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Public Health Public Health

Natural Language Processing for Smart Healthcare.

In IEEE reviews in biomedical engineering

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We first elaborate on different NLP approaches and the NLP pipeline for smart healthcare from the technical point of view. Then, in the context of smart healthcare applications employing NLP techniques, we introduce representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss two specific medical issues, i.e., the coronavirus disease 2019 (COVID-19) pandemic and mental health, in which NLP-driven smart healthcare plays an important role. Finally, we discuss the limitations of current works and identify the directions for future works.

Zhou Binggui, Yang Guanghua, Shi Zheng, Ma Shaodan

2022-Sep-28

Surgery Surgery

COVID-19 and public support for autonomous technologies-Did the pandemic catalyze a world of robots?

In PloS one ; h5-index 176.0

By introducing a novel risk to human interaction, COVID-19 may have galvanized interest in uses of artificial intelligence (AI). But was the pandemic a large enough catalyst to change public attitudes about the costs and benefits of autonomous systems whose operations increasingly rely on AI? To answer this question, we use a preregistered research design that exploits variation across the 2018 and 2020 waves of the CCES/CES, a nationally representative survey of adults in the United States. We compare support for autonomous cars, autonomous surgeries, weapons, and cyber defense pre- and post-the beginning of the COVID-19 pandemic. We find that, despite the incentives created by COVID-19, the pandemic did not increase support for most of these technologies, except in the case of autonomous surgery among those who know someone who died of COVID-19. The results hold even when controlling for a variety of relevant political and demographic factors. The pandemic did little to push potential autonomous vehicle users to support adoption. Further, American concerns about autonomous weapons, including cyber defense, remain sticky and perhaps exacerbated over the last two years. These findings suggest that the relationship between the COVID-19 pandemic and the adoption of many of these systems is far more nuanced and complex than headlines may suggest.

Horowitz Michael C, Kahn Lauren, Macdonald Julia, Schneider Jacquelyn

2022

General General

Machine learning and artificial intelligence: applications in healthcare epidemiology.

In Antimicrobial stewardship & healthcare epidemiology : ASHE

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

Hamilton Alisa J, Strauss Alexandra T, Martinez Diego A, Hinson Jeremiah S, Levin Scott, Lin Gary, Klein Eili Y

2021

Dermatology Dermatology

Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms.

In Communications medicine

Background : Short-term prediction of COVID-19 epidemics is crucial to decision making. We aimed to develop supervised machine-learning algorithms on multiple digital metrics including symptom search trends, population mobility, and vaccination coverage to predict local-level COVID-19 growth rates in the UK.

Methods : Using dynamic supervised machine-learning algorithms based on log-linear regression, we explored optimal models for 1-week, 2-week, and 3-week ahead prediction of COVID-19 growth rate at lower tier local authority level over time. Model performance was assessed by calculating mean squared error (MSE) of prospective prediction, and naïve model and fixed-predictors model were used as reference models. We assessed real-time model performance for eight five-weeks-apart checkpoints between 1st March and 14th November 2021. We developed an online application (COVIDPredLTLA) that visualised the real-time predictions for the present week, and the next one and two weeks.

Results : Here we show that the median MSEs of the optimal models for 1-week, 2-week, and 3-week ahead prediction are 0.12 (IQR: 0.08-0.22), 0.29 (0.19-0.38), and 0.37 (0.25-0.47), respectively. Compared with naïve models, the optimal models maintain increased accuracy (reducing MSE by a range of 21-35%), including May-June 2021 when the delta variant spread across the UK. Compared with the fixed-predictors model, the advantage of dynamic models is observed after several iterations of update.

Conclusions : With flexible data-driven predictors selection process, our dynamic modelling framework shows promises in predicting short-term changes in COVID-19 cases. The online application (COVIDPredLTLA) could assist decision-making for control measures and planning of healthcare capacity in future epidemic growths.

Wang Xin, Dong Yijia, Thompson William David, Nair Harish, Li You

2022

Disease prevention, Epidemiology

Public Health Public Health

Excess diabetes mellitus-related deaths during the COVID-19 pandemic in the United States.

In EClinicalMedicine

Background : Diabetes mellitus (DM) is a critical risk factor for severe SARS-CoV-2 infection, and SARS-CoV-2 infection contributes to worsening glycemic control. The COVID-19 pandemic profoundly disrupted the delivery of care for patients with diabetes. We aimed to determine the trend of DM-related deaths during the pandemic.

Methods : In this serial population-based study between January 1, 2006 and December 31, 2021, mortality data of decedents aged ≥25 years from the National Vital Statistics System dataset was analyzed. Decedents with DM as the underlying or contributing cause of death on the death certificate were defined as DM-related deaths. Excess deaths were estimated by comparing observed versus expected age-standardized mortality rates derived from mortality during 2006-2019 with linear and polynomial regression models. The trends of mortality were quantified with joinpoint regression analysis. Subgroup analyses were performed by age, sex, race/ethnicity, and state.

Findings : Among 4·25 million DM-related deaths during 2006-2021, there was a significant surge of more than 30% in mortality during the pandemic, from 106·8 (per 100,000 persons) in 2019 to 144·1 in 2020 and 148·3 in 2021. Adults aged 25-44 years had the most pronounced rise in mortality. Widened racial/ethnic disparity was observed, with Hispanics demonstrating the highest excess deaths (67·5%; 95% CI 60·9-74·7%), almost three times that of non-Hispanic whites (23·9%; 95% CI 21·2-26·7%).

Interpretation : The United States saw an increase in DM-related mortality during the pandemic. The disproportionate rise in young adults and the widened racial/ethnic disparity warrant urgent preventative interventions from diverse stakeholders.

Funding : National Natural Science Foundation of China.

Lv Fan, Gao Xu, Huang Amy Huaishiuan, Zu Jian, He Xinyuan, Sun Xiaodan, Liu Jinli, Gao Ning, Jiao Yang, Keane Margaret G, Zhang Lei, Yeo Yee Hui, Wang Youfa, Ji Fanpu

2022-Dec

Disparity, Epidemiology, Mortality, Predictive analysis, Temporal trend

Public Health Public Health

The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days.

In BMJ global health

BACKGROUND : We examined the human toll and subsequent humanitarian crisis resulting from the Russian invasion of Ukraine, which began on 24 February 2022.

METHOD : We extracted and analysed data resulting from Russian military attacks on Ukrainians between 24 February and 4 August 2022. The data tracked direct deaths and injuries, damage to healthcare infrastructure and the impact on health, the destruction of residences, infrastructure, communication systems, and utility services - all of which disrupted the lives of Ukrainians.

