun, Xu Wenyao

2022-Oct-19

Accurate, Acoustic, COVID-19, Explainable

Public Health Public Health

CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images.

In International journal of machine learning and cybernetics

Since the emergence of the novel coronavirus in December 2019, it has rapidly swept across the globe, with a huge impact on daily life, public health and the economy around the world. There is an urgent necessary for a rapid and economical detection method for the Covid-19. In this study, we used the transformers-based deep learning method to analyze the chest X-rays of normal, Covid-19 and viral pneumonia patients. Covid-Vision-Transformers (CovidViT) is proposed to detect Covid-19 cases through X-ray images. CovidViT is based on transformers block with the self-attention mechanism. In order to demonstrate its superiority, this research is also compared with other popular deep learning models, and the experimental result shows CovidViT outperforms other deep learning models and achieves 98.0% accuracy on test set, which means that the proposed model is excellent in Covid-19 detection. Besides, an online system for quick Covid-19 diagnosis is built on http://yanghang.site/covid19.

Yang Hang, Wang Liyang, Xu Yitian, Liu Xuhua

2022-Oct-19

Covid-19, Deep learning, Self-attention, Transformers

General General

A Hybrid Deep Transfer Learning Model With Kernel Metric for COVID-19 Pneumonia Classification Using Chest CT Images.

In IEEE/ACM transactions on computational biology and bioinformatics

Coronavirus disease-2019 (COVID-19) as a new pneumonia which is extremely infectious, the classification of this coronavirus is essential to effectively control the development of the epidemic. Pathological changes in the chest computed tomography (CT) scans are often used as one of the diagnostic criteria of COVID-19. Meanwhile, deep learning-based transfer learning is currently an effective strategy for computer-aided diagnosis (CAD). To further improve the performance of deep transfer learning model used for COVID-19 classification with CT images, in this article, we propose a hybrid model combined with a semi-supervised domain adaption model and extreme learning machine (ELM) classifier, and the application of a novel multikernel correntropy induced loss function in transfer learning is also presented. The proposed model is evaluated on open-source datasets. The experimental results are compared to some baseline models to verify the effectiveness, while adopting accuracy, precision, recall, F1 score and area under curve (AUC) as the evaluation metrics. Experimental results show that the proposed method improves the performance of original model and is more suitable for CT images analysis.

Li Jianyuan, Luo Xiong, Ma Huimin, Zhao Wenbing

2022-Oct-24

General General

Understanding How the Design and Implementation of Online Consultations Affect Primary Care Quality: Systematic Review of Evidence With Recommendations for Designers, Providers, and Researchers.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Online consultations (OCs) allow patients to contact their care providers on the web. Worldwide, OCs have been rolled out in primary care rapidly owing to policy initiatives and COVID-19. There is a lack of evidence regarding how OC design and implementation influence care quality.

OBJECTIVE : We aimed to synthesize research on the impacts of OCs on primary care quality, and how these are influenced by system design and implementation.

METHODS : We searched databases from January 2010 to February 2022. We included quantitative and qualitative studies of real-world OC use in primary care. Quantitative data were transformed into qualitative themes. We used thematic synthesis informed by the Institute of Medicine domains of health care quality, and framework analysis informed by the nonadoption, abandonment, scale-up, spread, and sustainability framework. Strength of evidence was judged using the GRADE-CERQual approach.

RESULTS : We synthesized 63 studies from 9 countries covering 31 OC systems, 14 (22%) of which used artificial intelligence; 41% (26/63) of studies were published from 2020 onward, and 17% (11/63) were published after the COVID-19 pandemic. There was no quantitative evidence for negative impacts of OCs on patient safety, and qualitative studies suggested varied perceptions of their safety. Some participants believed OCs improved safety, particularly when patients could describe their queries using free text. Staff workload decreased when sufficient resources were allocated to implement OCs and patients used them for simple problems or could describe their queries using free text. Staff workload increased when OCs were not integrated with other software or organizational workflows and patients used them for complex queries. OC systems that required patients to describe their queries using multiple-choice questionnaires increased workload for patients and staff. Health costs decreased when patients used OCs for simple queries and increased when patients used them for complex queries. Patients using OCs were more likely to be female, younger, and native speakers, with higher socioeconomic status. OCs increased primary care access for patients with mental health conditions, verbal communication difficulties, and barriers to attending in-person appointments. Access also increased by providing a timely response to patients' queries. Patient satisfaction increased when using OCs owing to better primary care access, although it decreased when using multiple-choice questionnaire formats.

CONCLUSIONS : This is the first theoretically informed synthesis of research on OCs in primary care and includes studies conducted during the COVID-19 pandemic. It contributes new knowledge that, in addition to having positive impacts on care quality such as increased access, OCs also have negative impacts such as increased workload. Negative impacts can be mitigated through appropriate OC system design (eg, free text format), incorporation of advanced technologies (eg, artificial intelligence), and integration into technical infrastructure (eg, software) and organizational workflows (eg, timely responses).

TRIAL REGISTRATION : PROSPERO CRD42020191802; https://tinyurl.com/2p84ezjy.

Darley Sarah, Coulson Tessa, Peek Niels, Moschogianis Susan, van der Veer Sabine N, Wong David C, Brown Benjamin C

2022-Oct-24

COVID-19, OC, care provider, general practice, health care professional, health outcome, pandemic, patient care, primary care, primary health care, remote consultation, systematic review, telemedicine, triage, workforce

Public Health Public Health

Associations between the use of aspirin or other antiplatelet drugs and all-cause mortality among patients with COVID-19: A meta-analysis.

In Frontiers in pharmacology

Introduction: Whether aspirin or other antiplatelet drugs can reduce mortality among patients with coronavirus disease (COVID-19) remains controversial. Methods: We identified randomized controlled trials, prospective cohort studies, and retrospective studies on associations between aspirin or other antiplatelet drug use and all-cause mortality among patients with COVID-19 in the PubMed database between March 2019 and September 2021. Newcastle-Ottawa Scale and Cochrane Risk of Bias Assessment Tool were used to assess the risk of bias. The I2 statistic was used to assess inconsistency among trial results. The summary risk ratio (RR) and odds ratio (OR) were obtained through the meta-analysis. Results: The 34 included studies comprised three randomized controlled trials, 27 retrospective studies, and 4 prospective cohort studies. The retrospective and prospective cohort studies showed low-to-moderate risks of bias per the Newcastle-Ottawa Scale score, while the randomized controlled trials showed low-to-high risks of bias per the Cochrane Risk of Bias Assessment Tool. The randomized controlled trials showed no significant effect of aspirin use on all-cause mortality in patients with COVID-19 {risk ratio (RR), 0.96 [95% confidence interval (CI) 0.90-1.03]}. In retrospective studies, aspirin reduced all-cause mortality in patients with COVID-19 by 20% [odds ratio (OR), 0.80 (95% CI 0.70-0.93)], while other antiplatelet drugs had no significant effects. In prospective cohort studies, aspirin decreased all-cause mortality in patients with COVID-19 by 15% [OR, 0.85 (95% CI 0.80-0.90)]. Conclusion: The administration of aspirin may reduce all-cause mortality in patients with COVID-19.

Su Wanting, Miao He, Guo Zhaotian, Chen Qianhui, Huang Tao, Ding Renyu

2022

COVID-19, antiplatelet drug, aspirin, meta-analysis, mortality

General General

Effect of Disulfide Bridge on the Binding of SARS-CoV-2 Fusion Peptide to Cell Membrane: A Coarse-Grained Study.

In ACS omega

In this paper, we present the parameterization of the CAVS coarse-grained (CG) force field for 20 amino acids, and our CG simulations show that the CAVS force field could accurately predict the amino acid tendency of the secondary structure. Then, we used the CAVS force field to investigate the binding of a severe acute respiratory syndrome-associated coronavirus fusion peptide (SARS-CoV-2 FP) to a phospholipid bilayer: a long FP (FP-L) containing 40 amino acids and a short FP (FP-S) containing 26 amino acids. Our CAVS CG simulations displayed that the binding affinity of the FP-L to the bilayer is higher than that of the FP-S. We found that the FP-L interacted more strongly with membrane cholesterol than the FP-S, which should be attributed to the stable helical structure of the FP-L at the C-terminus. In addition, we discovered that the FP-S had one major and two minor membrane-bound states, in agreement with previous all-atom molecular dynamics (MD) studies. However, we found that both the C-terminal and N-terminal amino acid residues of the FP-L can strongly interact with the bilayer membrane. Furthermore, we found that the disulfide bond formed between Cys840 and Cys851 stabilized the helices of the FP-L at the C-terminus, enhancing the interaction between the FP-L and the bilayer membrane. Our work indicates that the stable helical structure is crucial for binding the SARS-CoV-2 FP to cell membranes. In particular, the helical stability of FP should have a significant influence on the FP-membrane binding.

Shen Hujun, Wu Zhenhua

2022-Oct-18

General General

Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning.

In Knowledge-based systems

Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.

Chen Shuixia, Xu Zeshui, Wang Xinxin, Zhang Chenxi

2022-Oct-17

Ambient air pollutants concentration prediction, COVID-19, Machine learning, Transfer learning

Radiology Radiology

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.

In Applied soft computing

Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The similarity between the images is determined using nearest neighbor classifiers that use the Euclidean distance between the feature vectors in latent space. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections, i.e., low infection, intermediate infection, high infection, and extremely high infection. To prove the robustness of the proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories.

Ahuja Sakshi, Panigrahi Bijaya Ketan, Dey Neelanjan, Taneja Arpit, Gandhi Tapan Kumar

2022-Oct-17

CNN, COVID-19 infection, CT scan, Siamese network

General General

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.

In One health (Amsterdam, Netherlands)

The complex, unpredictable nature of pathogen occurrence has required substantial efforts to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning (ML) and Deep Learning (DL) techniques combined with their unique ability to uncover connections between large amounts of diverse data, we conducted a PRISMA systematic review to investigate advances in ID prediction for human and animal diseases using ML and DL. This review included the type of IDs modeled, ML and DL techniques utilized, geographical distribution, prediction tasks performed, input features utilized, spatial and temporal scales, error metrics used, computational efficiency, uncertainty quantification, and missing data handling methods. Among 237 relevant articles published between January 2001 and May 2021, highly contagious diseases in humans were most often represented, including COVID-19 (37.1%), influenza/influenza-like illnesses (9.3%), dengue (8.9%), and malaria (5.1%). Out of 37 diseases identified, 51.4% were zoonotic, 37.8% were human-only, and 8.1% were animal-only, with only 1.6% economically significant, non-zoonotic livestock diseases. Despite the number of zoonoses, 86.5% of articles modeled humans whereas only a few articles (5.1%) contained more than one host species. Eastern Asia (32.5%), North America (17.7%), and Southern Asia (13.1%) were the most represented locations. Frequent approaches included tree-based ML (38.4%) and feed-forward neural networks (26.6%). Articles predicted temporal incidence (66.7%), disease risk (38.0%), and/or spatial movement (31.2%). Less than 10% of studies addressed uncertainty quantification, computational efficiency, and missing data, which are essential to operational use and deployment. This study highlights trends and gaps in ML and DL for ID prediction, providing guidelines for future works to better support biopreparedness and response. To fully utilize ML and DL for improved ID forecasting, models should include the full disease ecology in a One-Health context, important food and agricultural diseases, underrepresented hotspots, and important metrics required for operational deployment.

Keshavamurthy Ravikiran, Dixon Samuel, Pazdernik Karl T, Charles Lauren E

2022-Dec

Deep learning, Disease forecast, Disease prediction, Infectious diseases, Machine learning, Systematic review

General General

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

bioRxiv Preprint

The COVID-19 pandemic has highlighted how viral variants that escape monoclonal antibodies can limit options to control an outbreak. With the emergence of the SARS-CoV-2 Omicron variant, many clinically used antibody drug products lost in vitro and in vivo potency, including AZD7442 and its constituent, AZD1061. Rapidly modifying such antibodies to restore efficacy to emerging variants is a compelling mitigation strategy. We therefore sought to computationally design an antibody that restores neutralization of BA.1 and BA.1.1 while simultaneously maintaining efficacy against SARS-CoV-2 B.1.617.2 (Delta), beginning from COV2-2130, the progenitor of AZD1061. Here we describe COV2-2130 derivatives that achieve this goal and provide a proof-of-concept for rapid antibody adaptation addressing escape variants. Our best antibody achieves potent and broad neutralization of BA.1, BA.1.1, BA.2, BA.2.12.1, BA.4, BA.5, and BA.5.5 Omicron subvariants, where the parental COV2-2130 suffers significant potency losses. This antibody also maintains potency against Delta and WA1/2020 strains and provides protection in vivo against the strains we tested, WA1/2020, BA.1.1, and BA.5. Because our design approach is computational - driven by high-performance computing-enabled simulation, machine learning, structural bioinformatics and multi-objective optimization algorithms - it can rapidly propose redesigned antibody candidates aiming to broadly target multiple escape variants and virus mutations known or predicted to enable escape.

Desautels, T. A.; Arrildt, K. T.; Zemla, A. T.; Lau, E. Y.; Zhu, F.; Ricci, D.; Cronin, S.; Zost, S.; Binshtein, E.; Scheaffer, S. M.; Engdahl, T. B.; Chen, E.; Goforth, J. W.; Vashchenko, D.; Nguyen, S.; Weilhammer, D. R.; Lo, J. K.-Y.; Rubinfeld, B.; Saada, E. A.; Weisenberger, T.; Lee, T.-H.; Whitener, B.; Case, J. B.; Ladd, A.; Silva, M. S.; Haluska, R. M.; Grzesiak, E. A.; Bates, T. W.; Petersen, B. K.; Thackray, L. B.; Segelke, B. W.; Lillo, A. M.; Sundaram, S.; Diamond, M. S.; Crowe, J. E.; Carnahan, R. H.; Faissol, D. M.

2022-10-24

General General

Effects of long-term COVID-19 confinement and music stimulation on mental state and brain activity of young people.

In Neuroscience letters

The Corona Virus Disease 2019 (COVID-19) pandemic may have had a negative emotional impact on individuals. This study investigated the effect of long-term lockdown and music on young people's mood and neurophysiological responses in the prefrontal cortex (PFC). Fifteen healthy young adults were recruited and PFC activation was acquired using functional near-infrared spectroscopy during the conditions of resting, Stroop and music stimulation. The Depression Anxiety Stress Scales mental scale scores were simultaneously recorded. Mixed effect models, paired t-tests, one-way ANOVAs and Spearman analyses were adopted to analyse the experimental parameters. Stress, anxiety and depression levels increased significantly from Day 30 to Day 40. In terms of reaction time, both Stroop1 and Stroop2 were faster on Day 40 than on Day 30 (P = 0.01, P = 0.003). The relative concentration changes of oxyhemoglobin were significantly higher during premusic conditions than music stimulation and postmusic Stroop. The intensity of functional connectivity shifted from inter- to intracerebral over time. In conclusion, the reduced hemodynamic response of the PFC in healthy young adults is associated with negative emotions, especially anxiety, during lockdown. Immediate music stimulation appears to improve efficiency by altering the pattern of connections in PFC.

Luo Lina, Shan Mianjia, Zu Yangmin, Chen Yufang, Bu Lingguo, Wang Lejun, Ni Ming, Niu Wenxin

2022-Oct-19

Cognitive neuroscience, Functional brain connectivity, Lockdown, Music stimulation, Negative emotions, Prefrontal cortex activation

Public Health Public Health

A storm in a teacup -- A biomimetic lung microphysiological system in conjunction with a deep-learning algorithm to monitor lung pathological and inflammatory reactions.

In Biosensors & bioelectronics

Creating a biomimetic in vitro lung model to recapitulate the infection and inflammatory reactions has been an important but challenging task for biomedical researchers. The 2D based cell culture models - culturing of lung epithelium - have long existed but lack multiple key physiological conditions, such as the involvement of different types of immune cells and the creation of connected lung models to study viral or bacterial infection between different individuals. Pioneers in organ-on-a-chip research have developed lung alveoli-on-a-chip and connected two lung chips with direct tubing and flow. Although this model provides a powerful tool for lung alveolar disease modeling, it still lacks interactions among immune cells, such as macrophages and monocytes, and the mimic of air flow and aerosol transmission between lung-chips is missing. Here, we report the development of an improved human lung physiological system (Lung-MPS) with both alveolar and pulmonary bronchial chambers that permits the integration of multiple immune cells into the system. We observed amplified inflammatory signals through the dynamic interactions among macrophages, epithelium, endothelium, and circulating monocytes. Furthermore, an integrated microdroplet/aerosol transmission system was fabricated and employed to study the propagation of pseudovirus particles containing microdroplets in integrated Lung-MPSs. Finally, a deep-learning algorithm was developed to characterize the activation of cells in this Lung-MPS. This Lung-MPS could provide an improved and more biomimetic sensory system for the study of COVID-19 and other high-risk infectious lung diseases.