RESULTS : As of 4 August 2022, 5552 civilians were killed outright and 8513 injured in Ukraine as a result of Russian attacks. Local officials estimate as many as 24 328 people were also killed in mass atrocities, with Mariupol being the largest (n=22 000) such example. Aside from wide swaths of homes, schools, roads, and bridges destroyed, hospitals and health facilities from 21 cities across Ukraine came under attack. The disruption to water, gas, electricity, and internet services also extended to affect supplies of medications and other supplies owing to destroyed facilities or production that ceased due to the war. The data also show that Ukraine saw an increase in cases of HIV/AIDS, tuberculosis, and Coronavirus (COVID-19).

CONCLUSIONS : The 2022 Russia-Ukraine War not only resulted in deaths and injuries but also impacted the lives and safety of Ukrainians through destruction of healthcare facilities and disrupted delivery of healthcare and supplies. The war is an ongoing humanitarian crisis given the continuing destruction of infrastructure and services that directly impact the well-being of human lives. The devastation, trauma and human cost of war will impact generations of Ukrainians to come.

Haque Ubydul, Naeem Amna, Wang Shanshan, Espinoza Juan, Holovanova Irina, Gutor Taras, Bazyka Dimitry, Galindo Rebeca, Sharma Sadikshya, Kaidashev Igor P, Chumachenko Dmytro, Linnikov Svyatoslav, Annan Esther, Lubinda Jailos, Korol Natalya, Bazyka Kostyantyn, Zhyvotovska Liliia, Zimenkovsky Andriy, Nguyen Uyen-Sa D T

2022-Sep

Child health, Epidemiology, Health systems, Mental Health & Psychiatry, Public Health

Public Health Public Health

An AI-driven Digital Health solution to support clinical management of long COVID patients: prospective multicenter observational study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : COVID-19 pandemic has evidenced the weaknesses of most health systems around the world, collapsing them, and depleting their available healthcare resources. Fortunately, the development and enforcement of specific public health policies such as vaccination, mask wearing and social distancing, among others, has made possible to reduce the prevalence and complications associated with COVID-19 in the acute phase. However, the aftermath of the global pandemic leads us to find an efficient approach to manage patients with long COVID. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted healthcare systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research towards the implementation of an AI-driven Digital Health solution that supports both the adaptive self-management of people living with long COVID and the healthcare staff in charge of the management and follow-up of this population.

OBJECTIVE : The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort.

METHODS : Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID. Furthermore, a prospective patient-generated dataset will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the FAIR (Findability, Accessibility, Interoperability, and Reuse) Guiding Principles for scientific data management and stewardship will be applied to the resulting dataset to encourage the continuous process of discovery, evaluation and reuse of information for the research community at large.

RESULTS : The SENSING-AI cohort is expected to be completed in early 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental wellbeing techniques to improve patients' self-management while providing useful and timely clinical decision support services to healthcare professionals based on risk stratification models and early detection of exacerbations.

CONCLUSIONS : SENSING-AI focuses on obtaining high quality data of long COVID patients' during their daily life. Supporting long-covid patients is of paramount importance on the current pandemic situation, including supporting their healthcare professionals in a cost-effective and efficient management of long COVID cases.

CLINICALTRIAL : Registered at clinicaltrials.gov. The NCT identifier (NCT05204615) for this prospective study.

INTERNATIONAL REGISTERED REPORT : DERR1-10.2196/37704.

Fuster-Casanovas Aïna, Fernandez-Luque Luis, Nuñez-Benjumea Francisco J, Moreno Conde Alberto, Luque-Romero Luis G, Bilionis Ioannis, Rubio Escudero Cristina, Chicchi Giglioli Irene Alice, Vidal-Alaball Josep

2022-Aug-29

General General

Longitudinal changes in mental health among medical students in China during the COVID-19 epidemic: depression, anxiety and stress at 1-year follow -up.

In Psychology, health & medicine

This study aimed to evaluate the influence of COVID-19 on the mental health of Chinese medical students at 1-year of follow-up. From 2 February 2020 to 23 February 2021, we conducted three waves of research online (T1 = during outbreak, T2 = controlling period, T3 = 1 year after outbreak). The survey collected demographic data and several self reporting questionnaires to measure the depressive, anxiety and stress symptoms. A total of 4002 participants complete the whole research phases. The study major, grade level and gender were the main factors related to psychological distress caused by the COVID-19 crisis. Importantly, medical knowledge has a protective effect on medical students' psychological distress during the COVID-19 period.

Zhang Lei, Du Jinmei, Chen Tingting, Sheng Rongrong, Ma Juncheng, Ji Gongjun, Yu Fengqiong, Ye Jianguo, Li Dandan, Li Zhenjing, Zhu Chunyan, Wang Kai

2022-Sep-27

1-year longitudinal study, COVID-19, China, medical students, psychological distress

General General

Application and Communication Optimization Technology of Unmanned Distribution Car under Deep Learning in Logistics Express of COVID-19.

In Computational intelligence and neuroscience

This work aims to solve the problem that the daily necessities of urban residents cannot be delivered during coronavirus disease 2019 (COVID-19), thereby reducing the possibility of the delivery personnel contracting COVID-19 due to the need to transport medicines to the hospital during the epidemic. Firstly, this work studies the application and communication optimization technology of unmanned delivery cars based on deep learning (DL) under COVID-19. Secondly, a route planning method for unmanned delivery cars based on the DL method is proposed under the influence of factors such as maximum flight time, load, and road conditions. This work analyzes and introduces unmanned delivery cars from four aspects combined with the actual operation of unmanned delivery cars and related literature: the characteristics, delivery mode, economy, and limitations of unmanned delivery cars. The unmanned delivery car is in the promotion stage. A basic AVRPTW model is established that minimizes the total delivery cost without considering the charging behavior under the restriction of some routes, delivery time, load, and other factors. The path optimization problem of unmanned delivery cars in various situations is considered. A multiobjective optimization model of the unmanned delivery car in the charging/swap mode is established with the goal of minimizing the total delivery cost and maximizing customer satisfaction under the premise of meeting the car driving requirements. An improved genetic algorithm is designed to solve the established model. Finally, the model is tested, and its results are analyzed. The effectiveness of this route planning method is proved through case analysis. Customer satisfaction, delivery time, cost input, and other aspects have been greatly improved through the improvement and optimization of the unmanned delivery car line, which has been well applied in practice. In addition, unmanned delivery cars are affected by many factors such as load, and the service time required for delivery is longer. Therefore, this work chooses an unmanned distribution car with strong endurance to improve distribution efficiency. The new hospital contactless distribution mode discussed here will play an important role in promoting future development.