Chen Zaozao, Huang Jie, Zhang Jing, Xu Zikang, Li Qiwei, Ouyang Jun, Yan Yuchuan, Sun Shiqi, Ye Huan, Wang Fei, Zhu Jianfeng, Wang Zhangyan, Chao Jie, Pu Yuepu, Gu Zhongze

2022-Oct-01

COVID-19, Cytokine storm, Lung-on-a-chip, Microfluidics

General General

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality In Post COVID-19 Era.

In IEEE reviews in biomedical engineering

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

Isgut Monica, Gloster Logan, Choi Katherine, Venugopalan Janani, Wang May D

2022-Oct-21

Internal Medicine Internal Medicine

Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients.

In Frontiers in immunology ; h5-index 100.0

Repeated vaccination against SARS-CoV-2 increases serological response in kidney transplant recipients (KTR) with high interindividual variability. No decision support tool exists to predict SARS-CoV-2 vaccination response to third or fourth vaccination in KTR. We developed, internally and externally validated five different multivariable prediction models of serological response after the third and fourth vaccine dose against SARS-CoV-2 in previously seronegative, COVID-19-naïve KTR. Using 20 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), least absolute shrinkage and selection operator (LASSO)-regularized LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 590 vaccinations were used. External validation was performed in four independent, international validation cohorts comprising 191, 184, 254, and 323 vaccinations, respectively. LASSO-regularized LR performed on the whole development dataset yielded a 20- and 10-variable model, respectively. External validation showed AUC-ROC of 0.840, 0.741, 0.816, and 0.783 for the sparser 10-variable model, yielding an overall performance 0.812. A 10-variable LASSO-regularized LR model predicts vaccination response in KTR with good overall accuracy. Implemented as an online tool, it can guide decisions whether to modulate immunosuppressive therapy before additional active vaccination, or to perform passive immunization to improve protection against COVID-19 in previously seronegative, COVID-19-naïve KTR.

Osmanodja Bilgin, Stegbauer Johannes, Kantauskaite Marta, Rump Lars Christian, Heinzel Andreas, Reindl-Schwaighofer Roman, Oberbauer Rainer, Benotmane Ilies, Caillard Sophie, Masset Christophe, Kerleau Clarisse, Blancho Gilles, Budde Klemens, Grunow Fritz, Mikhailov Michael, Schrezenmeier Eva, Ronicke Simon

2022

COVID-19, clinical decision support, immunosuppression therapy, kidney transplantation, vaccination

General General

Artificial intelligence assisted acute patient journey.

In Frontiers in artificial intelligence

Artificial intelligence is taking the world by storm and soon will be aiding patients in their journey at the hospital. The trials and tribulations of the healthcare system during the COVID-19 pandemic have set the stage for shifting healthcare from a physical to a cyber-physical space. A physician can now remotely monitor a patient, admitting them only if they meet certain thresholds, thereby reducing the total number of admissions at the hospital. Coordination, communication, and resource management have been core issues for any industry. However, it is most accurate in healthcare. Both systems and providers are exhausted under the burden of increasing data and complexity of care delivery, increasing costs, and financial burden. Simultaneously, there is a digital transformation of healthcare in the making. This transformation provides an opportunity to create systems of care that are artificial intelligence-enabled. Healthcare resources can be utilized more justly. The wastage of financial and intellectual resources in an overcrowded healthcare system can be avoided by implementing IoT, telehealth, and AI/ML-based algorithms. It is imperative to consider the design principles of the patient's journey while simultaneously prioritizing a better user experience to alleviate physician concerns. This paper discusses the entire blueprint of the AI/ML-assisted patient journey and its impact on healthcare provision.

Nazir Talha, Mushhood Ur Rehman Muhammad, Asghar Muhammad Roshan, Kalia Junaid S

2022

AI-based clinical decision support system, Automated EMR summary, acute patient journey, artificial intelligence, electronic-triage, health IoT

General General

Deep learning models-based CT-scan image classification for automated screening of COVID-19.

In Biomedical signal processing and control

COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body. This virus badly affected the lives and wellness of millions of people worldwide and spread widely. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of coronavirus. Computed tomography (CT) scanning has proven useful in diagnosing several respiratory lung problems, including COVID-19 infections. Automated detection of COVID-19 using chest CT-scan images may reduce the clinician's load and save the lives of thousands of people. This study proposes a robust framework for the automated screening of COVID-19 using chest CT-scan images and deep learning-based techniques. In this work, a publically accessible CT-scan image dataset (contains the 1252 COVID-19 and 1230 non-COVID chest CT images), two pre-trained deep learning models (DLMs) namely, MobileNetV2 and DarkNet19, and a newly-designed lightweight DLM, are utilized for the automated screening of COVID-19. A repeated ten-fold holdout validation method is utilized for the training, validation, and testing of DLMs. The highest classification accuracy of 98.91% is achieved using transfer-learned DarkNet19. The proposed framework is ready to be tested with more CT images. The simulation results with the publicly available COVID-19 CT scan image dataset are included to show the effectiveness of the presented study.

Gupta Kapil, Bajaj Varun

2023-Feb

COVID-19, CT-scan images, Deep learning, Transfer learning

Internal Medicine Internal Medicine

Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic.

In Clinical and translational allergy

Background : During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma.

Methods : We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques.

Results : The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%.

Conclusions : ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic.

Gawlewicz-Mroczka Agnieszka, Pytlewski Adam, Celejewska-Wójcik Natalia, Ćmiel Adam, Gielicz Anna, Sanak Marek, Mastalerz Lucyna

2022-Oct

COVID‐19 pandemic, machine learning, nonsteroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (NERD), nonsteroidal anti‐inflammatory drug tolerant asthma (NTA), oral aspirin challenge

General General

Smartphone-based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Application to SARS-CoV-2 Lateral Flow Immunoassays.

In JMIR public health and surveillance

BACKGROUND : Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed altering a correct epidemiological surveillance.

OBJECTIVE : To evaluate an artificial intelligence-based smartphone application, connected to a cloud web platform, to automatically and objectively read rapid diagnostic test (RDT) results and assess its impact on COVID-19 pandemic management.

METHODS : Overall, 252 human sera were used to inoculate a total of 1,165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department.

RESULTS : Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8-96.1%) for reading IgG band of COVID-19 antibodies RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100% and specificity was 95.8%, CI 94.3-97.3%. All COVID-19 antigen RDTs were correctly read by the app.

CONCLUSIONS : The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDTs brands. The web platform serves as a real time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.

Bermejo-Peláez David, Marcos-Mencía Daniel, Álamo Elisa, Pérez-Panizo Nuria, Mousa Adriana, Dacal Elena, Lin Lin, Vladimirov Alexander, Cuadrado Daniel, Mateos-Nozal Jesús, Galán Juan Carlos, Romero-Hernandez Beatriz, Cantón Rafael, Luengo-Oroz Miguel, Rodriguez-Dominguez Mario

2022-Oct-13

General General

Discovery and analytical validation of a vocal biomarker to monitor anosmia and ageusia in patients with Covid-19: Cross-sectional study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The Covid-19 disease has multiple symptoms, being anosmia, varying from 75-95%, and ageusia, varying from 50-80% of infected patients, the most prevalent ones. An automatic assessment tool for these symptoms will help monitor the disease in a fast and non-invasive manner.

OBJECTIVE : We hypothesized that people with Covid-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them.

METHODS : This study is made on population-based data. Participants were assessed daily through an online questionnaire and asked to register two different types of voice recordings, they were adults (older than 18 years old) that were confirmed by a PCR test to be positive for Covid-19 in Luxembourg and that passed through the exclusion criteria. Statistical methods like Recursive Feature Elimination (RFE) for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD Prediction Model Development checklist was used to structure the research.

RESULTS : This study included 259 participants. Young (<35 years old) and females showed a higher rate of ageusia and anosmia. Participants were 41 (SD = 13) years old on average and the dataset was balanced for sex (134 females (52%) and 125 males (48%) out of 259). The analyzed symptom was present in 94 out of 259 (36%) participants of the population and in 450 out of 1636 (28%) audio recordings. Two machine learning models were built, one for Android and one for iOS devices and both had high accuracy, being 88% for Android and 85% for iOS. The final biomarker was then calculated using these models and internally validated.

CONCLUSIONS : This study demonstrates that people with Covid-19 who have anosmia and ageusia have different voice features from those without it. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance telemonitoring of Covid-19-related symptoms.

CLINICALTRIAL : Approved by the National Research Ethics Committee of Luxembourg (study number 202003/07) in April 2020 and is registered Clinicaltrials.gov NCT04380987, https://clinicaltrials.gov/ct2/show/NCT04380987.

Higa Eduardo, Zhang Lu, Elbéji Abir, Fischer Aurélie, Aguayo Gloria A, Nazarov Petr V, Fagherazzi Guy

2022-Sep-07

General General

Evaluation of digital economy development level based on multi-attribute decision theory.

In PloS one ; h5-index 176.0

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China's digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China's provincial digital economy.

Su Jinqi, Su Ke, Wang Shubin

2022

General General

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

In Annals of intensive care ; h5-index 37.0

BACKGROUND : For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources.

METHODS : From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking.

RESULTS : The median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode.

CONCLUSIONS : In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning.

Dam Tariq A, Roggeveen Luca F, van Diggelen Fuda, Fleuren Lucas M, Jagesar Ameet R, Otten Martijn, de Vries Heder J, Gommers Diederik, Cremer Olaf L, Bosman Rob J, Rigter Sander, Wils Evert-Jan, Frenzel Tim, Dongelmans Dave A, de Jong Remko, Peters Marco A A, Kamps Marlijn J A, Ramnarain Dharmanand, Nowitzky Ralph, Nooteboom Fleur G C A, de Ruijter Wouter, Urlings-Strop Louise C, Smit Ellen G M, Mehagnoul-Schipper D Jannet, Dormans Tom, de Jager Cornelis P C, Hendriks Stefaan H A, Achterberg Sefanja, Oostdijk Evelien, Reidinga Auke C, Festen-Spanjer Barbara, Brunnekreef Gert B, Cornet Alexander D, van den Tempel Walter, Boelens Age D, Koetsier Peter, Lens Judith, Faber Harald J, Karakus A, Entjes Robert, de Jong Paul, Rettig Thijs C D, Arbous Sesmu, Vonk Sebastiaan J J, Machado Tomas, Herter Willem E, de Grooth Harm-Jan, Thoral Patrick J, Girbes Armand R J, Hoogendoorn Mark, Elbers Paul W G

2022-Oct-20

Acute respiratory distress syndrome, COVID-19, Mechanical ventilation

General General

Serological responses to human virome define clinical outcomes of Italian patients infected with SARS-CoV-2.

In International journal of biological sciences

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 sequentially enrolled hospitalized patients with COVID-19-associated pneumonia from Brescia, Italy using the VirScan phage-display method to characterize circulating antibodies binding to 96,179 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in immune antibody repertoires against many known pathogenic and non-pathogenic human viruses. This antiviral antibody response was linked to longitudinal trajectories of disease severity and was further confirmed in additional 125 COVID-19 patients from the same geographical region in Northern Italy. By applying a machine-learning-based strategy, a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival was developed and validated. These results provide a basis for understanding the role of memory B-cell repertoire to viral epitopes in COVID-19-related symptoms and suggest that a unique anti-viral antibody repertoire signature may be useful to define COVID-19 clinical severity.

Wang Limin, Candia Julián, Ma Lichun, Zhao Yongmei, Imberti Luisa, Sottini Alessandra, Quiros-Roldan Eugenia, Dobbs Kerry, Burbelo Peter D, Cohen Jeffrey I, Delmonte Ottavia M, Forgues Marshonna, Liu Hui, Matthews Helen F, Shaw Elana, Stack Michael A, Weber Sarah E, Zhang Yu, Lisco Andrea, Sereti Irini, Su Helen C, Notarangelo Luigi D, Wang Xin Wei

2022

General General

COVID19 Diagnosis Using Chest X-rays and Transfer Learning.

In medRxiv : the preprint server for health sciences

** : A pandemic of respiratory illnesses from a novel coronavirus known as Sars-CoV-2 has swept across the globe since December of 2019. This is calling upon the research community including medical imaging to provide effective tools for use in combating this virus. Research in biomedical imaging of viral patients is already very active with machine learning models being created for diagnosing Sars-CoV-2 infections in patients using CT scans and chest x-rays. We aim to build upon this research. Here we used a transfer-learning approach to develop models capable of diagnosing COVID19 from chest x-ray. For this work we compiled a dataset of 112120 negative images from the Chest X-Ray 14 and 2725 positive images from public repositories. We tested multiple models, including logistic regression and random forest and XGBoost with and without principal components analysis, using five-fold cross-validation to evaluate recall, precision, and f1-score. These models were compared to a pre-trained deep-learning model for evaluating chest x-rays called COVID-Net. Our best model was XGBoost with principal components with a recall, precision, and f1-score of 0.692, 0.960, 0.804 respectively. This model greatly outperformed COVID-Net which scored 0.987, 0.025, 0.048. This model, with its high precision and reasonable sensitivity, would be most useful as "rule-in" test for COVID19. Though it outperforms some chemical assays in sensitivity, this model should be studied in patients who would not ordinarily receive a chest x-ray before being used for screening.

CCS CONCEPTS : Life and Medical Sciences • Machine Learning • Artificial Intelligence.

Reference format : Jonathan Stubblefield, Jason Causey, Dakota Dale, Jake Qualls, Emily Bellis, Jennifer Fowler, Karl Walker and Xiuzhen Huang. 2022. COVID19 Diagnosis Using Chest X-Rays and Transfer Learning.

Stubblefield Jonathan, Causey Jason, Dale Dakota, Qualls Jake, Bellis Emily, Fowler Jennifer, Walker Karl, Huang Xiuzhen

2022-Oct-12

General General

Distinct responses of newly identified monocyte subsets to advanced gastrointestinal cancer and COVID-19.

In Frontiers in immunology ; h5-index 100.0

Monocytes are critical cells of the immune system but their role as effectors is relatively poorly understood, as they have long been considered only as precursors of tissue macrophages or dendritic cells. Moreover, it is known that this cell type is heterogeneous, but our understanding of this aspect is limited to the broad classification in classical/intermediate/non-classical monocytes, commonly based on their expression of only two markers, i.e. CD14 and CD16. We deeply dissected the heterogeneity of human circulating monocytes in healthy donors by transcriptomic analysis at single-cell level and identified 9 distinct monocyte populations characterized each by a profile suggestive of specialized functions. The classical monocyte subset in fact included five distinct populations, each enriched for transcriptomic gene sets related to either inflammatory, neutrophil-like, interferon-related, and platelet-related pathways. Non-classical monocytes included two distinct populations, one of which marked specifically by elevated expression levels of complement components. Intermediate monocytes were not further divided in our analysis and were characterized by high levels of human leukocyte antigen (HLA) genes. Finally, we identified one cluster included in both classical and non-classical monocytes, characterized by a strong cytotoxic signature. These findings provided the rationale to exploit the relevance of newly identified monocyte populations in disease evolution. A machine learning approach was developed and applied to two single-cell transcriptome public datasets, from gastrointestinal cancer and Coronavirus disease 2019 (COVID-19) patients. The dissection of these datasets through our classification revealed that patients with advanced cancers showed a selective increase in monocytes enriched in platelet-related pathways. Of note, the signature associated with this population correlated with worse prognosis in gastric cancer patients. Conversely, after immunotherapy, the most activated population was composed of interferon-related monocytes, consistent with an upregulation in interferon-related genes in responder patients compared to non-responders. In COVID-19 patients we confirmed a global activated phenotype of the entire monocyte compartment, but our classification revealed that only cytotoxic monocytes are expanded during the disease progression. Collectively, this study unravels an unexpected complexity among human circulating monocytes and highlights the existence of specialized populations differently engaged depending on the pathological context.

Rigamonti Alessandra, Castagna Alessandra, Viatore Marika, Colombo Federico Simone, Terzoli Sara, Peano Clelia, Marchesi Federica, Locati Massimo

2022

COVID-19, cancer, immunotherapy, machine learning, monocyte, single-cell transcriptome

General General

Detection of COVID-19 using deep learning on x-ray lung images.

In PeerJ. Computer science

COVID-19 is a widespread deadly virus that directly affects the human lungs. The spread of COVID-19 did not stop at humans but also reached animals, so it was necessary to limit it is spread and diagnose cases quickly by applying a quarantine to the infected people. Recently x-ray lung images are used to determine the infection and from here the idea of this research came to use deep learning techniques to analyze x-ray lung images publicly available on Kaggle to possibly detect COVID-19 infection. In this article, we have proposed a method to possibly detect the COVID-19 by analyzing the X-ray images and applying a number of deep learning pre-trained models such as InceptionV3, DenseNet121, ResNet50, and VGG16, and the results are compared to determine the best performance model and accuracy with the least loss for our dataset. Our evaluation results showed that the best performing model for our dataset is ResNet50 with accuracies of 99.99%, 99.50%, and 99.44% for training, validation, and testing respectively followed by DenseNet121, InceptionV3, and finally VGG16.

Odeh AbdAlRahman, Alomar Ayah, Aljawarneh Shadi

2022

COVID-19, Classification, Deep learning, Supervised learning, Transfer learning

General General

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021).

In PeerJ. Computer science

Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020-2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1-13, 1442 (July 11-23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people's impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.

Shambour Mohd Khaled

2022

Convolutional Neural Networks (CNN), Deep learning, Hajj rituals, Long short term memory, Sentiment analysis

Public Health Public Health

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Disease Severity and to Test the Association with Unhealthy Opioid Use (UOU): Retrospective Cross-sectional Cohort Study.

In JMIR public health and surveillance

BACKGROUND : The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. Unhealthy opioid use impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19.

OBJECTIVE : To apply machine learning techniques in order to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity.

METHODS : This retrospective, cross-sectional cohort study was conducted based on data from 4,110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. Inclusion criteria were unplanned admissions for patients ≥18 years of age; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or two COVID-19 ICD-10 codes recorded in the encounter. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for two subgroups: encounters with UOU and COVID-19 and those with no-UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with three utilization metrics: low - unplanned admission, medium - unplanned admission and receiving mechanical ventilation, and high - unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and body mass index (BMI).

RESULTS : Topic modeling yielded ten topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (e.g., HIV) and no-UOU and COVID-19 (e.g., diabetes). In regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29, P=.009).

CONCLUSIONS : Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

CLINICALTRIAL :

Thompson Hale M, Sharma Brihat, Smith Dale, Bhalla Sameer, Erondu Ihuoma, Hazra Aniruddha, Ilyas Yousaf, Pachwicewicz Paul, Sheth Neeral K, Chhabra Neeraj, Karnik Niranjan S, Afshar Majid

2022-Oct-18

Pathology Pathology

A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters.

In Life science alliance

Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60-0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0-73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available at https://xixihospital-liufang.shinyapps.io/DynNomapp/.

Bao Jianfeng, Liu Shourong, Liang Xiao, Wang Congcong, Cao Lili, Li Zhaoyi, Wei Furong, Fu Ai, Shi Yingqiu, Shen Bo, Zhu Xiaoli, Zhao Yuge, Liu Hong, Miao Liangbin, Wang Yi, Liang Shuang, Wu Linyan, Huang Jinsong, Guo Tiannan, Liu Fang

2023-Jan

General General

Role of Technology in Detection of COVID-19.

In Cureus

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus caused coronavirus infection termed as COVID-19, an illness that has spread devastation all over the world. It was developed first in China and had swiftly spread throughout the world. COVID has created imposed burden on health in the lives of all individuals around the globe. This article provides a number of unprecedented detection technologies used in the detection of infection. COVID has created a large number of symptoms in the young, adolescent as well as elderly population. Old age people are susceptible to fatal serious symptoms because of low immunity. With these goals in mind, this article includes substantial condemning descriptions of the majority of initiatives in order to create diagnostic tools for easy diagnosis. It also provides the reader with a multidisciplinary viewpoint on how traditional approaches such as serology and reverse transcriptase polymerase chain reaction (RT-PCR) along with the frontline techniques such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas and artificial intelligence/machine learning have been utilized to gather information. The story will inspire creative new ways for successful detection therapy and to prevent this pandemic among a wide audience of operating and aspiring biomedical scientists and engineers.

Lohiya Drishti V, Pathak Swanand S

2022-Sep

artificial intelligence, cas system, covid, ct-scan, elisa, molecular investigations, rt-pcr, sars-cov-2, serological test, thermometer thermal scanners

General General

Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI.

In International journal of wireless information networks

** : In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.

Graphical Abstract :

Su Zhuoran, Pahlavan Kaveh, Agu Emmanuel, Wei Haowen

2022-Oct-14

BLE, COVID-19, Classical estimation theory, Proximity detection, RSSI features, UWB

Public Health Public Health

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections.

In The Science of the total environment

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Ravindra Khaiwal, Bahadur Samsher Singh, Katoch Varun, Bhardwaj Sanjeev, Kaur-Sidhu Maninder, Gupta Madhu, Mor Suman

2022-Oct-15

ARI, Air pollution, Machine learning programs, Random forest regression, Risk prediction

General General

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.

Hussain Mohammad Arafat, Mirikharaji Zahra, Momeny Mohammad, Marhamati Mahmoud, Neshat Ali Asghar, Garbi Rafeef, Hamarneh Ghassan

2022-Oct-07

Active learning, COVID-19, Deep learning, Noisy teacher, Pneumonia, Segmentation, Semi-supervised learning

Public Health Public Health

Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.

In Environmental science and pollution research international

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.

Yang Chuan, An Shuyi, Qiao Baojun, Guan Peng, Huang Desheng, Wu Wei

2022-Oct-18

Automatic machine learning, COVID-19, Countermeasures, HFMD, Prediction, Time series

General General

Clinical and Temporal Characterization of COVID-19 Subgroups Using Patient Vector Embeddings of Electronic Health Records.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : To identify and characterize clinical subgroups of hospitalized COVID-19 patients.

MATERIALS AND METHODS : Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups.

RESULTS : A diverse cohort of 11,313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3-74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and severity (mild-moderate/severe/critical). Subgroup temporal patterns were characterized by the durations in each subgroup, transitions between subgroups, and the complete paths throughout the course of hospitalization.

DISCUSSION : Several subgroups had mild-moderate SARS-CoV-2 infections but were hospitalized for underlying conditions (pregnancy, cardiovascular disease (CVD), etc.). Subgroup 7 included solid organ transplant recipients who mostly developed mild-moderate or severe disease. Subgroup 9 had a history of type-2 diabetes, kidney and CVD, and suffered the highest rates of heart failure (45.2%) and end-stage renal disease (80.6%). Subgroup 13 was the oldest (median: 82.7 years) and had mixed severity but high mortality (33.3%). Subgroup 17 had critical disease and the highest mortality (64.6%), with age (median: 68.1 years) being the only notable risk factor. Subgroups 18-20 had critical disease with high complication rates and long hospitalizations (median: 40+ days). All subgroups are detailed in the full text. A chord diagram depicts the most common transitions, and paths with the highest prevalence, longest hospitalizations, lowest and highest mortalities are presented. Understanding these subgroups and their pathways may aid clinicians in their decisions for better management and earlier intervention for patients.

Ta Casey N, Zucker Jason E, Chiu Po-Hsiang, Fang Yilu, Natarajan Karthik, Weng Chunhua

2022-Oct-18

COVID-19, Cluster analysis, SARS-CoV-2, Unsupervised machine learning

Public Health Public Health

Public Willingness to Engage With COVID-19 Contact Tracing, Quarantine, and Exposure Notification.

In Public health reports (Washington, D.C. : 1974)

OBJECTIVES : We conducted a survey to understand how people's willingness to share information with contact tracers, quarantine after a COVID-19 exposure, or activate and use a smartphone exposure notification (EN) application (app) differed by the person or organization making the request or recommendation.

METHODS : We analyzed data from a nationally representative survey with hypothetical scenarios asking participants (N = 2157) to engage in a public health action by health care providers, public health departments, employers, and others. We used Likert scales and ordered logistic regression to compare willingness to take action based on which person or organization made the request, and we summarized findings by race and ethnicity.

RESULTS : The highest levels of willingness to engage in contact tracing (adjusted odds ratio [aOR] = 1.74; 95% CI, 1.55-1.96), quarantine (aOR = 1.91; 95% CI, 1.69-2.15), download/activate an EN app (aOR = 1.30; 95% CI, 1.16-1.46), and notify other EN users (aOR = 1.43; 95% CI, 1.27-1.60) were reported when the request came from the participant's personal health care provider rather than from federal public health authorities. When compared with non-Hispanic White participants, non-Hispanic Black participants reported significantly higher levels of willingness to engage in contact tracing (aOR = 1.32; 95% CI, 1.18-1.48), quarantine (aOR = 1.49; 95% CI, 1.37-1.63), download/activate an EN app (aOR = 2.19; 95% CI, 2.01-2.38), and notify other EN users (aOR = 1.63; 95% CI, 1.49-1.79).

CONCLUSIONS : Partnering with individuals and organizations perceived as trustworthy may help influence people expressing a lower level of willingness to engage in each activity, while those expressing a higher level of willingness to engage in each activity may benefit from targeted communications.

Liccardi Ilaria, Alekseyev Jesslyn, Woltz Vilhelm L Andersen, McLean Jody E, Zurko Mary Ellen

2022-Oct-18

COVID-19, attitudes, contact tracing, exposure notification, health knowledge, practice, quarantine

Public Health Public Health

Struggling With Recovery From Opioids: Who Is at Risk During COVID-19?

In Journal of addiction medicine ; h5-index 27.0

OBJECTIVES : Individuals in recovery from opioid use disorder (OUD) are vulnerable to the impacts of the COVID-19 pandemic. Recent findings suggest increased relapse risk and overdose linked to COVID-19-related stressors. We aimed to identify individual-level factors associated with COVID-19-related impacts on recovery.

METHODS : This observational study (NCT04577144) enrolled 216 participants who previously partook in long-acting buprenorphine subcutaneous injection clinical trials (2015-2017) for OUD. Participants indicated how COVID-19 affected their recovery from substance use. A machine learning approach Classification and Regression Tree analysis examined the association of 28 variables with the impact of COVID-19 on recovery, including demographics, substance use, and psychosocial factors. Tenfold cross-validation was used to minimize overfitting.

RESULTS : Twenty-six percent of the sample reported that COVID-19 had made recovery somewhat or much harder. Past-month opioid use was higher among those who reported that recovery was harder compared with those who did not (51% vs 24%, respectively; P < 0.001). The final classification tree (overall accuracy, 80%) identified the Beck Depression Inventory (BDI-II) as the strongest independent risk factor associated with reporting COVID-19 impact. Individuals with a BDI-II score ≥10 had 6.45 times greater odds of negative impact (95% confidence interval, 3.29-13.30) relative to those who scored <10. Among individuals with higher BDI-II scores, less progress in managing substance use and treatment of OUD within the past 2 to 3 years were also associated with negative impacts.

CONCLUSIONS : These findings underscore the importance of monitoring depressive symptoms and perceived progress in managing substance use among those in recovery from OUD, particularly during large-magnitude crises.

Keith Diana R, Tegge Allison N, Stein Jeffrey S, Athamneh Liqa N, Craft William H, Chilcoat Howard D, Le Moigne Anne, DeVeaugh-Geiss Angela, Bickel Warren K

2022-Oct-18

Pathology Pathology

Robustness of Demonstration-based Learning Under Limited Data Scenario

ArXiv Preprint

Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonstrations can significantly improve performance on few-shot NER. However, why such demonstrations are beneficial for the learning process remains unclear since there is no explicit alignment between the demonstrations and the predictions. In this paper, we design pathological demonstrations by gradually removing intuitively useful information from the standard ones to take a deep dive of the robustness of demonstration-based sequence labeling and show that (1) demonstrations composed of random tokens still make the model a better few-shot learner; (2) the length of random demonstrations and the relevance of random tokens are the main factors affecting the performance; (3) demonstrations increase the confidence of model predictions on captured superficial patterns. We have publicly released our code at https://github.com/SALT-NLP/RobustDemo.

Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang, Diyi Yang

2022-10-19

General General

Retraction Note: An adaptive speech signal processing for COVID-19 detection using deep learning approach.

In International journal of speech technology

[This retracts the article DOI: 10.1007/s10772-021-09878-0.].

Al-Dhlan Kawther A

2022-Oct-13

General General

Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation

ArXiv Preprint

Cervical cancer is a public health problem, where the treatment has a better chance of success if detected early. The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Also, this paper compares models for instance and semantic detection approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results, and the best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively. For instance segmentation, the Mask R-CNN using ResNet-50 performs better in the visual inspection and has a 0.61 of the IoU metric. We conclude that the instance segmentation and semantic segmentation models can be used in combination to make a cascade model able to select a nucleus and subsequently segment the nucleus and its respective nucleolar organizer regions (NORs).

João Gustavo Atkinson Amorim, André Victória Matias, Allan Cerentini, Luiz Antonio Buschetto Macarini, Alexandre Sherlley Onofre, Fabiana Botelho Onofre, Aldo von Wangenheim

2022-10-19

General General

Profitability of Ichimoku-Based Trading Rule in Vietnam Stock Market in the Context of the COVID-19 Outbreak.

In Computational economics

Ichimoku Kinkohyo or Ichimoku Cloud Chart is one of the most popular technical indicators used by traders all over the world. However, its profitability is heavily influenced by the market environment, to which it is applied. Furthermore, the COVID-19 outbreak may have an impact on the market environment as well as the performance of all technical indicators. This study is the first to look into the profitability of Ichimoku-based trading rules in the Vietnamese stock market in the context of the COVID-19 outbreak. More particularly, the COVID-19 outbreak has a positive influence on the performance of this strategy when considering the entire market as well as a variety of industries including real estate industry, food and beverage industry, resource industry, and automotive and electronic components industry. Compared to the pre-pandemic period, the return on investment obtained per each transaction using the Ichimoku-based strategy increased by roughly 8 - 9 % in the pandemic period. Compared to the Buy-and-hold method, the Ichimoku-based strategy could slightly increase Accumulated return while posing a lower risk. The findings indicate that the Ichimoku-based strategy is applicable to the Vietnam stock market, regardless of the adverse effects of the pandemic on the industries.

Che-Ngoc Ha, Do-Thi Nga, Nguyen-Trang Thao

2022-Oct-13

COVID-19, Ichimoku cloud, Non-parametric statistics, Return on Investment, Vietnamese stock market

General General

Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials

ArXiv Preprint

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem. In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren-Miner index) in composite materials by processing the signals of Lamb Waves -- a non-destructive SHM technique -- with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude. The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an \textit{online learning} context where streams of data are fed continuously to the model, avoids repeated training -- essential for reliable real-time, continuous monitoring.

Ismael Ben-Yelun, Miguel Diaz-Lago, Luis Saucedo-Mora, Miguel Angel Sanz, Ricardo Callado, Francisco Javier Montans

2022-10-19

General General

Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator

ArXiv Preprint

When a deep learning model is sequentially trained on different datasets, it forgets the knowledge acquired from previous data, a phenomenon known as catastrophic forgetting. It deteriorates performance of the deep learning model on diverse datasets, which is critical in privacy-preserving deep learning (PPDL) applications based on transfer learning (TL). To overcome this, we propose review learning (RL), a generative-replay-based continual learning technique that does not require a separate generator. Data samples are generated from the memory stored within the synaptic weights of the deep learning model which are used to review knowledge acquired from previous datasets. The performance of RL was validated through PPDL experiments. Simulations and real-world medical multi-institutional experiments were conducted using three types of binary classification electronic health record data. In the real-world experiments, the global area under the receiver operating curve was 0.710 for RL and 0.655 for TL. Thus, RL was highly effective in retaining previously learned knowledge.

Jaesung Yoo, Sunghyuk Choi, Ye Seul Yang, Suhyeon Kim, Jieun Choi, Dongkyeong Lim, Yaeji Lim, Hyung Joon Joo, Dae Jung Kim, Rae Woong Park, Hyeong-Jin Yoon, Kwangsoo Kim

2022-10-17

Public Health Public Health

Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19.