Song Xinyue, Luan Fengkai

2022

Ophthalmology Ophthalmology

Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images

18th International Symposium on Medical Information Processing and Analysis (SIPAIM) 2022

Automated optic disc (OD) and optic cup (OC) segmentation in fundus images is relevant to efficiently measure the vertical cup-to-disc ratio (vCDR), a biomarker commonly used in ophthalmology to determine the degree of glaucomatous optic neuropathy. In general this is solved using coarse-to-fine deep learning algorithms in which a first stage approximates the OD and a second one uses a crop of this area to predict OD/OC masks. While this approach is widely applied in the literature, there are no studies analyzing its real contribution to the results. In this paper we present a comprehensive analysis of different coarse-to-fine designs for OD/OC segmentation using 5 public databases, both from a standard segmentation perspective and for estimating the vCDR for glaucoma assessment. Our analysis shows that these algorithms not necessarily outperfom standard multi-class single-stage models, especially when these are learned from sufficiently large and diverse training sets. Furthermore, we noticed that the coarse stage achieves better OD segmentation results than the fine one, and that providing OD supervision to the second stage is essential to ensure accurate OC masks. Moreover, both the single-stage and two-stage models trained on a multi-dataset setting showed results in pair or even better than other state-of-the-art alternatives, while ranking first in REFUGE for OD/OC segmentation. Finally, we evaluated the models for vCDR prediction in comparison with six ophthalmologists on a subset of AIROGS images, to understand them in the context of inter-observer variability. We noticed that vCDR estimates recovered both from single-stage and coarse-to-fine models can obtain good glaucoma detection results even when they are not highly correlated with manual measurements from experts.

Eugenia Moris, Nicolás Dazeo, Maria Paula Albina de Rueda, Francisco Filizzola, Nicolás Iannuzzo, Danila Nejamkin, Kevin Wignall, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando

2022-09-28

Pathology Pathology

Partial annotations for the segmentation of large structures with low annotation cost

Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham

Deep learning methods have been shown to be effective for the automatic segmentation of structures and pathologies in medical imaging. However, they require large annotated datasets, whose manual segmentation is a tedious and time-consuming task, especially for large structures. We present a new method of partial annotations that uses a small set of consecutive annotated slices from each scan with an annotation effort that is equal to that of only few annotated cases. The training with partial annotations is performed by using only annotated blocks, incorporating information about slices outside the structure of interest and modifying a batch loss function to consider only the annotated slices. To facilitate training in a low data regime, we use a two-step optimization process. We tested the method with the popular soft Dice loss for the fetal body segmentation task in two MRI sequences, TRUFI and FIESTA, and compared full annotation regime to partial annotations with a similar annotation effort. For TRUFI data, the use of partial annotations yielded slightly better performance on average compared to full annotations with an increase in Dice score from 0.936 to 0.942, and a substantial decrease in Standard Deviations (STD) of Dice score by 22% and Average Symmetric Surface Distance (ASSD) by 15%. For the FIESTA sequence, partial annotations also yielded a decrease in STD of the Dice score and ASSD metrics by 27.5% and 33% respectively for in-distribution data, and a substantial improvement also in average performance on out-of-distribution data, increasing Dice score from 0.84 to 0.9 and decreasing ASSD from 7.46 to 4.01 mm. The two-step optimization process was helpful for partial annotations for both in-distribution and out-of-distribution data. The partial annotations method with the two-step optimizer is therefore recommended to improve segmentation performance under low data regime.

Bella Specktor Fadida, Daphna Link Sourani, Liat Ben Sira Elka Miller, Dafna Ben Bashat, Leo Joskowicz

2022-09-25

Public Health Public Health

Utilizing geospatial intelligence and user modeling to allow for a customized health awareness campaign during the pandemic: The case of COVID-19 in Saudi Arabia.

In Journal of infection and public health

BACKGROUND : As of 2022, people are getting better at learning how to coexist with the Covid-19 global pandemic. In Saudi Arabia, many attempts have been made to raise public health awareness. However, most health awareness campaigns are generic and might not influence the desired behavior among individuals.

OBJECTIVES : This study aims to apply geospatial intelligence and user modeling to profile the districts of the city of Jeddah. This customized map can provide a baseline for a customized health awareness campaign that targets the locals of each district individually based on the virus spread level.

METHODOLOGY : It is ongoing research, which has resulted in the creation of a health messages library in the first phase [1]. This paper focuses on a second phase of the research study, which aims to provide a customized baseline for this campaign by applying the geospatial artificial intelligence technique known as space-time cube (STC). STC was applied to create a local map of the Saudi city of Jeddah, representing three different profiles for the city's districts. The model is built using valid COVID-19 clinical data obtained from one of Jeddah's general hospitals.

RESULTS AND IMPLICATIONS : When applied, STC displays three profiles for the districts of Jeddah city: high infection, moderate infection, and low infection. To assess the geo-intelligent map, a new instrument was created and validated. The usability and practicality of this map were quantitatively evaluated in a cross-sectional survey using the goal-question-metric measurement framework, and a total of 43 participants filled out the questionnaire. The results indicate that the geo-intelligent map is suitable for everyday use, as evidenced by the participants' responses. We argue that the developed instrument can also be used to assess any geo-intelligence map. This research provides a legitimate approach to customizing health awareness messages during pandemics.

Alrige Mayda, Bitar Hind, Meccawy Maram, Mullachery Balakrishnan

2022-Sep-01

COVID-19, Customization, Geospatial intelligence, Health awareness campaign, Space-time cube (STC), User modeling

General General

Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies

ArXiv Preprint

Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units. In particular, NLC is a challenge for evaluating the effects of environmental policies and climate events on health-related outcomes such as air pollution exposure. This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interference. Then, it proposes a broadly applicable framework, termed "weather2vec", that uses the theory of balancing scores to learn representations of non-local information into a scalar or vector defined for each observational unit, which is subsequently used to adjust for confounding in conjunction with causal inference methods. The framework is evaluated in a simulation study and two case studies on air pollution where the weather is an (inherently regional) known confounder.

Mauricio Tec, James Scott, Corwin Zigler

2022-09-25

General General

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

In PloS one ; h5-index 176.0

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

González-Cebrián Alba, Borràs-Ferrís Joan, Ordovás-Baines Juan Pablo, Hermenegildo-Caudevilla Marta, Climente-Marti Mónica, Tarazona Sonia, Vitale Raffaele, Palací-López Daniel, Sierra-Sánchez Jesús Francisco, Saez de la Fuente Javier, Ferrer Alberto

2022

General General

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain.

In Biometrical journal. Biometrische Zeitschrift

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.