In Scientific reports ; h5-index 158.0

We investigated the association between a wide range of comorbidities and COVID-19 in-hospital mortality and assessed the influence of multi morbidity on the risk of COVID-19-related death using a large, regional cohort of 6036 hospitalized patients. This retrospective cohort study was conducted using Patient Administration System Admissions and Discharges data. The International Classification of Diseases 10th edition (ICD-10) diagnosis codes were used to identify common comorbidities and the outcome measure. Individuals with lymphoma (odds ratio [OR], 2.78;95% CI,1.64-4.74), metastatic cancer (OR, 2.17; 95% CI,1.25-3.77), solid tumour without metastasis (OR, 1.67; 95% CI,1.16-2.41), liver disease (OR: 2.50, 95% CI,1.53-4.07), congestive heart failure (OR, 1.69; 95% CI,1.32-2.15), chronic obstructive pulmonary disease (OR, 1.43; 95% CI,1.18-1.72), obesity (OR, 5.28; 95% CI,2.92-9.52), renal disease (OR, 1.81; 95% CI,1.51-2.19), and dementia (OR, 1.44; 95% CI,1.17-1.76) were at increased risk of COVID-19 mortality. Asthma was associated with a lower risk of death compared to non-asthma controls (OR, 0.60; 95% CI,0.42-0.86). Individuals with two (OR, 1.79; 95% CI, 1.47-2.20; P < 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43-2.27; P < 0.001) were at increasingly higher risk of death when compared to those with no underlying conditions. Furthermore, multi morbidity patterns were analysed by identifying clusters of conditions in hospitalised COVID-19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identified, with recognisable co-occurrences of COVID-19 with different combinations of diseases, namely, cardiovascular (100%) and renal (15.6%) diseases in patient Cluster 1; mental and neurological disorders (100%) with metabolic and endocrine diseases (19.3%) in patient Cluster 2; respiratory (100%) and cardiovascular (15.0%) diseases in patient Cluster 3, cancer (5.9%) with genitourinary (9.0%) as well as metabolic and endocrine diseases (9.6%) in patient Cluster 4; metabolic and endocrine diseases (100%) and cardiovascular diseases (69.1%) in patient Cluster 5; mental and neurological disorders (100%) with cardiovascular diseases (100%) in patient Cluster 6. The highest mortality of 29.4% was reported in Cluster 6.

Bucholc Magda, Bradley Declan, Bennett Damien, Patterson Lynsey, Spiers Rachel, Gibson David, Van Woerden Hugo, Bjourson Anthony J

2022-Oct-15

General General

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

In BMC medical imaging

BACKGROUND : Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it's very important to be accurate in the early stages of diagnosis and treatment.

RESULTS : We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology's. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble.

CONCLUSIONS : To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.

Visuña Lara, Yang Dandi, Garcia-Blas Javier, Carretero Jesus

2022-Oct-15

CNN, COVID-19 classification, Deep ensemble learning, Grad-CAM, Stacking, Voting

Public Health Public Health

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

In The Science of the total environment

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

Lv Yunqian, Tian Hezhong, Luo Lining, Liu Shuhan, Bai Xiaoxuan, Zhao Hongyan, Zhang Kai, Lin Shumin, Zhao Shuang, Guo Zhihui, Xiao Yifei, Yang Junqi

2022-Oct-10

Air quality, COVID-19, Disease burden, Long-term trends, Meteorological parameters, Random forest

General General

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.

In Computers in biology and medicine

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy person's sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. With constant rise in the COVID-19 cases, there has been a constant rise in the need of efficient and safe ways to detect an infected individual. With the cases multiplying constantly, the current detecting devices like RT-PCR and fast testing kits have become short in supply. An effectual Covid-19 detection model using devised hybrid Honey Badger Optimization-based Deep Neuro Fuzzy Network (HBO-DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed hybrid HBO algorithm. Accordingly, the developed Hybrid HBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed Hybrid HBO-based DNFN is outpaced than other existing approaches in terms of testing accuracy, sensitivity and specificity of "0.9176, 0.9218 and 0. 9219". All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. When k-fold value is 9, sensitivity of existing techniques and developed JHBO-based DNFN is 0.8982, 0.8816, 0.8938, and 0.9207. The sensitivity of developed approach is improved by means of gaussian filtering model. The specificity of DCNN is 0.9125, BI-AT-GRU is 0.8926, and XGBoost is 0.9014, while developed JHBO-based DNFN is 0.9219 in k-fold value 9.

Dar Jawad Ahmad, Srivastava Kamal Kr, Ahmed Lone Sajaad

2022-Oct-03

(SARS-CoV-2) Covid-19 detection, Fuzzy, Hybrid optimization, Mel frequency cepstral coefficients, Neural network, Spectral centroid, Spectral flatness

General General

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.

In Computers in biology and medicine

Chest X-ray (CXR) images are considered useful to monitor and investigate a variety of pulmonary disorders such as COVID-19, Pneumonia, and Tuberculosis (TB). With recent technological advancements, such diseases may now be recognized more precisely using computer-assisted diagnostics. Without compromising the classification accuracy and better feature extraction, deep learning (DL) model to predict four different categories is proposed in this study. The proposed model is validated with publicly available datasets of 7132 chest x-ray (CXR) images. Furthermore, results are interpreted and explained using Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Modelagnostic Explanation (LIME), and SHapley Additive exPlanation (SHAP) for better understandably. Initially, convolution features are extracted to collect high-level object-based information. Next, shapely values from SHAP, predictability results from LIME, and heatmap from Grad-CAM are used to explore the black-box approach of the DL model, achieving average test accuracy of 94.31 ± 1.01% and validation accuracy of 94.54 ± 1.33 for 10-fold cross validation. Finally, in order to validate the model and qualify medical risk, medical sensations of classification are taken to consolidate the explanations generated from the eXplainable Artificial Intelligence (XAI) framework. The results suggest that XAI and DL models give clinicians/medical professionals persuasive and coherent conclusions related to the detection and categorization of COVID-19, Pneumonia, and TB.

Bhandari Mohan, Shahi Tej Bahadur, Siku Birat, Neupane Arjun

2022-Oct-03

COVID-19, Deep learning, Grad-CAM, LIME, Pneumonia, SHAP, Tuberculosis, eXplainable AI

Radiology Radiology

The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models' multilabel classification and a proof-of-concept study.

In Health informatics journal ; h5-index 25.0

BACKGROUND : Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification.

METHODS : In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases' categories of the datasets of requests and reports.

RESULTS : The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757-0.859)] to 0.976 [95% CI (0.956-0.996)] for the requests and 0.746 [95% CI (0.689-0.802)] to 1.0 [95% CI (1.0-1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922-0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data.

CONCLUSION : Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.

Olthof Allard W, van Ooijen Peter Ma, Cornelissen Ludo J

chest imaging, data mining, machine learning, natural language processing, radiology

General General

Smart healthcare: A prospective future medical approach for COVID-19.

In Journal of the Chinese Medical Association : JCMA

COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), (2) patient stratification with distinct short-term outcomes (e.g. mild or severe diseases) and long-term outcomes (e.g. long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep/machine learning (DL/ML) capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things (IoMT), which encompasses wearable devices, smartphone apps, Internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.

Yang De-Ming, Chang Tai-Jay, Hung Kai-Feng, Wang Mong-Lien, Cheng Yen-Fu, Chiang Su-Hua, Chen Mei-Fang, Liao Yi-Ting, Lai Wei-Qun, Liang Kung-Hao

2022-Oct-12

Public Health Public Health

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis.

In Disaster medicine and public health preparedness

OBJECTIVES : The present study aims to examine COVID-19 vaccination discussions on Twitter in Turkey and conduct sentiment analysis.

METHODS : The current study performed sentiment analysis of Twitter data with artificial intelligence (AI)'s Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first Covid-19 case was seen in Turkey, to April 18, 2022. 10308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing machine learning classifiers.

RESULTS : It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGB algorithm had higher scores.

CONCLUSIONS : Three out of four tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.

Özsezer Gözde, Mermer Gülengül

2022-Oct-13

COVID-19, Twitter, Vaccine, sentiment analysis

Public Health Public Health

Deep learning techniques for detecting and recognizing face masks: A survey.

In Frontiers in public health

The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.

Alturki Rahaf, Alharbi Maali, AlAnzi Ftoon, Albahli Saleh

2022

convolutional neural network, crowd monitoring, face mask, public health, transfer learning

General General

Warfarin anticoagulation management during the COVID-19 pandemic: The role of internet clinic and machine learning.

In Frontiers in pharmacology

Background: Patients who received warfarin require constant monitoring by hospital staff. However, social distancing and stay-at-home orders, which were universally adopted strategies to avoid the spread of COVID-19, led to unprecedented challenges. This study aimed to optimize warfarin treatment during the COVID-19 pandemic by determining the role of the Internet clinic and developing a machine learning (ML) model to predict anticoagulation quality. Methods: This retrospective study enrolled patients who received warfarin treatment in the hospital anticoagulation clinic (HAC) and "Internet + Anticoagulation clinic" (IAC) of the Nanjing Drum Tower Hospital between January 2020 and September 2021. The primary outcome was the anticoagulation quality of patients, which was evaluated by both the time in therapeutic range (TTR) and international normalized ratio (INR) variability. Anticoagulation quality and incidence of adverse events were compared between HAC and IAC. Furthermore, five ML algorithms were used to develop the anticoagulation quality prediction model, and the SHAP method was introduced to rank the feature importance. Results: Totally, 241 patients were included, comprising 145 patients in the HAC group and 96 patients in the IAC group. In the HAC group and IAC group, 73.1 and 69.8% (p = 0.576) of patients achieved good anticoagulation quality, with the average TTR being 79.9 ± 20.0% and 80.6 ± 21.1%, respectively. There was no significant difference in the incidence of adverse events between the two groups. Evaluating the five ML models using the test set, the accuracy of the XGBoost model was 0.767, and the area under the receiver operating characteristic curve was 0.808, which showed the best performance. The results of the SHAP method revealed that age, education, hypertension, aspirin, and amiodarone were the top five important features associated with poor anticoagulation quality. Conclusion: The IAC contributed to a novel management method for patients who received warfarin during the COVID-19 pandemic, as effective as HAC and with a low risk of virus transmission. The XGBoost model could accurately select patients at a high risk of poor anticoagulation quality, who could benefit from active intervention.

Dai Meng-Fei, Li Shu-Yue, Zhang Ji-Fan, Wang Bao-Yan, Zhou Lin, Yu Feng, Xu Hang, Ge Wei-Hong

2022

COVID-19, anticoagulation quality, internet, machine learning, telemedicine, warfarin

General General

Reduced B cell antigenicity of Omicron lowers host serologic response.

In Cell reports ; h5-index 119.0

The SARS-CoV-2 Omicron variant evades most neutralizing vaccine-induced antibodies and is associated with lower antibody titers upon breakthrough infections than previous variants. However, the mechanism remains unclear. Here, we find using a geometric deep-learning model that Omicron's extensively mutated receptor binding site (RBS) features reduced antigenicity compared with previous variants. Mice immunization experiments with different recombinant receptor binding domain (RBD) variants confirm that the serological response to Omicron is drastically attenuated and less potent. Analyses of serum cross-reactivity and competitive ELISA reveal a reduction in antibody response across both variable and conserved RBD epitopes. Computational modeling confirms that the RBS has a potential for further antigenicity reduction while retaining efficient receptor binding. Finally, we find a similar trend of antigenicity reduction over decades for hCoV229E, a common cold coronavirus. Thus, our study explains the reduced antibody titers associated with Omicron infection and reveals a possible trajectory of future viral evolution.

Tubiana Jérôme, Xiang Yufei, Fan Li, Wolfson Haim J, Chen Kong, Schneidman-Duhovny Dina, Shi Yi

2022-Sep-28

CP: Immunology, CP: Microbiology, Omicron variant of concern, SARS-CoV-2, antigenicity, computational structural biology, deep learning, spike protein

General General

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications.

In Environmental science and pollution research international

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.

Meskher Hicham, Belhaouari Samir Brahim, Thakur Amrit Kumar, Sathyamurthy Ravishankar, Singh Punit, Khelfaoui Issam, Saidur Rahman

2022-Oct-12

Artificial intelligent, COVID-19, Environmental analysis, MENA, Machine learning, Meteorological factors

General General

Angiotensin-converting Enzyme-2 (ACE2) Expression in Pediatric Liver Disease.

In Applied immunohistochemistry & molecular morphology : AIMM

The membrane protein angiotensin-converting enzyme-2 (ACE2) has gained notoriety as the receptor for severe acute respiratory syndrome coronavirus 2. Prior evidence has shown ACE2 is expressed within the liver but its function has not been fully discerned. Here, we utilized novel methodology to assess ACE2 expression in pediatric immune-mediated liver disease to better understand its presence in liver diseases and its role during infections such as COVID-19. We stained liver tissue with ACE2-specific immunofluorescent antibodies, analyzed via confocal microscopy. Computational deep learning-based segmentation models identified nuclei and cells, allowing the quantification of mean cellular and cytosolic immunofluorescent. Spatial transcriptomics provided high-throughput gene expression analysis in tissue to determine cellular composition for ACE2 expression. ACE2 plasma expression was quantified via enzyme-linked immunosorbent assay. High ACE2 expression was seen at the apical surface of cholangiocytes, with lower expression within hepatocyte cytosol and nonparenchymal cells (P<0.001). Children with liver disease had higher ACE2 hepatic expression than pediatric control tissue (P<0.001). Adult control tissue had higher expression than pediatric control (P<0.001). Plasma ACE2 was not found to be statistically different between samples. Spatial transcriptomics identified cell composition of ACE2-expressing spots containing antibody-secreting cells. Our results show ACE2 expression throughout the liver, with strongest localization to cholangiocyte membranes. Machine learning can be used to rapidly identify hepatic cellular components for histologic analysis. ACE2 expression in the liver may be increased in pediatric liver disease. Future work is needed to better understand the role of ACE2 in chronic disease and acute infections.

Stevens James P, Kolachala Vasantha L, Joshi Gaurav N, Nagpal Sini, Gibson Greg, Gupta Nitika A

2022-Oct-11

General General

Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction

bioRxiv Preprint

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the ACE2 receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly-compiled database of 96 species we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8 - 87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5 - 91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Mollentze, N.; Keen, D.; Munkhbayar, U.; Biek, R.; Streicker, D. G.

2022-10-13

General General

A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID

ArXiv Preprint

This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance need. We present 31,177 hours of flight data across 28,935 flights, which occur relative to 2,111 unplanned maintenance events clustered into 36 types of maintenance issues. Flights are annotated as before or after maintenance, with some flights occurring on the day of maintenance. Collecting data to evaluate predictive maintenance systems is challenging because it is difficult, dangerous, and unethical to generate data from compromised aircraft. To overcome this, we use the National General Aviation Flight Information Database (NGAFID), which contains flights recorded during regular operation of aircraft, and maintenance logs to construct a part failure dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and consider the probability that the RUL of a part is greater than 2 days. Unlike previous datasets generated with simulations or in laboratory settings, the NGAFID Aviation Maintenance Dataset contains real flight records and maintenance logs from different seasons, weather conditions, pilots, and flight patterns. Additionally, we provide Python code to easily download the dataset and a Colab environment to reproduce our benchmarks on three different models. Our dataset presents a difficult challenge for machine learning researchers and a valuable opportunity to test and develop prognostic health management methods

Hong Yang, Travis Desell

2022-10-13

General General

UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.

In An international journal on information fusion

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called  U n c e r t a i n t y F u s e N e t , which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of the existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble Monte Carlo Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively. Moreover, our  U n c e r t a i n t y F u s e N e t model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

Abdar Moloud, Salari Soorena, Qahremani Sina, Lam Hak-Keung, Karray Fakhri, Hussain Sadiq, Khosravi Abbas, Acharya U Rajendra, Makarenkov Vladimir, Nahavandi Saeid

2022-Oct-05

COVID-19, Deep learning, Early fusion, Feature fusion, Uncertainty quantification

General General

Post-script-Retail forecasting: Research and practice.

In International journal of forecasting

This note updates the 2019 review article "Retail forecasting: Research and practice" in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.

Fildes Robert, Kolassa Stephan, Ma Shaohui

COVID-19, Disruption, Instability, Machine learning, Omni-retailing, Online retail, Structural change

General General

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.

In Stochastic environmental research and risk assessment : research journal

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.

Sardar Iqra, Akbar Muhammad Azeem, Leiva Víctor, Alsanad Ahmed, Mishra Pradeep

2022-Oct-05

Artificial intelligence, Facebook Prophet algorithm, GLM, R software, SARS-CoV-2, South Asian Association for Regional Cooperation countries, Time-series models

General General

Predicting pattern of coronavirus using X-ray and CT scan images.

In Network modeling and analysis in health informatics and bioinformatics

Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world's central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.

Khurana Batra Payal, Aggarwal Paras, Wadhwa Dheeraj, Gulati Mehul

2022

CT scan, Convolutional Neural Network (CNN), Coronavirus, Deep learning, Prediction, X-ray

General General

ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System.

In Arabian journal for science and engineering

The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework's pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.

Saheb Shaik Khasim, Narayanan B, Rao Thota Venkat Narayana

2022-Oct-04

Convolutional neural networks, Covid-19, Deep learning, Medical image analysis, Multi-scale feature selection

Public Health Public Health

Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach.

In Frontiers in genetics ; h5-index 62.0

COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.

Ghose Partho, Alavi Muhaddid, Tabassum Mehnaz, Ashraf Uddin Md, Biswas Milon, Mahbub Kawsher, Gaur Loveleen, Mallik Saurav, Zhao Zhongming

2022

COVID-19, CT scan, classification, deep learning, transfer learning, x-ray

General General

Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning.

In Heliyon

The coronavirus disease 2019 (COVID-19) pandemic has severely affected Thailand's economy, which relies heavily on tourism. In this study, we labeled the sentiment and intention classes of English-language tweets related to tourism in Bangkok, Chiang Mai, and Phuket. Then, the accuracy of three machine learning algorithms (decision tree, random forest, and support vector machine) in predicting the sentiments and intentions of the tweets was investigated. The support vector machine algorithm provided the best results for sentiment analysis, with a maximum accuracy of 77.4%. In the intention analysis, the random forest algorithm achieved an accuracy of 95.4%. In a subsequent preliminary qualitative content analysis, the top 10 words found in each sentiment and intention class were gathered to provide insights and suggestions to help increase tourism in Thailand. The results of this study suggest that to help restore tourism in Thailand, tourist destinations, natural attractions, restaurants, and nightlife should be promoted. In addition, the two main concerns of tourists to Thailand should be addressed: COVID-19 and current political tensions.

Leelawat Natt, Jariyapongpaiboon Sirawit, Promjun Arnon, Boonyarak Samit, Saengtabtim Kumpol, Laosunthara Ampan, Yudha Alfan Kurnia, Tang Jing

2022-Oct

COVID19, Machine learning, Sentiment analysis, Thailand, Tourism, Tweet

General General

The Commoditization of AI for Molecule Design.

In Artificial intelligence in the life sciences

Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will increasingly require machine learning models for design and optimization of molecules as they become "designed by AI". AI and machine learning has essentially become a commodity within the pharmaceutical industry. This perspective will briefly describe our personal opinions of how machine learning has evolved and is being applied to model different molecule properties that crosses industries in their utility and ultimately suggests the potential for tight integration of AI into equipment and automated experimental pipelines. It will also describe how many groups have implemented generative models covering different architectures, for de novo design of molecules. We also highlight some of the companies at the forefront of using AI to demonstrate how machine learning has impacted and influenced our work. Finally, we will peer into the future and suggest some of the areas that represent the most interesting technologies that may shape the future of molecule design, highlighting how we can help increase the efficiency of the design-make-test cycle which is currently a major focus across industries.

Urbina Fabio, Ekins Sean

2022-Dec

Artificial intelligence, design-make-test, machine learning, molecule design, recurrent neural networks

Radiology Radiology

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening.

In Frontiers in public health

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

Liu Xiao-Ping, Yang Xu, Xiong Miao, Mao Xuanyu, Jin Xiaoqing, Li Zhiqiang, Zhou Shuang, Chang Hang

2022

Coronavirus Disease 2019 (COVID-19), artificial intelligence, biomedical imaging application, chest CT image, imaging biomarker, multicentric retrospective study

General General

EffViT-COVID: A dual-path network for COVID-19 percentage estimation.

In Expert systems with applications

The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19; however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labelled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves  0 . 9886 ± 0 . 009 ,  1 . 23 ± 0 . 378 , and  3 . 12 ± 1 . 56 , PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be  < 2 % . In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.

Chauhan Joohi, Bedi Jatin

2022-Oct-03

COVID-19, Deep network, EfficientNet-B7, Huber loss, Percentage estimation, Vision transformer

General General

Predicting South Korean adolescents vulnerable to obesity after the COVID-19 pandemic using categorical boosting and shapley additive explanation values: A population-based cross-sectional survey.

In Frontiers in pediatrics

Objective : This study identified factors related to adolescent obesity during the COVID-19 pandemic by using machine learning techniques and developed a model for predicting high-risk obesity groups among South Korean adolescents based on the result.

Materials and methods : This study analyzed 50,858 subjects (male: 26,535 subjects, and female: 24,323 subjects) between 12 and 18 years old. Outcome variables were classified into two classes (normal or obesity) based on body mass index (BMI). The explanatory variables included demographic factors, mental health factors, life habit factors, exercise factors, and academic factors. This study developed a model for predicting adolescent obesity by using multiple logistic regressions that corrected all confounding factors to understand the relationship between predictors for South Korean adolescent obesity by inputting the seven variables with the highest Shapley values found in categorical boosting (CatBoost).

Results : In this study, the top seven variables with a high impact on model output (based on SHAP values in CatBoost) were gender, mean sitting hours per day, the number of days of conducting strength training in the past seven days, academic performance, the number of days of drinking soda in the past seven days, the number of days of conducting the moderate-intensity physical activity for 60 min or more per day in the past seven days, and subjective stress perception level.

Conclusion : To prevent obesity in adolescents, it is required to detect adolescents vulnerable to obesity early and conduct monitoring continuously to manage their physical health.

Byeon Haewon

2022

COVID-19 pandemic, CatBoost, adolescent, machine learning, obesity

General General

COVID-19-related Nepali Tweets Classification in a Low Resource Setting

ArXiv Preprint

Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets into various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, they still need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models' relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.

Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, Bishesh Khanal

2022-10-11

General General

Access to online learning: Machine learning analysis from a social justice perspective.

In Education and information technologies

Access to education is the first step to benefiting from it. Although cumulative online learning experience is linked academic learning gains, between-country inequalities mean that large populations are prevented from accumulating such experience. Low-and-middle-income countries are affected by disadvantages in infrastructure such as internet access and uncontextualised learning content, and parents who are less available and less well-resourced than in high-income countries. COVID-19 has exacerbated the global inequalities, with girls affected more than boys in these regions. Therefore, the present research mined online learning data to identify features that are important for access to online learning. Data mining of 54,842,787 initial (random subsample n = 5000) data points from one online learning platform was conducted by partnering theory with data in model development. Following examination of a theory-led machine learning model, a data-led approach was taken to reach a final model. The final model was used to derive Shapley values for feature importance. As expected, country differences, gender, and COVID-19 were important features in access to online learning. The data-led model development resulted in additional insights not examined in the initial, theory-led model: namely, the importance of Math ability, year of birth, session difficulty level, month of birth, and time taken to complete a session.

McIntyre Nora A

2022-Oct-04

COVID-19, Country inequalities, Educational access, Machine learning, Online learning

General General

Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees

ArXiv Preprint

Misinformation spread in online social networks is an urgent-to-solve problem having harmful consequences that threaten human health, public safety, economics, and so on. In this study, we construct a novel dataset, called MiDe-22, having 5,284 English and 5,064 Turkish tweets with their misinformation labels under several recent events, including the Russia-Ukraine war, COVID-19 pandemic, and Refugees. Moreover, we provide the user engagements to the tweets in terms of likes, replies, retweets, and quotes. We present a detailed data analysis with descriptive statistics and temporal analysis, and provide the experimental results of a benchmark evaluation for misinformation detection on our novel dataset.

Cagri Toraman, Oguzhan Ozcelik, Furkan Şahinuç, Fazli Can

2022-10-11

General General

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine.

In Phytomedicine : international journal of phytotherapy and phytopharmacology

BACKGROUND : Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE : This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN : We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS : The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT : Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS : The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

Lin Yumeng, Zhang You, Wang Dongyang, Yang Bowen, Shen Ying-Qiang

2022-Oct-01

Artificial intelligence (AI), Computer-assisted, Drug screening, Drug design, Natural products, Traditional Chinese medicine (TCM)

General General

Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients' health prediction.

In PloS one ; h5-index 176.0

The fast-growing quantity of information hinders the process of machine learning, making it computationally costly and with substandard results. Feature selection is a pre-processing method for obtaining the optimal subset of features in a data set. Optimization algorithms struggle to decrease the dimensionality while retaining accuracy in high-dimensional data set. This article proposes a novel chaotic opposition fruit fly optimization algorithm, an improved variation of the original fruit fly algorithm, advanced and adapted for binary optimization problems. The proposed algorithm is tested on ten unconstrained benchmark functions and evaluated on twenty-one standard datasets taken from the Univesity of California, Irvine repository and Arizona State University. Further, the presented algorithm is assessed on a coronavirus disease dataset, as well. The proposed method is then compared with several well-known feature selection algorithms on the same datasets. The results prove that the presented algorithm predominantly outperform other algorithms in selecting the most relevant features by decreasing the number of utilized features and improving classification accuracy.

Bacanin Nebojsa, Budimirovic Nebojsa, K Venkatachalam, Strumberger Ivana, Alrasheedi Adel Fahad, Abouhawwash Mohamed

2022

Radiology Radiology

Evaluation of Federated Learning Variations for COVID-19 diagnosis using Chest Radiographs from 42 US and European hospitals.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described COVID-19 diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations.

MATERIALS AND METHODS : We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the FedAvg algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, FedAMP).

RESULTS : We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, p = 0.5) and improved model generalizability with the FedAvg model (p < 0.05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation.

CONCLUSION : FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.

Peng Le, Luo Gaoxiang, Walker Andrew, Zaiman Zachary, Jones Emma K, Gupta Hemant, Kersten Kristopher, Burns John L, Harle Christopher A, Magoc Tanja, Shickel Benjamin, Steenburg Scott D, Loftus Tyler, Melton Genevieve B, Gichoya Judy Wawira, Sun Ju, Tignanelli Christopher J

2022-Oct-10

Artificial Intelligence, COVID-19, Computer Vision, Federated Learning

General General

MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization.

In Frontiers in medicine

The pursuit of potential inhibitors for novel targets has become a very important problem especially over the last 2 years with the world in the midst of the COVID-19 pandemic. This entails performing high throughput screening exercises on drug libraries to identify potential "hits". These hits are identified using analysis of their physical properties like binding affinity to the target receptor, octanol-water partition coefficient (LogP) and more. However, drug libraries can be extremely large and it is infeasible to calculate and analyze the physical properties for each of those molecules within acceptable time and moreover, each molecule must possess a multitude of properties apart from just the binding affinity. To address this problem, in this study, we propose an extension to the Machine learning framework for Enhanced MolEcular Screening (MEMES) framework for multi-objective Bayesian optimization. This approach is capable of identifying over 90% of the most desirable molecules with respect to all required properties while explicitly calculating the values of each of those properties on only 6% of the entire drug library. This framework would provide an immense boost in identifying potential hits that possess all properties required for a drug molecules.

Mehta Sarvesh, Goel Manan, Priyakumar U Deva

2022

Bayesian optimization, High throughout screening, chemical space exploration, drug discovery, machine learning, virtual screening

General General

An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.

Das Bihter

2022-Nov-15

Big data analysis, Biomedical signal processing, Covid-19, Linear algebra, Machine learning

General General

Audits and COVID-19: A paradigm shift in the making.

In Business horizons

The COVID-19 pandemic has exposed the obsolescence and vulnerability of many existing auditing practices. Whilst some progressive practices have been implemented (i.e., remote audits using rudimentary Information & Communication Technologies), a new paradigm is needed to not only account for the risk of repeated lockdowns, but also to align practices with the level of digitalization, automation, and use of artificial intelligence in the current business environment. In this paper, we argue that the adoption of new technologies requires a fundamental rethinking of how auditing services are delivered. We argue that new technological possibilities have implications for five other auditing elements that enable a shift from the old to the new paradigm of auditing, namely actors, processes, spaces, training and skills development, and services. We explain how non-financial audits conducted under the new paradigm are key enablers of a firm's ability to participate and thrive in a competitive international marketplace.

Castka Pavel, Searcy Cory

2021-Nov-18

Audit, Certification, Inspection, Technology, Testing

Public Health Public Health

Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods.

In Frontiers in microbiology

Patients infected with SARS-CoV-2 at various severities have different clinical manifestations and treatments. Mild or moderate patients usually recover with conventional medical treatment, but severe patients require prompt professional treatment. Thus, stratifying infected patients for targeted treatment is meaningful. A computational workflow was designed in this study to identify key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection. First, the methylation features in the expression profile were deeply analyzed by a Monte Carlo feature selection method. A feature list was generated. Next, this ranked feature list was fed into the incremental feature selection method to determine the optimal features for different classification algorithms, thereby further building optimal classifiers. These selected key features were analyzed by functional enrichment to detect their biofunctional information. Furthermore, a set of rules were set up by a white-box algorithm, decision tree, to uncover different methylation patterns on various severity of SARS-CoV-2 infection. Some genes (PARP9, MX1, IRF7), corresponding to essential methylation sites, and rules were validated by published academic literature. Overall, this study contributes to revealing potential expression features and provides a reference for patient stratification. The physicians can prioritize and allocate health and medical resources for COVID-19 patients based on their predicted severe clinical outcomes.

Liu Zhiyang, Meng Mei, Ding ShiJian, Zhou XiaoChao, Feng KaiYan, Huang Tao, Cai Yu-Dong

2022

SARS-CoV-2, classification rule, machine learning, methylation, severity

General General

Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19.

In Scientific reports ; h5-index 158.0

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

Xu Yixi, Trivedi Anusua, Becker Nicholas, Blazes Marian, Ferres Juan Lavista, Lee Aaron, Conrad Liles W, Bhatraju Pavan K

2022-Oct-08

General General

The COVIDTW study: Clinical predictors of COVID-19 mortality and a novel AI prognostic model using chest X-ray.

In Journal of the Formosan Medical Association = Taiwan yi zhi

BACKGROUND : There is a lack of published research on the impact of the first wave of the COVID-19 pandemic in Taiwan. We investigated the mortality risk factors among critically ill patients with COVID-19 in Taiwan during the initial wave. Furthermore, we aim to develop a novel AI mortality prediction model using chest X-ray (CXR) alone.

METHOD : We retrospectively reviewed the medical records of patients with COVID-19 at Taipei Tzu Chi Hospital from May 15 to July 15 2021. We enrolled adult patients who received invasive mechanical ventilation. The CXR images of each enrolled patient were divided into 4 categories (1st, pre-ETT, ETT, and WORST). To establish a prediction model, we used the MobilenetV3-Small model with "Imagenet" pretrained weights, followed by high Dropout regularization layers. We trained the model with these data with Five-Fold Cross-Validation to evaluate model performance.

RESULT : A total of 64 patients were enrolled. The overall mortality rate was 45%. The median time from symptom onset to intubation was 8 days. Vasopressor use and a higher BRIXIA score on the WORST CXR were associated with an increased risk of mortality. The areas under the curve of the 1st, pre-ETT, ETT, and WORST CXRs by the AI model were 0.87, 0.92, 0.96, and 0.93 respectively.

CONCLUSION : The mortality rate of COVID-19 patients who receive invasive mechanical ventilation was high. Septic shock and high BRIXIA score were clinical predictors of mortality. The novel AI mortality prediction model using CXR alone exhibited a high performance.

Wu Chih-Wei, Pham Bach-Tung, Wang Jia-Ching, Wu Yao-Kuang, Kuo Chan-Yen, Hsu Yi-Chiung

2022-Sep-26

Artificial intelligence, COVID-19, Chest X-rays, Intensive care unit, Mortality, Prognosis

General General

Impact of Covid-19 on research and training in Parkinson's disease.

In International review of neurobiology

The Coronavirus Disease 2019 (Covid-19) pandemic and the consequent restrictions imposed worldwide have posed an unprecedented challenge to research and training in Parkinson's disease (PD). The pandemic has caused loss of productivity, reduced access to funding, an oft-acute switch to digital platforms, and changes in daily work protocols, or even redeployment. Frequently, clinical and research appointments were suspended or changed as a solution to limit the risk of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) spread and infection, but since the care and research in the field of movement disorders had traditionally been performed at in-person settings, the repercussions of the pandemic have even been more keenly felt in these areas. In this chapter, we review the implications of this impact on neurological research and training, with an emphasis on PD, as well as highlight lessons that can be learnt from how the Covid-19 pandemic has been managed in terms of restrictions in these crucial aspects of the neurosciences. One of the solutions brought to the fore has been to replace the traditional way of performing research and training with remote, and therefore socially distanced, alternatives. However, this has introduced fresh challenges in international collaboration, contingency planning, study prioritization, safety precautions, artificial intelligence, and various forms of digital technology. Nonetheless, in the long-term, these strategies will allow us to mitigate the adverse impact on PD research and training in future crises.