Rueda María Del Mar, Pasadas-Del-Amo Sara, Rodríguez Beatriz Cobo, Castro-Martín Luis, Ferri-García Ramón

2022-Sep-22

COVID-19, machine-learning techniques, nonprobability surveys, propensity score adjustment, survey sampling

General General

Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.

In Journal of imaging

In this work, the performance of five deep learning architectures in classifying COVID-19 in a multi-class set-up is evaluated. The classifiers were built on pretrained ResNet-50, ResNet-50r (with kernel size 5×5 in the first convolutional layer), DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transformer. The cross entropy and weighted cross entropy were minimised with Adam and AdamW. In total, 20 experiments were conducted with 10 repetitions and obtained the following metrics: accuracy (Acc), balanced accuracy (BA), F1 and F2 from the general Fβ macro score, Matthew's Correlation Coefficient (MCC), sensitivity (Sens) and specificity (Spec) followed by bootstrapping. The performance of the classifiers was compared by using the Friedman-Nemenyi test. The results show that less complex architectures such as ResNet-50, ResNet-50r and DenseNet-121 were able to achieve better generalization with rankings of 1.53, 1.71 and 3.05 for the Matthew Correlation Coefficient, respectively, while MobileNet-v3 and CaiT obtained rankings of 3.72 and 5.0, respectively.

Ascencio-Cabral Azucena, Reyes-Aldasoro Constantino Carlos

2022-Sep-01

COVID-19, Friedman–Nemenyi tests, bootstrap, deep neural networks, transformer, weighted cross entropy

Internal Medicine Internal Medicine

Research on Application of Meticulous Nursing Scheduling Management Based on Data-Driven Intelligent Optimization Technology.

In Computational intelligence and neuroscience

The management of nursing scheduling in healthcare facilities have faced new challenges during the COVID-19 pandemic. With the rapid development of big data and artificial intelligence technology, data-driven intelligent medical services are what we need to study nowadays. This paper not only proposes reasonable solutions in areas such as refined nursing scheduling by using these scientific technologies to quickly realize the allocation of human resources in hospitals. It also accelerates the development of hospital informatization construction through computer technology, establishing a scientific and intelligent medical platform that meets the needs of users. Aiming at the problem of nursing scheduling in medical service data research, this paper proposes a complete plan by analyzing the development of the medical platform at this stage. Firstly, established an intelligent medical service platform, and studied the medical management from the perspective of data. Then, analyze the intelligent medical platform data by utilizing optimized algorithms, through reasonable analysis under various constraints, to get the basic nursing scheduling plan that meets the needs of medical institutions. Finally, considering the actual situation of emergency medical treatment, the decision classification model is introduced under the basic scheme to further screen out the optimal management scheme of modern medical treatment.

Zhai YanPing, Li Run, Yan ZhiLi

2022

Public Health Public Health

Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States.

In Open forum infectious diseases

A tree model identified adults age ≤34 years, Johnson & Johnson primary series recipients, people from racial/ethnic minority groups, residents of nonlarge metro areas, and those living in socially vulnerable communities in the South as less likely to be boosted. These findings can guide clinical/public health outreach toward specific subpopulations.

Meng Lu, Fast Hannah E, Saelee Ryan, Zell Elizabeth, Murthy Bhavini Patel, Murthy Neil Chandra, Lu Peng-Jun, Shaw Lauren, Harris LaTreace, Gibbs-Scharf Lynn, Chorba Terence

2022-Sep

COVID-19, COVID-19 vaccination, booster dose, coronavirus

Public Health Public Health

Facebook Intervention to Connect Alaska Native People with Resources and Support to Quit Smoking: CAN Quit Pilot Randomized Controlled Trial.

In Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco

INTRODUCTION : There is some evidence that social media interventions can promote smoking cessation. This randomized controlled pilot study is the first to evaluate the feasibility and potential efficacy of a Facebook smoking cessation intervention among Alaska Native adults.

METHODS : Recruitment and data collection occurred December 2019-March 2021. Participants were recruited statewide in Alaska using Facebook advertisements with a targeted sample of 60 enrolled. Participants were stratified by gender, age, and rural/urban residence and randomly assigned to receive referral resources on evidence-based cessation treatments (EBCTs) (control, n=30) or these resources plus a three-month, closed/private, culturally tailored, Facebook group (intervention, n=31) that connected participants to EBCT resources and was moderated by two Alaska Native Trained Tobacco Specialists. Assessments were conducted online post-randomization at one, three, and six months. Outcomes were feasibility (recruitment, retention, intervention engagement), self-reported use of EBCTs, and biochemically confirmed seven-day point-prevalence smoking abstinence.

RESULTS : Of intervention participants, 90% engaged (e.g., posted, commented) more than once. Study retention was 57% at six months (no group differences). The proportion utilizing EBCTs was about double for intervention compared with the control group participants at three and six months. Smoking abstinence was higher for intervention than control participants at three months (6.5% vs. 0%, p=0.16) but comparable at six months (6.4% vs. 6.7%, p=0.97).

CONCLUSIONS : While additional research is needed to promote long-term cessation, this pilot trial supports recruitment feasibility during the COVID-19 pandemic, consumer uptake, and a signal for intervention efficacy on the uptake of cessation treatment and short-term smoking abstinence.

IMPLICATIONS : This study is the first evaluation of a social media intervention for smoking cessation among Indigenous people. We learned that statewide Facebook recruitment of Alaska Native adults who smoke was feasible and there was a signal for the efficacy of a Facebook intervention on the uptake of evidence-based cessation treatment and short-term (three months) biochemically verified smoking abstinence. Clinically, social media platforms may complement current care models by connecting Alaska Native individuals and others living in hard-to-reach communities to cessation treatment resources.

Patten Christi A, Koller Kathryn R, Sinicrope Pamela S, Prochaska Judith J, Young Colleen, Resnicow Kenneth, Decker Paul A, Hughes Christine A, Merritt Zoe T, McConnell Clara R, Huang Ming, Thomas Timothy K

2022-Sep-20

Alaska Native people, Facebook, disparities, intervention, smoking cessation, social media, treatment

General General

Predictors of COVID-19 vaccination rate in USA: A machine learning approach.

In Machine learning with applications

In this study, we examine state-level features and policies that are most important in achieving a threshold level vaccination rate to curve the effects of the COVID-19 pandemic. We employ CHAID, a decision tree algorithm, on three different model specifications to answer this question based on a dataset that includes all the states in the United States. Workplace travel emerges as the most important predictor; however, the governors' political affiliation (PA) replaces it in a more conservative feature set that includes economic features and the growth rate of COVID-19 cases. We also employ several alternative algorithms as a robustness check. Results from these checks confirm our original findings regarding workplace travels and political affiliation. The accuracy under different model specifications ranges from 80%-88%, whereas the sensitivity is between 92.5%-100%. Our findings provide actionable policy insights to increase vaccination rates and combat the COVID-19 pandemic.