Wan Yi-Min, van Wamelen Daniel J, Lau Yue Hui, Rota Silvia, Tan Eng-King

2022

Covid-19, Impact, Movement disorders, “Parkinsons disease”, Research, Training

General General

Mathematical modeling and AI based decision making for COVID-19 suspects backed by novel distance and similarity measures on plithogenic hypersoft sets.

In Artificial intelligence in medicine ; h5-index 34.0

It goes without saying that coronavirus (COVID-19) is an infectious disease and many countries are coping with its different variants. Owing to the limited medical facilities, vaccine and medical experts, need of the hour is to intelligently tackle its spread by making artificial intelligence (AI) based smart decisions for COVID-19 suspects who develop different symptoms and they are kept under observation and monitored to see the severity of the symptoms. The target of this study is to analyze COVID-19 suspects data and detect whether a suspect is a COVID-19 patient or not, and if yes, then to what extent, so that a suitable decision can be made. The decision can be categorized such that an infected person can be isolated or quarantined at home or at a facilitation center or the person can be sent to the hospital for the treatment. This target is achieved by designing a mathematical model of COVID-19 suspects in the form of a multi-criteria decision making (MCDM) model and a novel AI based technique is devised and implemented with the help of newly developed plithogenic distance and similarity measures in fuzzy environment. All findings are depicted graphically for a clear understanding and to provide an insight of the necessity and effectiveness of the proposed method. The concept and results of the proposed technique make it suitable for implementation in machine learning, deep learning, pattern recognition etc.

Ahmad Muhammad Rayees, Afzal Usman

2022-Oct

COVID-19, Multi-criteria decision making (MCDM), Plithogenic distance measure (PDM), Plithogenic hypersoft set (PHSS), Plithogenic similarity measure (PSM)

General General

Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model.

In Artificial intelligence in medicine ; h5-index 34.0

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease.

Md Saleh Nurul Izrin, Ab Ghani Hadhrami, Jilani Zairul

2022-Oct

COVID-19, Formal Concept Analysis (FCA), Hospital admissions, Long Short-Term Memory (LSTM)

General General

A self-supervised COVID-19 CT recognition system with multiple regularizations.

In Computers in biology and medicine

The diagnosis of Coronavirus Disease 2019 (COVID-19) exploiting machine learning algorithms based on chest computed tomography (CT) images has become an important technology. Though many excellent computer-aided methods leveraging CT images have been designed, they do not possess sufficiently high recognition accuracy. Besides, these methods entail vast amounts of training data, which might be difficult to be satisfied in some real-world applications. To address these two issues, this paper proposes a novel COVID-19 recognition system based on CT images, which has high recognition accuracy, while only requiring a small amount of training data. Specifically, the system possesses the following three improvements: 1) Data: a novel redesigned BCELoss that incorporates Label Smoothing, Focal Loss, and Label Weighting Regularization (LSFLLW-R) technique for optimizing the solution space and preventing overfitting, 2) Model: a backbone network processed by two-phase contrastive self-supervised learning for classifying multiple labels, and 3) Method: a decision-fusing ensemble learning method for getting a more stable system, with balanced metric values. Our proposed system is evaluated on the small-scale expanded COVID-CT dataset, achieving an accuracy of 94.3%, a precision of 94.1%, a recall (sensitivity) of 93.4%, an F1-score of 94.7%, and an Area Under the Curve (AUC) of 98.9%, for COVID-19 diagnosis, respectively. These experimental results verify that our system can not only identify pathological locations effectively, but also achieve better performance in terms of accuracy, generalizability, and stability, compared with several other state-of-the-art COVID-19 diagnosis methods.

Lu Han, Dai Qun

2022-Sep-29

COVID-19 CT Diagnosis, Contrastive learning, Deep neural network, Ensemble learning, Loss regularization

Radiology Radiology

RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning.

In Radiology. Artificial intelligence

Purpose : To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning.

Materials and Methods : This retrospective study included patients who underwent a radiologic study between 2005 and 2020 at an outpatient imaging facility. Key images and associated labels from the studies were retrospectively extracted from the original study interpretation. These images were used for RadImageNet model training with random weight initiation. The RadImageNet models were compared with ImageNet models using the area under the receiver operating characteristic curve (AUC) for eight classification tasks and using Dice scores for two segmentation problems.

Results : The RadImageNet database consists of 1.35 million annotated medical images in 131 872 patients who underwent CT, MRI, and US for musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, abdominal, and pulmonary pathologic conditions. For transfer learning tasks on small datasets-thyroid nodules (US), breast masses (US), anterior cruciate ligament injuries (MRI), and meniscal tears (MRI)-the RadImageNet models demonstrated a significant advantage (P < .001) to ImageNet models (9.4%, 4.0%, 4.8%, and 4.5% AUC improvements, respectively). For larger datasets-pneumonia (chest radiography), COVID-19 (CT), SARS-CoV-2 (CT), and intracranial hemorrhage (CT)-the RadImageNet models also illustrated improved AUC (P < .001) by 1.9%, 6.1%, 1.7%, and 0.9%, respectively. Additionally, lesion localizations of the RadImageNet models were improved by 64.6% and 16.4% on thyroid and breast US datasets, respectively.

Conclusion : RadImageNet pretrained models demonstrated better interpretability compared with ImageNet models, especially for smaller radiologic datasets.Keywords: CT, MR Imaging, US, Head/Neck, Thorax, Brain/Brain Stem, Evidence-based Medicine, Computer Applications-General (Informatics) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Cadrin-Chênevert in this issue.

Mei Xueyan, Liu Zelong, Robson Philip M, Marinelli Brett, Huang Mingqian, Doshi Amish, Jacobi Adam, Cao Chendi, Link Katherine E, Yang Thomas, Wang Ying, Greenspan Hayit, Deyer Timothy, Fayad Zahi A, Yang Yang

2022-Sep

Brain/Brain Stem, CT, Computer Applications–General (Informatics), Evidence-based Medicine, Head/Neck, MR Imaging, Thorax, US

General General

Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.

In Frontiers in medicine

COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.

Castro-Castro Ana Cristina, Figueroa-Protti Lucia, Molina-Mora Jose Arturo, Rojas-Salas María Paula, Villafuerte-Mena Danae, Suarez-Sánchez María José, Sanabría-Castro Alfredo, Boza-Calvo Carolina, Calvo-Flores Leonardo, Solano-Vargas Mariela, Madrigal-Sánchez Juan José, Sibaja-Campos Mario, Silesky-Jiménez Juan Ignacio, Chaverri-Fernández José Miguel, Soto-Rodríguez Andrés, Echeverri-McCandless Ann, Rojas-Chaves Sebastián, Landaverde-Recinos Denis, Weigert Andreas, Mora Javier

2022

COVID-19, CXCL10, IL-38, SARS-CoV-2, cytokine profile

General General

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.

In Frontiers in immunology ; h5-index 100.0

Background : The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.

Methods : Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.

Results : The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.

Conclusions : Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

Penrice-Randal Rebekah, Dong Xiaofeng, Shapanis Andrew George, Gardner Aaron, Harding Nicholas, Legebeke Jelmer, Lord Jenny, Vallejo Andres F, Poole Stephen, Brendish Nathan J, Hartley Catherine, Williams Anthony P, Wheway Gabrielle, Polak Marta E, Strazzeri Fabio, Schofield James P R, Skipp Paul J, Hiscox Julian A, Clark Tristan W, Baralle Diana

2022

COVID-19, Critical Care, RNA-seq - RNA sequencing, biomarkers, prognosis, topology, transcriptome

General General

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia.

In EBioMedicine

BACKGROUND : A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients.

METHODS : This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers.

FINDINGS : Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses.

INTERPRETATION : SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated.

FUNDING : This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Lyons Patrick G, Bhavani Sivasubramanium V, Mody Aaloke, Bewley Alice, Dittman Katherine, Doyle Aisling, Windham Samuel L, Patel Tej M, Raju Bharat Neelam, Keller Matthew, Churpek Matthew M, Calfee Carolyn S, Michelson Andrew P, Kannampallil Thomas, Geng Elvin H, Sinha Pratik

2022-Oct-03

Hospital outcomes, Influenza, SARS-CoV-2, Statistical modelling, Viral pneumonia

Ophthalmology Ophthalmology

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

In PloS one ; h5-index 176.0

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

Trivedi Anusua, Robinson Caleb, Blazes Marian, Ortiz Anthony, Desbiens Jocelyn, Gupta Sunil, Dodhia Rahul, Bhatraju Pavan K, Liles W Conrad, Kalpathy-Cramer Jayashree, Lee Aaron Y, Lavista Ferres Juan M

2022

General General

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients.

In Medical & biological engineering & computing ; h5-index 32.0

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program.

Fadja Arnaud Nguembang, Fraccaroli Michele, Bizzarri Alice, Mazzuchelli Giulia, Lamma Evelina

2022-Oct-06

Covid-19, Decision Trees, Deep Learning, Hierarchical Probabilistic Logic Program, Severity

General General

Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.

In Journal of medical systems ; h5-index 48.0

Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.

Sitaula Chiranjibi, Shahi Tej Bahadur

2022-Oct-06

Classification, Deep learning, Detection, Monkeypox, Pandemic, SARS-Cov2

oncology Oncology

Exploration of the Potential Link, Hub Genes, and Potential Drugs for Coronavirus Disease 2019 and Lung Cancer Based on Bioinformatics Analysis.

In Journal of oncology

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has a huge influence on global public health and the economy. Lung cancer is one of the high-risk factors of COVID-19, but the molecular mechanism of lung cancer and COVID-19 is still unclear, and further research is needed. Therefore, we used the transcriptome information of the public database and adopted bioinformatics methods to identify the common pathways and molecular biomarkers of lung cancer and COVID-19 to further understand the connection between them. The two RNA-seq data sets in this study-GSE147507 (COVID-19) and GSE33532 (lung cancer)-were both derived from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) for lung cancer and COVID-19 patients. We conducted Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and found some common features between lung cancer and COVID-19. We also performed TFs-gene, miRNAs-gene, and gene-drug analyses. In total, 32 DEGs were found. A protein-protein interaction (PPI) network was constructed by DEGs, and 10 hub genes were screened. Finally, the identified drugs may be helpful for COVID-19 treatment.

Wang Ye, Li Qing, Zhang Jianfang, Xie Hui

2022

General General

An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification.

In Biomedizinische Technik. Biomedical engineering

OBJECTIVES : The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.

METHODS : The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells.

RESULTS : After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%.

CONCLUSIONS : Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.

Palanivel Silambarasi, Nallasamy Viswanathan

2022-Oct-05

deep learning, image classification, inception V3, leukocyte, residual network

General General

Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.

In Medical & biological engineering & computing ; h5-index 32.0

The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.

Wu Chennan, Liu Yang, Guo Xiang, Zhu Tianshui, Bao Zongliang

2022-Oct-05

Cognitive load, Deep learning, EEG, Eye-tracking, Multimodal, Remote learning

General General

Face mask detection and social distance monitoring system for COVID-19 pandemic.

In Multimedia tools and applications

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.

Javed Iram, Butt Muhammad Atif, Khalid Samina, Shehryar Tehmina, Amin Rashid, Syed Adeel Muzaffar, Sadiq Marium

2022-Sep-30

Coronavirus, Face mask detection, Single and multi-stage detectors, Social distance measurement

Public Health Public Health

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients.

In Annals of operations research

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.

Saadatmand Sara, Salimifard Khodakaram, Mohammadi Reza, Kuiper Alex, Marzban Maryam, Farhadi Akram

2022-Sep-29

COVID-19 pandemic, Ensemble modeling, ML in health systems, Supervised learning

Surgery Surgery

SARS-CoV-2: Has artificial intelligence stood the test of time.

In Chinese medical journal

Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government-public relations.

Sajid Mir Ibrahim, Ahmed Shaheer, Waqar Usama, Tariq Javeria, Chundrigarh Mohsin, Balouch Samira Shabbir, Abaidullah Sajid

2022-Aug-05

General General

Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

ArXiv Preprint

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis.

Saurav Sengupta, Johanna Loomba, Suchetha Sharma, Donald E. Brown, Lorna Thorpe, Melissa A Haendel, Christopher G Chute, Stephanie Hong

2022-10-05

Radiology Radiology

Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.

In The British journal of radiology

OBJECTIVES : The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs.

METHODS : This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHAP values.

RESULTS : The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed.

CONCLUSIONS : These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together.

ADVANCES IN KNOWLEDGE : This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.

Walston Shannon L, Matsumoto Toshimasa, Miki Yukio, Ueda Daiju

2022-Oct-04

General General

HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection

ArXiv Preprint

Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy single-image compression problem to reduce both computational and data requirements without training. For Cardiomegaly detection in chest X-ray images, we propose HeartSpot and four spatial bias priors. HeartSpot priors define how to sample pixels based on domain knowledge from medical literature and from machines. HeartSpot privatizes chest X-ray images by discarding up to 97% of pixels, such as those that reveal the shape of the thoracic cage, bones, small lesions and other sensitive features. HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart. HeartSpot offers strong compression, with up to 32x fewer pixels and 11x smaller filesize. Cardiomegaly detectors using HeartSpot are up to 9x faster to train or at least as accurate (up to +.01 AUC ROC) when compared to a baseline DenseNet121. HeartSpot is post-hoc explainable by re-using existing attribution methods without requiring access to the original non-privatized image. In summary, HeartSpot improves speed and accuracy, reduces image size, improves privacy and ensures explainability. Source code: https://www.github.com/adgaudio/HeartSpot

Elvin Johnson, Shreshta Mohan, Alex Gaudio, Asim Smailagic, Christos Faloutsos, Aurélio Campilho

2022-10-05

Public Health Public Health

Data Exploration and Classification of News Article Reliability: Deep Learning Study.

In JMIR infodemiology

Background : During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This "infodemic" is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic.

Objective : We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online.

Methods : First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability.

Results : We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model.

Conclusions : This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives.

Zhan Kevin, Li Yutong, Osmani Rafay, Wang Xiaoyu, Cao Bo

COVID-19, deep learning, ensemble model, false information, infodemic, news article reliability

General General

Learning to Act: Novel Integration of Algorithms and Models for Epidemic Preparedness

ArXiv Preprint

In this work we present a framework which may transform research and praxis in epidemic planning. Introduced in the context of the ongoing COVID-19 pandemic, we provide a concrete demonstration of the way algorithms may learn from epidemiological models to scale their value for epidemic preparedness. Our contributions in this work are two fold: 1) a novel platform which makes it easy for decision making stakeholders to interact with epidemiological models and algorithms developed within the Machine learning community, and 2) the release of this work under the Apache-2.0 License. The objective of this paper is not to look closely at any particular models or algorithms, but instead to highlight how they can be coupled and shared to empower evidence-based decision making.

Sekou L. Remy, Oliver E. Bent

2022-10-05

General General

Real-Time Social Distance Measurement and Face Masks Detection in Public Transportation Systems during the COVID-19 Pandemic and Post-pandemic Era: Theoretical Approach and Case Study in Italy.

In Transportation research interdisciplinary perspectives

Due to its remarkable learning ability and benefits in several areas of real-life, deep learning-based applications have recovered to be a topic of great research importance in the last years. This article presents a method devoted to guarantee safety conditions in public transportation systems (PTS) during COVID-19 pandemic and post-pandemic era. The paper describes a viable real-time model based on deep learning for monitoring social distance between users and detecting the face masks in stop areas and inside the vehicles of public transportation systems. Detections are made using the Deep learning approach and YOLOv3 algorithm. The safety rule violations are represented by red bounding-boxes and by red circles in the "eyes' bird view" as output of the video surveillance analyses. The Datasets used to train the neural network are the "Caltech Pedestrian Dataset" and the "COVID-19 Medical Face Mask Detection Dataset". Metrics, such as the Loss, the Accuracy and the Precision, obtained in the testing process of the neural network were used to evaluate the performance of the model in detecting the users and face masks. The proposed method was recently tested in the Public Transportation System of the Municipality of Piazza Armerina (Italy). The results show a significant reliability of the proposed method in detecting in real-time the interactions between users of the PTS in terms of variations over time of the mutual distancing and recognizing cases of violation of the imposed minimum social distance and FFP2 face masks use.

Guerrieri Marco, Parla Giuseppe

2022-Sep-28

Covid-19, Deep learning, Pandemic and post-pandemic era, Social distancing, YOLOv3, face mask detection

General General

COVID-19 Identification in Chest X-Ray Images Using Intelligent Multi-Level Classification Scenario.

In Computers & electrical engineering : an international journal

COVID-19 is an evolving respiratory transmittable disease, and it holds all daily activity worldwide as a global pandemic. It appeared in the city of Wuhan (China) in November 2019 and slowly started spreading to the rest of the world. The number of cases keeps increasing drastically, leading to a shortage of medical resources and testing kids worldwide. As the physicians facing this problem, several scientists and specialists in Artificial Intelligent (AI) are rendering their support to healthcare professionals in the early detection of COVID-19 using chest X-ray image samples to determine the level of severity at a low cost. This paper proposed Genetic Deep Learning Convolutional Neural Network (GDCNN) architecture that includes Huddle Particle Swarm Optimization as an alternative to Gradient descent. Huddle PSO performs better when clubbed with GDCNN architecture. Based on publicly available datasets, trained chest X-ray images are used to predict and identify various pneumonia diseases. The proposed model performed better with an accuracy of 97.23%, a sensitivity of 98.62%, specificity of 97.0%, and precision of 93.0%. The proposed model act as a tool for earlier detection of COVID-19. In the future, we plan to apply the proposed model for the larger dataset and to predict various lung diseases.

Babukarthik R G, Chandramohan D, Tripathi Diwakar, Kumar Manish, Sambasivam G

2022-Sep-26

COVID-19, Genetic Algorithm, Genetic Deep Learning Convolutional Neural Network, Huddle Particle Swarm, Optimization, Pneumonia

General General

Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray.

In Advances in computational intelligence

Due to the outbreak of COVID-19 disease globally, countries around the world are facing shortages of resources (i.e. testing kits, medicine). A quick diagnosis of COVID-19 and isolating patients are crucial in curbing the pandemic, especially in rural areas. This is because the disease is highly contagious and can spread easily. To assist doctors, several studies have proposed an initial detection of COVID-19 cases using radiological images. In this paper, we propose an alternative method for analyzing chest X-ray images to provide an efficient and accurate diagnosis of COVID-19 which can run on edge devices. The approach acts as an enabler for the deep learning model to be deployed in practical application. Here, the convolutional neural network models which are fine-tuned to predict COVID-19 and pneumonia infection from chest X-ray images are developed by adopting transfer learning techniques. The developed model yielded an accuracy of 98.13%, sensitivity of 97.7%, and specificity of 99.1%. To highlight the important regions in the X-ray images which directs the model to its decision/prediction, we adopted the Gradient Class Activation Map (Grad-CAM). The generated heat maps from the Grad-CAM were then compared with the annotated X-ray images by board-certified radiologists. Results showed that the findings strongly correlate with clinical evidence. For practical deployment, we implemented the trained model in edge devices (NCS2) and this has achieved an improvement of 90% in inference speed compared to CPU. This shows that the developed model has the potential to be implemented on the edge, for example in primary care clinics and rural areas which are not well-equipped or do not have access to stable internet connections.

Koh Shaline Jia Thean, Nafea Marwan, Nugroho Hermawan

2022

Chest x-ray, Deep learning, Edge computing, Visualization

General General

Predicting the sentiment of South Korean Twitter users toward vaccination after the emergence of COVID-19 Omicron variant using deep learning-based natural language processing.

In Frontiers in medicine

Although the full vaccination rate of South Korea compared to other countries, concerns about the effectiveness of the vaccine are growing as new COVID variants such as Alpha, Beta, Gamma, Delta, and Omicron appear over time. In this study, we collected Twitter data in South Korea that contained keywords like vaccines after the outbreak of the Omicron variant from 27 November 2021 to 14 February 2022. First, we analyzed the relationship between potential keywords associated with vaccination after the appearance of the Omicron variant in Twitter using network analysis. Second, we developed an efficient model for predicting the emotion of speech regarding vaccination after the COVID-19 Omicron variant pandemic by using deep learning algorithms. We constructed sentiment analysis models regarding vaccination after the COVID-19 Omicron pandemic by using five algorithms [i.e., support vector machine (SVM), recurrent neural networks (RNNs), long short-term memory models (LSTMs), bidirectional encoder representations from transformers (BERT), and Korean BERT (KoBERT)]. The results confirmed that KoBERT showed the best performance (71%) in all predictive performance indicators (accuracy, precision, and F1 score). It is necessary to prepare measures to alleviate the negative factorss of the public about vaccination in the long-term pandemic situation and help the public recognize the efficacy and safety of vaccination by using big data based on the results of this study.

Eom Gayeong, Yun Sanghyun, Byeon Haewon

2022

BERT, COVID-19 Omicron variant, NLP, deep learning, sentiment analysis

Radiology Radiology

Augmentation of literature review of COVID-19 radiology.

In World journal of radiology

We suggest an augmentation of the excellent comprehensive review article titled "Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic" under the following categories: (1) "Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia"; (2) "Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis"; (3) "Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including 'intelligent edge' and other remote monitoring devices"; (4) "Artificial intelligence in COVID-19 imaging"; (5) "Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed"; and (6) "A minor correction to a passage on pulmonary destruction".

Merchant Suleman Adam, Nadkarni Prakash, Shaikh Mohd Javed Saifullah

2022-Sep-28

Artificial intelligence in COVID-19, COVID-19 imaging, COVID-19 radiological findings, COVID-19-associated coagulopathy, Cardiac magnetic resonance imaging, Chest radiographs, Computed tomography, Hamptons hump, Westermark sign

General General

Forecasting adversities of COVID-19 waves in India using intelligent computing.

In Innovations in systems and software engineering

The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.

Chakraborty Arijit, Das Dipankar, Mitra Sajal, De Debashis, Pal Anindya J

2022-Sep-26

COVID-19, Extreme learning, MAPE, Machine learning, RMSE

General General

A robust defect detection method for syringe scale without positive samples.

In The Visual computer

With the worldwide spread of the COVID-19 pandemic, the demand for medical syringes has increased dramatically. Scale defect, one of the most common defects on syringes, has become a major barrier to boosting syringe production. Existing methods for scale defect detection suffer from large volumes of data requirements and the inability to handle diverse and uncertain defects. In this paper, we propose a robust scale defects detection method with only negative samples and favorable detection performance to solve this problem. Different from conventional methods that work in a batch-mode defects detection manner, we propose to locate the defects on syringes with a two-stage framework, which consists of two components, that is, the scale extraction network and the scale defect discriminator. Concretely, the SeNet is first built to utilize the convolutional neural network to extract the main structure of the scale. After that, the scale defect discriminator is designed to detect and label the scale defects. To evaluate the performance of our method, we conduct experiments on one real-world syringe dataset. The competitive results, that is, 99.7% on F1, prove the effectiveness of our method.

Wang Xiaodong, Xu Xianwei, Wang Yanli, Wu Pengtao, Yan Fei, Zeng Zhiqiang

2022-Sep-27

Deep learning, Defect detection, Image processing, Image segmentation

General General

A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms.

In Multimedia tools and applications

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model's parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting.

Pradhan Ashwini Kumar, Mishra Debahuti, Das Kaberi, Obaidat Mohammad S, Kumar Manoj

2022-Sep-27

COVID 19, Hill climbing algorithms, Image classification, Tailored convolutional neural network, X-Ray images

Public Health Public Health

Understanding the influence of online information, misinformation, disinformation and reinformation on COVID-19 vaccine acceptance: Protocol for a multicomponent study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The COVID-19 pandemic generated an explosion in the amount of information shared online, including false and misleading information on the virus, and recommended protective behaviours. Prior to the pandemic, online mis- and disinformation were already identified as having an impact on people's decision to refuse or delay recommended vaccination for themselves or their children.

OBJECTIVE : The overall aim of this study is to better understand the influence of online mis- and disinformation on COVID-19 decisions and investigate potential solutions to reduce the impact of online mis- and disinformation about vaccines.

METHODS : Based on different research approaches, this study involves 1) the use of artificial intelligence techniques, 2) a online survey, 3) interviews and, 4) a scoping review and an environmental scan of the literature.

RESULTS : As of September 1st, 2022, data collection is completed for all objectives. Analysis is being conducted and results should be disseminated in the upcoming months.

CONCLUSIONS : Findings from this study will help understand the underlying determinants of vaccine hesitancy among Canadian individuals and identify effective tailored interventions to improve vaccine acceptance among them.

INTERNATIONAL REGISTERED REPORT : DERR1-10.2196/41012.

Dube Eve, MacDonald Shannon E, Manca Terra, Bettinger Julie A, Driedger S Michelle, Graham Janice, Greyson Devon, MacDonald Noni E, Meyer Samantha, Roch Geneviève, Vivion Maryline, Aylsworth Laura, Witteman Holly, Gélinas-Gascon Félix, Marques Sathler Guimaraes Lucas, Hakim Hina, Gagnon Dominique, Béchard Benoît, Gramaccia Julie A, Khoury Richard, Tremblay Sébastien

2022-Sep-08

Radiology Radiology

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.

In Quantitative imaging in medicine and surgery

Background : This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.

Methods : This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.

Results : Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.

Conclusions : Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, which may facilitate individualized management of patients with this disease.

Jiang Zekun, Yin Jin, Han Peilun, Chen Nan, Kang Qingbo, Qiu Yue, Li Yiyue, Lao Qicheng, Sun Miao, Yang Dan, Huang Shan, Qiu Jiajun, Li Kang

2022-Oct

COVID-19, computed tomography (CT), machine learning, quantitative image analysis, radiomics

General General

Automated Medical Device Display Reading Using Deep Learning Object Detection

ArXiv Preprint

Telemedicine and mobile health applications, especially during the quarantine imposed by the covid-19 pandemic, led to an increase on the need of transferring health monitor readings from patients to specialists. Considering that most home medical devices use seven-segment displays, an automatic display reading algorithm should provide a more reliable tool for remote health care. This work proposes an end-to-end method for detection and reading seven-segment displays from medical devices based on deep learning object detection models. Two state of the art model families, EfficientDet and EfficientDet-lite, previously trained with the MS-COCO dataset, were fine-tuned on a dataset comprised by medical devices photos taken with mobile digital cameras, to simulate real case applications. Evaluation of the trained model show high efficiency, where all models achieved more than 98% of detection precision and more than 98% classification accuracy, with model EfficientDet-lite1 showing 100% detection precision and 100% correct digit classification for a test set of 104 images and 438 digits.

Lucas P. Moreira

2022-10-04

General General

Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes.

In Frontiers in sociology

Ageism has not been centered in scholarship on AI or algorithmic harms despite the ways in which older adults are both digitally marginalized and positioned as targets for surveillance technology and risk mitigation. In this translation paper, we put gerontology into conversation with scholarship on information and data technologies within critical disability, race, and feminist studies and explore algorithmic harms of surveillance technologies on older adults and care workers within nursing homes in the United States and Canada. We start by identifying the limitations of emerging scholarship and public discourse on "digital ageism" that is occupied with the inclusion and representation of older adults in AI or machine learning at the expense of more pressing questions. Focusing on the investment in these technologies in the context of COVID-19 in nursing homes, we draw from critical scholarship on information and data technologies to deeply understand how ageism is implicated in the systemic harms experienced by residents and workers when surveillance technologies are positioned as solutions. We then suggest generative pathways and point to various possible research agendas that could illuminate emergent algorithmic harms and their animating force within nursing homes. In the tradition of critical gerontology, ours is a project of bringing insights from gerontology and age studies to bear on broader work on automation and algorithmic decision-making systems for marginalized groups, and to bring that work to bear on gerontology. This paper illustrates specific ways in which important insights from critical race, disability and feminist studies helps us draw out the power of ageism as a rhetorical and analytical tool. We demonstrate why such engagement is necessary to realize gerontology's capacity to contribute to timely discourse on algorithmic harms and to elevate the issue of ageism for serious engagement across fields concerned with social and economic justice. We begin with nursing homes because they are an understudied, yet socially significant and timely setting in which to understand algorithmic harms. We hope this will contribute to broader efforts to understand and redress harms across sectors and marginalized collectives.

Berridge Clara, Grigorovich Alisa

2022

artificial intelligence, big data, dementia, long-term care, machine learning, older adults, privacy, technology

General General

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.

In Archives of computational methods in engineering : state of the art reviews

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

Koul Apeksha, Bawa Rajesh K, Kumar Yogesh

2022-Sep-28

General General

SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.

In Frontiers in immunology ; h5-index 100.0

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

Shashkova Tatiana I, Umerenkov Dmitriy, Salnikov Mikhail, Strashnov Pavel V, Konstantinova Alina V, Lebed Ivan, Shcherbinin Dmitriy N, Asatryan Marina N, Kardymon Olga L, Ivanisenko Nikita V

2022

GVP, antibody - antigen complex, conformational B-cell epitopes, epitopes, protein language model, transfer learning, transformer

General General

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches.

In Computational intelligence and neuroscience

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model's development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted.

Hajjej Fahima, Ayouni Sarra, Hasan Malek, Abir Tanvir

2022

General General

Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 mRNA SARS-CoV-2 vaccine via non-negative tensor factorization.

In iScience

Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.

Ikeda Kei, Nakada Taka-Aki, Kageyama Takahiro, Tanaka Shigeru, Yoshida Naoki, Ishikawa Tetsuo, Goshima Yuki, Otaki Natsuko, Iwami Shingo, Shimamura Teppei, Taniguchi Toshibumi, Igari Hidetoshi, Hanaoka Hideki, Yokote Koutaro, Tsuyuzaki Koki, Nakajima Hiroshi, Kawakami Eiryo

2022-Sep-28

Adverse reaction, COVID19, Computational phenotyping, Tensor factorization, mRNA vaccine

General General

A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.

In Biomedical signal processing and control

Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase, caused by the difference in the image intensity and object region distribution between the training set and test set. In this paper, we proposed a novel unsupervised method for COVID-19 infection segmentation that aims to learn the domain-invariant features from lung cancer and COVID-19 images to improve the generalization ability of the segmentation network for use with COVID-19 CT images. First, to address the intensity difference, we proposed a novel data augmentation module based on Fourier Transform, which transfers the annotated lung cancer data into the style of COVID-19 image. Secondly, to reduce the distribution difference, we designed a teacher-student network to learn rotation-invariant features for segmentation. The experiments demonstrated that even without getting access to the annotations of the COVID-19 CT images during the training phase, the proposed network can achieve a state-of-the-art segmentation performance on COVID-19 infection.

Chen Han, Jiang Yifan, Ko Hanseok, Loew Murray

2023-Jan

COVID-19, Computed tomography, Fourier Transform, Infection segmentation, Teacher–student network

General General

The role of diversity and ensemble learning in credit card fraud detection.

In Advances in data analysis and classification

The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process. Standard machine learning techniques are widely employed, but inadequate for the evolving nature of customers behavior entailing continuous changes in the underlying data distribution. his problem is often tackled by discarding past knowledge, despite its potential relevance in the case of recurrent concepts. Appropriate exploitation of historical knowledge is necessary: we propose a learning strategy that relies on diversity-based ensemble learning and allows to preserve past concepts and reuse them for a faster adaptation to changes. In our experiments, we adopt several state-of-the-art diversity measures and we perform comparisons with various other learning approaches. We assess the effectiveness of our proposed learning strategy on extracts of two real datasets from two European countries, containing more than 30 M and 50 M transactions, provided by our industrial partner, Worldline, a leading company in the field.

Paldino Gian Marco, Lebichot Bertrand, Le Borgne Yann-Aël, Siblini Wissam, Oblé Frédéric, Boracchi Giacomo, Bontempi Gianluca

2022-Sep-28

Concept drift, Diversity, Ensemble learning, Finance, Fraud detection

General General

Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology.

In Technology in society

Online reviews have been used effectively to understand customers' satisfaction and preferences. COVID-19 crisis has significantly impacted customers' satisfaction in several sectors such as tourism and hospitality. Although several research studies have been carried out to analyze consumers' satisfaction using survey-based methodologies, consumers' satisfaction has not been well explored in the event of the COVID-19 crisis, especially using available data in social network sites. In this research, we aim to explore consumers' satisfaction and preferences of restaurants' services during the COVID-19 crisis. Furthermore, we investigate the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. We applied a new approach to achieve the objectives of this research. We first developed a hybrid approach using clustering, supervised learning, and text mining techniques. Learning Vector Quantization (LVQ) was used to cluster customers' preferences. To predict travelers' preferences, decision trees were applied to each segment of LVQ. We used a text mining technique; Latent Dirichlet Allocation (LDA), for textual data analysis to discover the satisfaction criteria from online customers' reviews. After analyzing the data using machine learning techniques, a theoretical model was developed to inspect the relationships between the restaurants' quality factors and customers' satisfaction. In this stage, Partial Least Squares (PLS) technique was employed. We evaluated the proposed approach using a dataset collected from the TripAdvisor platform. The outcomes of the two-stage methodology were discussed and future research directions were suggested according to the limitations of this study.