Osman Syed Muhammad Ishraque, Sabit Ahmed

2022-Sep-16

COVID-19, Decision tree, Health policy, Machine learning, Vaccination, Vaccine hesitancy

General General

A computational approach to biological pathogenicity.

In Molecular genetics and genomics : MGG

The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.

Garzon Max, Mainali Sambriddhi, Chacon Maria Fernanda, Azizzadeh-Roodpish Shima

2022-Sep-20

Digital genomic signature, Gibbs energy, Hybridization, Machine learning, Pathogenic relationship, Pathogens/nonpathogens, h-distance

General General

virDTL: Viral Recombination Analysis Through Phylogenetic Reconciliation and Its Application to Sarbecoviruses and SARS-CoV-2.

In Journal of computational biology : a journal of computational molecular cell biology

An accurate understanding of the evolutionary history of rapidly-evolving viruses like SARS-CoV-2, responsible for the COVID-19 pandemic, is crucial to tracking and preventing the spread of emerging pathogens. However, viruses undergo frequent recombination, which makes it difficult to trace their evolutionary history using traditional phylogenetic methods. In this study, we present a phylogenetic workflow, virDTL, for analyzing viral evolution in the presence of recombination. Our approach leverages reconciliation methods developed for inferring horizontal gene transfer in prokaryotes and, compared to existing tools, is uniquely able to identify ancestral recombinations while accounting for several sources of inference uncertainty, including in the construction of a strain tree, estimation and rooting of gene family trees, and reconciliation itself. We apply this workflow to the Sarbecovirus subgenus and demonstrate how a principled analysis of predicted recombination gives insight into the evolution of SARS-CoV-2. In addition to providing confirming evidence for the horseshoe bat as its zoonotic origin, we identify several ancestral recombination events that merit further study.

Zaman Sumaira, Sledzieski Samuel, Berger Bonnie, Wu Yi-Chieh, Bansal Mukul S

2022-Sep-20

SARS-CoV-2, Sarbecovirus evolution, phylogenetic reconciliation, viral recombination

General General

Insights into performance evaluation of compound-protein interaction prediction methods.

In Bioinformatics (Oxford, England)

MOTIVATION : Machine-learning-based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent improvements in predictive accuracy, we have observed a number of fundamental issues in experiment design that produce overoptimistic estimates of model performance.

RESULTS : We systematically analyze the impact of several factors affecting generalization performance of CPI predictors that are overlooked in existing work: (i) similarity between training and test examples in cross-validation; (ii) synthesizing negative examples in absence of experimentally verified negative examples and (iii) alignment of evaluation protocol and performance metrics with real-world use of CPI predictors in screening large compound libraries. Using both state-of-the-art approaches by other researchers as well as a simple kernel-based baseline, we have found that effective assessment of generalization performance of CPI predictors requires careful control over similarity between training and test examples. We show that, under stringent performance assessment protocols, a simple kernel-based approach can exceed the predictive performance of existing state-of-the-art methods. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization in comparison to more sophisticated strategies used in existing studies. Our analyses indicate that using proposed experiment design strategies can offer significant improvements for CPI prediction leading to effective target compound screening for drug repurposing and discovery of putative chemical ligands of SARS-CoV-2-Spike and Human-ACE2 proteins.

AVAILABILITY AND IMPLEMENTATION : Code and supplementary material available at https://github.com/adibayaseen/HKRCPI.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Yaseen Adiba, Amin Imran, Akhter Naeem, Ben-Hur Asa, Minhas Fayyaz

2022-Sep-16

Pathology Pathology

CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks.

In Briefings in bioinformatics

A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments.

Leung Ricky Wai Tak, Jiang Xiaosen, Zong Xueqing, Zhang Yanhong, Hu Xinlin, Hu Yaohua, Qin Jing

2022-Sep-17

conditions, drugs, regulatory networks, small molecules, transcriptional control

General General

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

In The Analyst

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degradation make them huge sources of micro- and nanoplastic particles. To better understand the consequences of the direct influence of microplastic pollution on biota, there is an urgent need to develop a reliable and high-throughput analytical tool for sub-micrometre plastic identification and visualisation in environmental and biological samples. This study evaluated the application of a combined technique based on dark-field enhanced microscopy and hyperspectral imaging augmented with deep learning data analysis for the visualisation, detection and identification of microplastic particles released from commercially available medical masks after 192 hours of UV-C irradiation. The analysis was performed using a separated blue-coloured spunbond outer layer and white-coloured meltblown interlayer that allowed us to assess the influence of the structure and pigmentation of intact and UV-exposed samples on classification performance. Microscopy revealed strong fragmentation of both layers and the formation of microparticles and fibres of various shapes after UV exposure. Based on the spectral signatures of both layers, it was possible to identify intact materials using a convolutional neural network successfully. However, the further classification of UV-exposed samples demonstrated that the spectral characteristics of samples in the visible to near-infrared range are disrupted, causing a decreased performance of the CNN. Despite this, the application of a deep learning algorithm in hyperspectral analysis outperformed the conventional spectral angle mapper technique in classifying both intact and UV-exposed samples, confirming the potential of the proposed approach in secondary microplastic analysis.

Ishmukhametov Ilnur, Batasheva Svetlana, Fakhrullin Rawil

2022-Sep-20

General General

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

In Patterns (New York, N.Y.)

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Madan Sumit, Demina Victoria, Stapf Marcus, Ernst Oliver, Fröhlich Holger

2022-Sep-09

John Cunningham polyomavirus major capsid protein VP1, SARS-CoV-2 spike glycoprotein, Siamese neural network, deep protein sequence embeddings, protein-protein interactions, virus-host interactions

Public Health Public Health

Designing optimal convolutional neural network architecture using differential evolution algorithm.

In Patterns (New York, N.Y.)

Convolutional neural networks (CNNs) are deep learning models used widely for solving various tasks like computer vision and speech recognition. CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time consuming, and challenging. To solve these, our study develops an improved differential evolution of convolutional neural network (IDECNN) algorithm to design CNN layer architectures for image classification. Variable-length encoding is utilized to represent the flexible layer architecture of a CNN model in IDECNN. An efficient heuristic mechanism is proposed in IDECNN to evolve CNN architecture through mutation and crossover to prevent premature convergence during the evolutionary process. Eight well-known imaging datasets were utilized. The results showed that IDECNN could design suitable architecture compared with 20 existing CNN models. Finally, CNN architectures are applied to pneumonia and coronavirus disease 2019 (COVID-19) X-ray biomedical image data. The results demonstrated the usefulness of the proposed approach to generate a suitable CNN model.

Ghosh Arjun, Jana Nanda Dulal, Mallik Saurav, Zhao Zhongming

2022-Sep-09

CNN, DE, NAS, convolutional neural network, differential evolution, image classification, neural architecture search, neuroevolution, optimal neural architecture

General General

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations.

In Annals of operations research

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

Sariyer Görkem, Ataman Mustafa Gokalp, Mangla Sachin Kumar, Kazancoglu Yigit, Dora Manoj

2022-Sep-15

Big data analytics, COVID-19, Emergency department, Machine learning, Sustainable operations

Internal Medicine Internal Medicine

Identification of High Death Risk Coronavirus Disease-19 Patients using Blood Tests.

In Advanced biomedical research

Background : The coronavirus disease (COVID-19) pandemic has made a great impact on health-care services. The prognosis of the severity of the disease help reduces mortality by prioritizing the allocation of hospital resources. Early mortality prediction of this disease through paramount biomarkers is the main aim of this study.

Materials and Methods : In this retrospective study, a total of 205 confirmed COVID-19 patients hospitalized from June 2020 to March 2021 were included. Demographic data, important blood biomarkers levels, and patient outcomes were investigated using the machine learning and statistical tools.

Results : Random forests, as the best model of mortality prediction, (Matthews correlation coefficient = 0.514), were employed to find the most relevant dataset feature associated with mortality. Aspartate aminotransferase (AST) and blood urea nitrogen (BUN) were identified as important death-related features. The decision tree method was identified the cutoff value of BUN >47 mg/dL and AST >44 U/L as decision boundaries of mortality (sensitivity = 0.4). Data mining results were compared with those obtained through the statistical tests. Statistical analyses were also determined these two factors as the most significant ones with P values of 4.4 × 10-7 and 1.6 × 10-6, respectively. The demographic trait of age and some hematological (thrombocytopenia, increased white blood cell count, neutrophils [%], RDW-CV and RDW-SD), and blood serum changes (increased creatinine, potassium, and alanine aminotransferase) were also specified as mortality-related features (P < 0.05).

Conclusions : These results could be useful to physicians for the timely detection of COVID-19 patients with a higher risk of mortality and better management of hospital resources.

Zadeh Hosseingholi Elaheh, Maddahi Saeede, Jabbari Sajjad, Molavi Ghader

2022

Aspartate aminotransferases, blood urea nitrogen, coronavirus disease-19, machine learning, prognosis

General General

A Novel COVID-19 Detection Model Based on DCGAN and Deep Transfer Learning.

In Procedia computer science

A continuing outbreak of pneumonia-related disease novel, Coronavirus has been recorded worldwide and has become a global health problem. This research aims to generate a constructive training data set for a neural network to detect COVID-19 from X-ray images. The creation of medical images is an issue in the field of deep learning. Medical image datasets are frequently unbalanced; using such datasets to train a deep neural network model to correctly classify medical conditions typically leads to over-fitting the data on majority class samples. Data augmentation is commonly used in training data to expand the dataset. Data augmentation may not be beneficial in medical domains with limited data. This paper proposed a data generation model using a Deep Convolutional Generative adversarial network (DCGAN), which generates fake instances with comparable properties to the original data. The model's Fréchet Distance of Inception (FID) was 23.78, close to the original data. Deep transfer learning-based models VGG-16, Inceptionv3 and MobilNet, were chosen as the backbone for COVID-19 detection. The present study aims to increase the dataset using the DCGAN data augmentation technique to improve classifier performance.

Puttagunta Muralikrishna, Subban Ravi, C Nelson Kennedy Babu

2022

COVID-19, Classification, Deep Transfer Learning, Generative Adversarial Networks

General General

Impact of Covid-19 on the Hospitality Industry and Responding to Future Pandemic through Technological Innovation.

In Procedia computer science

Covid-19 pandemic has severely affected the human lives and businesses around the world. Globally, the demand for hospitality services is at an all-time low due to borders closing and restricted movement in various countries. This article highlights the impact of Covid-19 on the hospitality industry, mainly hotels and restaurants. It further discusses ICT (Information and Communication Technology) and machine learning-based solutions for the current and future pandemics. The study has used the exploratory research method. It has referred to the existing theoretical and empirical findings in the hospitality establishment with regard to the Covid-19 impact.

Alotaibi Eid, Khan Asharul

2022

Coronavirus impact, Covid-19 impact, Internet of Things, artificial intellignce, hospitality industry, hotel industry, machine learning

Public Health Public Health

Artificial Intelligence Approaches on X-ray-oriented Images Process for Early Detection of COVID-19.

In Journal of medical signals and sensors

Background : COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease.

Methods : A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020.

Results : We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%).

Conclusion : This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Rezayi Sorayya, Ghazisaeedi Marjan, Kalhori Sharareh Rostam Niakan, Saeedi Soheila

2019-nCoV disease, X-ray images, artificial intelligence, computed tomography, deep learning, image processing

General General

School Virus Infection Simulator for customizing school schedules during COVID-19.

In Informatics in medicine unlocked

Even as the COVID-19 pandemic raged worldwide, schools strived to provide consistent education to their students. In such situations, schools require customized schedules that can address the health concerns and safety of the students to safely reopen and remain open. School schedules can be customized in many ways, and different approaches' impact on education and effectiveness in reducing infectious risks are different. To address this issue, we developed the School Virus Infection Simulation-Model (SVISM) for teachers and education policymakers. By taking into account the students' lesson schedules, classroom volume, air circulation rates in the classrooms, and infectability of the students, SVISM simulates the spread of infection at a school. We demonstrate the impact of several school schedules in self-contained and departmentalized classrooms and evaluate them in terms of the maximum number of students infected simultaneously, and the percentage of face-to-face lessons. The results show that the impact of increasing the classroom ventilation rate is not as stable as that of customizing school schedules. In addition, school schedules can differently impact the maximum number of students infected simultaneously, depending on whether classrooms are self-contained or departmentalized. We found that the maximum number of students infected simultaneously under a certain schedule with 50 percentage of face-to-face lessons in self-contained classrooms is higher than the maximum number of students infected simultaneously having schedules with a higher percentage of face-to-face lessons; this phenomenon was not found in departmentalized classrooms. These results show that the SVISM can help teachers and education policymakers plan school schedules appropriately to reduce the maximum number of students infected simultaneously, while also maintaining a certain rate of face-to-face lessons.