Zibarzani Masoumeh, Abumalloh Rabab Ali, Nilashi Mehrbakhsh, Samad Sarminah, Alghamdi O A, Nayer Fatima Khan, Ismail Muhammed Yousoof, Mohd Saidatulakmal, Mohammed Akib Noor Adelyna

2022-Aug

Customer satisfaction, Machine learning, Segmentation, Social data analysis, Text mining

Surgery Surgery

Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011-2021: A bibliometric analysis.

In Frontiers in public health

Ultrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is an advanced computer program that can solve this problem. Therefore, the relevant literature on the application of artificial intelligence in the ultrasonic field from 2011 to 2021 was screened by authors from the Web of Science Core Collection, which aims to summarize the trend of artificial intelligence application in the field of ultrasound, meanwhile, visualize and predict research hotspots. A total of 908 publications were included in the study. Overall, the number of global publications is on the rise, and studies on the application of artificial intelligence in the field of ultrasound continue to increase. China has made the largest contribution in this field. In terms of institutions, Fudan University has the most number of publications. Recently, IEEE Access is the most published journal. Suri J. S. published most of the articles and had the highest number of citations in this field (29 articles). It's worth noting that, convolutional neural networks (CNN), as a kind of deep learning algorithm, was considered to bring better image analysis and processing ability in recent most-cited articles. According to the analysis of keywords, the latest keyword is "COVID-19" (2020.8). The co-occurrence analysis of keywords by VOSviewer visually presented four clusters which consisted of "deep learning," "machine learning," "application in the field of visceral organs," and "application in the field of cardiovascular". The latest hot words of these clusters were "COVID-19; neural-network; hepatocellular carcinoma; atherosclerotic plaques". This study reveals the importance of multi-institutional and multi-field collaboration in promoting research progress.

Xia Demeng, Chen Gaoqi, Wu Kaiwen, Yu Mengxin, Zhang Zhentao, Lu Yixian, Xu Lisha, Wang Yin

2022

CNN, COVID-19, artificial intelligence, bibliometrics, ultrasound

General General

The impact factors of social media users' forwarding behavior of COVID-19 vaccine topic: Based on empirical analysis of Chinese Weibo users.

In Frontiers in public health

Introduction : Social media, an essential source of public access to information regarding the COVID-19 vaccines, has a significant effect on the transmission of information regarding the COVID-19 vaccines and helps the public gain correct insights into the effectiveness and safety of the COVID-19 vaccines. The forwarding behavior of social media users on posts concerned with COVID-19 vaccine topics can rapidly disseminate vaccine information in a short period, which has a significant effect on transmission and helps the public access relevant information. However, the factors of social media users' forwarding posts are still uncertain thus far. In this paper, we investigated the factors of the forwarding COVID-19 vaccines Weibo posts on Chinese social media and verified the correlation between social network characteristics, Weibo textual sentiment characteristics, and post forwarding.

Methods : This paper used data mining, machine learning, sentiment analysis, social network analysis, and regression analysis. Using " (COVID-19 vaccine)" as the keyword, we used data mining to crawl 121,834 Weibo posts on Sina Weibo from 1 January 2021 to 31 May 2021. Weibo posts not closely correlated with the topic of the COVID-19 vaccines were filtered out using machine learning. In the end, 3,158 posts were used for data analysis. The proportions of positive sentiment and negative sentiment in the textual of Weibo posts were calculated through sentiment analysis. On that basis, the sentiment characteristics of Weibo posts were determined. The social network characteristics of information transmission on the COVID-19 vaccine topic were determined through social network analysis. The correlation between social network characteristics, sentiment characteristics of the text, and the forwarding volume of posts was verified through regression analysis.

Results : The results suggest that there was a significant positive correlation between the degree of posting users in the social network structure and the amount of forwarding. The relationship between the closeness centrality and the forwarding volume was significantly positive. The betweenness centrality was significantly positively correlated with the forwarding volume. There was no significant relationship between the number of posts containing more positive sentiments and the forwarding volume of posts. There was a significant positive correlation between the number of Weibo posts containing more negative sentiments and the forwarding volume.

Conclusion : According to the characteristics of users, COVID-19 vaccine posts from opinion leaders, "gatekeepers," and users with high-closeness centrality are more likely to be reposted. Users with these characteristics should be valued for their important role in disseminating information about COVID-19 vaccines. In addition, the sentiment contained in the Weibo post is an important factor influencing the public to forward vaccine posts. Special attention should be paid to the negative sentimental tendency contained in this post on Weibo to mitigate the negative impact of the information epidemic and improve the transmission effect of COVID-19 vaccine information.

Sun Kun, Wang Han, Zhang Jinsheng

2022

COVID-19 vaccine, emotion, forwarding behavior, social media, social network structure

Public Health Public Health

Characteristics and outcomes of SARS-COV 2 critically ill patients after emergence of the variant of concern 20H/501Y.V2: A comparative cohort study.

In Medicine

There are currently no data regarding characteristics of critically ill patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant of concern (VOC) 20H/501Y.V2. We therefore aimed to describe changes of characteristics in critically ill patients with Covid-19 between the first and the second wave when viral genome sequencing indicated that VOC was largely dominant in Mayotte Island (Indian Ocean). Consecutive patients with Covid-19 and over 18 years admitted in the unique intensive care unit (ICU) of Mayotte during wave 2 were compared with an historical cohort of patients admitted during wave 1. We performed a LR comparing wave 1 and wave 2 as outcomes. To complete analysis, we built a Random Forest model (RF), that is, a machine learning classification tool- using the same variable set as that of the LR. We included 156 patients, 41 (26.3%) and 115 (73.7%) belonging to the first and second waves respectively. Univariate analysis did not find difference in demographic data or in mortality. Our multivariate LR found that patients in wave 2 had less fever (absence of fever aOR 5.23, 95% confidence interval (CI) 1.89-14.48, p = .001) and a lower simplified acute physiology score (SAPS II) (aOR 0.95, 95% CI 0.91-0.99, p = .007) at admission; at 24 hours, the need of invasive mechanical ventilation was higher (aOR 3.49, 95% CI 0.98-12.51, p = .055) and pO2/FiO2 ratio was lower (aOR 0.99, 95 % CI 0.98-0.99, p = .03). Patients in wave 2 had also an increased risk of ventilator-associated pneumonia (VAP) (aOR 4.64, 95% CI 1.54-13.93, p = .006). Occurrence of VAP was also a key variable to classify patients between wave 1 and wave 2 in the variable importance plot of the RF model. Our data suggested that VOC 20H/501Y.V2 could be associated with a higher severity of respiratory failure at admission and a higher risk for developing VAP. We hypothesized that the expected gain in survival brought by recent improvements in critical care management could have been mitigated by increased transmissibility of the new lineage leading to admission of more severe patients. The immunological role of VOC 20H/501Y.V2 in the propensity for VAP requires further investigations.

Aries Philippe, Huet Olivier, Balicchi Julien, Mathais Quentin, Estagnasie Camille, Martin-Lecamp Gonzague, Simon Olivier, Morvan Anne-Cécile, Puech Bérénice, Subiros Marion, Blonde Renaud, Boue Yvonnick

2022-Sep-30

Public Health Public Health

Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens.

In EBioMedicine

BACKGROUND : The identification of baseline host determinants that associate with robust HIV-1 vaccine-induced immune responses could aid HIV-1 vaccine development. We aimed to assess both the collective and relative performance of baseline characteristics in classifying individual participants in nine different Phase 1-2 HIV-1 vaccine clinical trials (26 vaccine regimens, conducted in Africa and in the Americas) as High HIV-1 vaccine responders.

METHODS : This was a meta-analysis of individual participant data, with studies chosen based on participant-level (vs. study-level summary) data availability within the HIV-1 Vaccine Trials Network. We assessed the performance of 25 baseline characteristics (demographics, safety haematological measurements, vital signs, assay background measurements) and estimated the relative importance of each characteristic in classifying 831 participants as High (defined as within the top 25th percentile among positive responders or above the assay upper limit of quantification) versus Non-High responders. Immune response outcomes included HIV-1-specific serum IgG binding antibodies and Env-specific CD4+ T-cell responses assessed two weeks post-last dose, all measured at central HVTN laboratories. Three variable importance approaches based on SuperLearner ensemble machine learning were considered.

FINDINGS : Overall, 30.1%, 50.5%, 36.2%, and 13.9% of participants were categorized as High responders for gp120 IgG, gp140 IgG, gp41 IgG, and Env-specific CD4+ T-cell vaccine-induced responses, respectively. When including all baseline characteristics, moderate performance was achieved for the classification of High responder status for the binding antibody responses, with cross-validated areas under the ROC curve (CV-AUC) of 0.72 (95% CI: 0.68, 0.76) for gp120 IgG, 0.73 (0.69, 0.76) for gp140 IgG, and 0.67 (95% CI: 0.63, 0.72) for gp41 IgG. In contrast, the collection of all baseline characteristics yielded little improvement over chance for predicting High Env-specific CD4+ T-cell responses [CV-AUC: 0.53 (0.48, 0.58)]. While estimated variable importance patterns differed across the three approaches, female sex assigned at birth, lower height, and higher total white blood cell count emerged as significant predictors of High responder status across multiple immune response outcomes using Approach 1. Of these three baseline variables, total white blood cell count ranked highly across all three approaches for predicting vaccine-induced gp41 and gp140 High responder status.

INTERPRETATION : The identified features should be studied further in pursuit of intervention strategies to improve vaccine responses and may be adjusted for in analyses of immune response data to enhance statistical power.

FUNDING : National Institute of Allergy and Infectious Diseases (UM1AI068635 to YH, UM1AI068614 to GDT, UM1AI068618 to MJM, and UM1 AI069511 to MCK), the Duke CFAR P30 AI064518 to GDT, and National Institute of Dental and Craniofacial Research (R01DE027245 to JJK). This work was also supported by the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding sources.

Huang Yunda, Zhang Yuanyuan, Seaton Kelly E, De Rosa Stephen, Heptinstall Jack, Carpp Lindsay N, Randhawa April Kaur, McKinnon Lyle R, McLaren Paul, Viegas Edna, Gray Glenda E, Churchyard Gavin, Buchbinder Susan P, Edupuganti Srilatha, Bekker Linda-Gail, Keefer Michael C, Hosseinipour Mina C, Goepfert Paul A, Cohen Kristen W, Williamson Brian D, McElrath M Juliana, Tomaras Georgia D, Thakar Juilee, Kobie James J

2022-Sep-27

Antibody, Baseline characteristics, CD4+ T cell, SuperLearner, Vaccine response heterogeneity, Variable importance measurements

General General

Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation.

In Medical image analysis

Medical image segmentation methods based on deep learning have made remarkable progress. However, such existing methods are sensitive to data distribution. Therefore, slight domain shifts will cause a decline of performance in practical applications. To relieve this problem, many domain adaptation methods learn domain-invariant representations by alignment or adversarial training whereas ignoring domain-specific representations. In response to this issue, this paper rethinks the traditional domain adaptation framework and proposes a novel orthogonal decomposition adversarial domain adaptation (ODADA) architecture for medical image segmentation. The main idea behind our proposed ODADA model is to decompose the input features into domain-invariant and domain-specific representations and then use the newly designed orthogonal loss function to encourage their independence. Furthermore, we propose a two-step optimization strategy to extract domain-invariant representations by separating domain-specific representations, fighting the performance degradation caused by domain shifts. Encouragingly, the proposed ODADA framework is plug-and-play and can replace the traditional adversarial domain adaptation module. The proposed method has consistently demonstrated effectiveness through comprehensive experiments on three publicly available datasets, including cross-site prostate segmentation dataset, cross-site COVID-19 lesion segmentation dataset, and cross-modality cardiac segmentation dataset. The source code is available at https://github.com/YonghengSun1997/ODADA.

Sun Yongheng, Dai Duwei, Xu Songhua

2022-Sep-21

Medical image segmentation, Orthogonal decomposition, Unsupervised domain adaptation

Public Health Public Health

Reproduction numbers of SARS-CoV-2 Omicron subvariants.

In Journal of travel medicine

Estimating the effective reproduction number of Omicron subvariants is crucial for evaluating the effectiveness of control measures, and adjusting control measures promptly. We conducted a systematic review to synthesize the evidence from estimates of the reproduction numbers for Omicron subvariants, and estimated their effective reproduction number.

Wang Shuqi, Zhang Fengdi, Wang Zhen, Du Zhanwei, Gao Chao

2022-Sep-30

Public Health Public Health

Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method.

In The journal of physical chemistry. B

The emergence of SARS-CoV-2 and its variants that critically affect global public health requires characterization of mutations and their evolutionary pattern from specific Variants of Interest (VOIs) to Variants of Concern (VOCs). Leveraging the concept of equilibrium statistical mechanics, we introduce a new responsive quantity defined as "Mutational Response Function (MRF)" aptly quantifying domain-wise average entropy-fluctuation in the spike glycoprotein sequence of SARS-CoV-2 based on its evolutionary database. As the evolution transits from a specific variant to VOC, we find that the evolutionary crossover is accompanied by a dramatic change in MRF, upholding the characteristic of a dynamic phase transition. With this entropic information, we have developed an ancestral-based machine learning method that helps predict future domain-specific mutations. The feedforward binary classification model pinpoints possible residues prone to future mutations that have implications for enhanced fusogenicity and pathogenicity of the virus. We believe such MRF analyses followed by a statistical mechanics augmented ML approach could help track different evolutionary stages of such species and identify a critical evolutionary transition that is alarming.

Sangeet Satyam, Sarkar Raju, Mohanty Saswat K, Roy Susmita

2022-Sep-30

General General

Development of a deep learning-based quantitative structure-activity relationship model to identify potential inhibitors against the 3C-like protease of SARS-CoV-2.

In Future medicinal chemistry ; h5-index 38.0

Background: In the recent COVID-19 pandemic, SARS-CoV-2 infection spread worldwide. The 3C-like protease (3CLpro) is a promising drug target for SARS-CoV-2. Results: We constructed a deep learning-based convolutional neural network-quantitative structure-activity relationship (CNN-QSAR) model and deployed it on various databases to predict the biological activity of 3CLpro inhibitors. Subsequently, molecular docking analysis, molecular dynamics simulations and binding free energy calculations were performed to validate the predicted inhibitory activity against 3CLpro of SARS-CoV-2. The model showed mean squared error = 0.114, mean absolute error = 0.24 and predicted R2 = 0.84 for the test dataset. Diosmin showed good binding affinity and stability over the course of the simulations. Conclusion: The results suggest that the proposed CNN-QSAR model can be an efficient method for hit prediction and a new way to identify hit compounds against 3CLpro of SARS-CoV-2.

Kumari Madhulata, Subbarao Naidu

2022-Sep-30

3CLpro, COVID-19, QSAR, SARS-CoV, SARS-CoV-2, convolutional neural network, deep learning, dynamic cross-correlation matrices, free energy landscape, principal component analysis

Surgery Surgery

AI in Health science: A Perspective.

In Current pharmaceutical biotechnology

By helping practitioners understand complicated and varied types of data, Artificial Intelligence (AI) has influenced medical practice deeply. It is the use of a computer to mimic intelligent behaviour. Many medical professions, particularly those reliant on imaging or surgery, are progressively developing AI. While AI cognitive component outperforms human intellect, it lacks awareness, emotions, intuition, and adaptability. With minimum human participation, AI is quickly growing in healthcare, and numerous AI applications have been created to address current issues. This article explains AI, its various elements and how to utilize them in healthcare. It also offers practical suggestions for developing an AI strategy to assist the digital healthcare transition.

Mishra Raghav, Chaudhary Kajal, Mishra Isha

2022-Sep-29

Applications, Artificial Intelligence, COVID-19, Cancer, Deep Learning, Healthcare, Machine Learning

General General

COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

In Contrast media & molecular imaging

Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

Abdulaal Mohammed J, Mehedi Ibrahim M, Abusorrah Abdullah M, Aljohani Abdulah Jeza, Milyani Ahmad H, Rana Md Masud, Mahmoud Mohamed

2022