Takahashi Satoshi, Kitazawa Masaki, Yoshikawa Atsushi

2022-Sep-13

COVID-19, Departmentalized classroom, Hybrid learning, School scheduling, Self-contained classroom, Virus infection

General General

Technostress causes cognitive overload in high-stress people: Eye tracking analysis in a virtual kiosk test.

In Information processing & management

In the midst of the COVID-19 pandemic, the use of non-face-to-face information and communication technology (ICT) such as kiosks has increased. While kiosks are useful overall, those who do not adapt well to these technologies experience technostress. The two most serious technostressors are inclusion and overload issues, which indicate a sense of inferiority due to a perceived inability to use ICT well and a sense of being overwhelmed by too much information, respectively. This study investigated the different effects of hybrid technostress-induced by both inclusion and overload issues-on the cognitive load among low-stress and high-stress people when using kiosks to complete daily life tasks. We developed a 'virtual kiosk test' to evaluate participants' cognitive load with eye tracking features and performance features when ordering burgers, sides, and drinks using the kiosk. Twelve low-stress participants and 13 high-stress participants performed the virtual kiosk test. As a result, regarding eye tracking features, high-stress participants generated a larger number of blinks, a longer scanpath length, a more distracted heatmap, and a more complex gaze plot than low-stress participants. Regarding performance features, high-stress participants took significantly longer to order and made more errors than low-stress participants. A support-vector machine (SVM) using both eye tracking features (i.e., number of blinks, scanpath length) and a performance feature (i.e., time to completion) best differentiated between low-stress and high-stress participants (89% accuracy, 100% sensitivity, 83.3% specificity, 75% precision, 85.7% F1 score). Overall, under technostress, high-stress participants experienced cognitive overload and consequently decreased performance; whereas, low-stress participants felt moderate arousal and improved performance. These varying effects of technostress can be interpreted through the Yerkes-Dodson law. Based on our findings, we proposed an adaptive interface, multimodal interaction, and virtual reality training as three implications for technostress relief in non-face-to-face ICT.

Kim Se Young, Park Hahyeon, Kim Hongbum, Kim Joon, Seo Kyoungwon

2022-Nov

Cognitive load, Eye tracking, Kiosk, Technostress, Virtual reality

General General

Endoscopic capsule robot-based diagnosis, navigation and localization in the gastrointestinal tract.

In Frontiers in robotics and AI

The proliferation of video capsule endoscopy (VCE) would not have been possible without continued technological improvements in imaging and locomotion. Advancements in imaging include both software and hardware improvements but perhaps the greatest software advancement in imaging comes in the form of artificial intelligence (AI). Current research into AI in VCE includes the diagnosis of tumors, gastrointestinal bleeding, Crohn's disease, and celiac disease. Other advancements have focused on the improvement of both camera technologies and alternative forms of imaging. Comparatively, advancements in locomotion have just started to approach clinical use and include onboard controlled locomotion, which involves miniaturizing a motor to incorporate into the video capsule, and externally controlled locomotion, which involves using an outside power source to maneuver the capsule itself. Advancements in locomotion hold promise to remove one of the major disadvantages of VCE, namely, its inability to obtain targeted diagnoses. Active capsule control could in turn unlock additional diagnostic and therapeutic potential, such as the ability to obtain targeted tissue biopsies or drug delivery. With both advancements in imaging and locomotion has come a corresponding need to be better able to process generated images and localize the capsule's position within the gastrointestinal tract. Technological advancements in computation performance have led to improvements in image compression and transfer, as well as advancements in sensor detection and alternative methods of capsule localization. Together, these advancements have led to the expansion of VCE across a number of indications, including the evaluation of esophageal and colon pathologies including esophagitis, esophageal varices, Crohn's disease, and polyps after incomplete colonoscopy. Current research has also suggested a role for VCE in acute gastrointestinal bleeding throughout the gastrointestinal tract, as well as in urgent settings such as the emergency department, and in resource-constrained settings, such as during the COVID-19 pandemic. VCE has solidified its role in the evaluation of small bowel bleeding and earned an important place in the practicing gastroenterologist's armamentarium. In the next few decades, further improvements in imaging and locomotion promise to open up even more clinical roles for the video capsule as a tool for non-invasive diagnosis of lumenal gastrointestinal pathologies.

Hanscom Mark, Cave David R

2022

AI, artificial intelligence, capsule, capsule endoscopy, capsule locomotion, gastrointestinal tract

General General

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.

In Computers & electrical engineering : an international journal

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Kumar Santosh, Nagar Rishab, Bhatnagar Saumya, Vaddi Ramesh, Gupta Sachin Kumar, Rashid Mamoon, Bashir Ali Kashif, Alkhalifah Tamim

2022-Sep-14

COVID-19, Chest X-ray, Cough-breathing sounds, Deep learning, Multimodal, Segmentation

General General

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.

In Frontiers in immunology ; h5-index 100.0

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Lu Lu, Liu Le-Ping, Gui Rong, Dong Hang, Su Yan-Rong, Zhou Xiong-Hui, Liu Feng-Xia

2022

COVID-19, differentially expressed gene (DEG), drug molecule, functional enrichment, gene ontology, hub gene, protein–protein interaction (PPI), sepsis

General General

Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.

In Royal Society open science

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

Heltberg Mathias L, Michelsen Christian, Martiny Emil S, Christensen Lasse Engbo, Jensen Mogens H, Halasa Tariq, Petersen Troels C

2022-Sep

COVID-19, agent-based modelling, fitting, pandemics, spatial heterogenity

General General

Temporal trends in health worker social media communication during the COVID-19 pandemic.

In Research in nursing & health

During the COVID-19 pandemic, healthcare professionals are exposed to extreme hazards and workplace stressors. Social media postings by physicians and nurses related to COVID-19 from January 21 to June 1, 2020 were obtained from the Reddit website. Topic modeling via Latent Dirichlet Allocation (LDA) using a machine-learning approach was performed on 1723 documents, each posted in a unique Reddit discussion. We selected the optimal number of topics using a heuristic approach based on examination of the rate of perplexity change (RPC) across LDA models. A two-step multiple linear regression was done to identify differences across time and between nurses versus physicians. Prevalent topics included excessive workload, positive emotional expression and collegial support, anger and frustration, testing positive for COVID-19 and treatment, use of personal protective equipment, impacts on healthcare jobs, disruption of medical procedures, and general healthcare issues. Nurses' posts initially reflected concern about workload, personal danger, safety precautions, and emotional support to their colleagues. Physicians posted initially more often than nurses about technical aspects of the coronavirus disease, medical equipment, and treatment. Differences narrowed over time: nurses increasingly made technical posts, while physicians' posts increasingly were in the personal domain, suggesting a convergence of the professions over time.

Ford Julian D, Marengo Davide, Olff Miranda, Armour Cherie, Elhai Jon D, Almquist Zack, Spiro Emma S

2022-Sep-19

COVID-19, nurses, physicians, social media, temporal trends

General General

An AI based digital-twin for prioritising pneumonia patient treatment.

In Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive care and ventilator support. The deep learning methods are used to build patient-specific digital-twins to identify and prioritise critical cases amongst severe pneumonia patients. The three-tiered strategy is proposed to generate severity indices to: (1) identify urgent cases, (2) assign critical care and mechanical ventilation, and (3) discontinue mechanical ventilation and critical care at the optimal time. The severity indices calculated in the present study are the probability of death and the probability of requiring mechanical ventilation. These enable the generation of patient prioritisation lists and facilitates the smooth flow of patients in and out of Intensive Therapy Units (ITUs). The proposed digital-twin is built on pre-trained deep learning models using data from more than 1895 pneumonia patients. The severity indices calculated in the present study are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89. This model may be employed in its current form to COVID-19 patients, but transfer learning with COVID-19 patient data will improve the predictions. The digital-twin model developed and tested is available via accompanying Supplemental material.

Chakshu Neeraj Kavan, Nithiarasu Perumal

2022-Sep-18

COVID-19, ITU, artificial intelligence, digital-twin, pneumonia

Pathology Pathology

S$^3$R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification

ArXiv Preprint

Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our knowledge, no appropriate self-supervised pre-training method has been designed for histopathology HSIs. In this paper, we introduce an efficient and effective Self-supervised Spectral Regression (S$^3$R) method, which exploits the low rank characteristic in the spectral domain of HSI. More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands. Then, the band is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1)S$^3$R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morphologies; (2)S$^3$R-BR, which regresses the missing band, making the model to learn the holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning methods, which focuses on natural images, S$^3$R converges at least 3 times faster, and achieves significant improvements up to 14% in accuracy when transferring to HSI classification tasks.

Xingran Xie, Yan Wang, Qingli Li

2022-09-19

General General

A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals

ArXiv Preprint

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology (Am) and rhythm (Ar). Our key insight is to suppress the over-dependence on Am or Ar while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively. In this paper, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.

Xingyao Wang, Yuwen Li, Hongxiang Gao, Xianghong Cheng, Jianqing Li, Chengyu Liu

2022-09-19

General General

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

ArXiv Preprint

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance's contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario.

Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li

2022-09-19

General General

Fault Detection in Ball Bearings

ArXiv Preprint

Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.

Joshua Pickard, Sarah Moll

2022-09-19

General General

A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices.

In Computers in biology and medicine

Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.

Alyafei Khalid, Ahmed Rashid, Abir Farhan Fuad, Chowdhury Muhammad E H, Naji Khalid Kamal

2022-Sep-01

Asymptomatic, COVID-19, Machine learning, Screening, Wearable systems

Public Health Public Health

A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers.

In PloS one ; h5-index 176.0

COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and COVID-19 transmission in SAARC countries. We also compared the predictive accuracy of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) methods for precise modelling of COVID-19 incidence. We compiled a daily dataset including confirmed COVID-19 case counts, minimum and maximum temperature (°C), relative humidity (%), surface pressure (kPa), precipitation (mm/day) and maximum wind speed (m/s) from the onset of the disease to January 29, 2022, in each country. The data were divided into training and test sets. The training data were used to fit ARIMAX model for examining significant meteorological risk factors. All significant factors were then used as covariates in ARIMAX and XGBoost models to predict the COVID-19 confirmed cases. We found that maximum temperature had a positive impact on the COVID-19 transmission in Afghanistan (β = 11.91, 95% CI: 4.77, 19.05) and India (β = 0.18, 95% CI: 0.01, 0.35). Surface pressure had a positive influence in Pakistan (β = 25.77, 95% CI: 7.85, 43.69) and Sri Lanka (β = 411.63, 95% CI: 49.04, 774.23). We also found that the XGBoost model can help improve prediction of COVID-19 cases in SAARC countries over the ARIMAX model. The study findings will help the scientific communities and policymakers to establish a more accurate early warning system to control the spread of the pandemic.

Rahman Md Siddikur, Chowdhury Arman Hossain

2022

Internal Medicine Internal Medicine

Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model.

In Internal and emergency medicine ; h5-index 30.0

Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. Using machine learning (ML) techniques we can detect parameters that help us to improve clinical diagnosis, since they are a system for the detection, prediction and treatment of complex data. ML techniques can be valuable for the study of COVID-19, especially because they can uncover complex patterns in large data sets. This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.

Casillas N, Torres A M, Moret M, Gómez A, Rius-Peris J M, Mateo J

2022-Sep-13

Artificial intelligence, COVID-19, Machine learning, Mortality, Prediction, SARS-CoV-2, XGB

General General

Predictive modelling and analytics of students' grades using machine learning algorithms.

In Education and information technologies

The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition, researchers and educational specialists around the globe always had a keen interest in predicting a student's performance based on the student's information such as previous exam results obtained and experiences. With the upsurge in using online learning platforms, predicting the student's performance by including their interactions such as discussion forums could be integrated to create a predictive model. The aims of the research are to provide a predictive model to forecast students' performance (grade/engagement) and to analyse the effect of online learning platform's features. The model created in this study made use of machine learning techniques to predict the final grade and engagement level of a learner. The quantitative approach for student's data analysis and processing proved that the Random Forest classifier outperformed the others. An accuracy of 85% and 83% were recorded for grade and engagement prediction respectively with attributes related to student profile and interaction on a learning platform.

Badal Yudish Teshal, Sungkur Roopesh Kevin

2022-Sep-08

Machine learning, Online learning platform, Predictive analysis, Random forest, Student engagement

General General

Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning.

In Biosensors & bioelectronics: X

We report a label-free method for detection of the SARS-CoV-2 virus in nasopharyngeal swab samples without purification steps and multiplication of the target which simplifies and expedites the analysis process. The kit consists of a textile grid on which liquid crystals (LC) are deposited and the grid is placed in a crossed polarized microscopy. The swab samples are subsequently placed on the LCs. In the presence of a particular biomolecule, the direction of LCs changes locally based on the properties of the biomolecule and forms a particular pattern. As the swab samples are not perfectly purified, image processing and machine learning techniques are employed to detect the presence of specific molecules or quantify their concentrations in the medium. The method can differentiate negative and positive COVID-19 samples with an accuracy of 96% and also differentiate COVID-19 from influenza types A and B with an accuracy of 93%. The kit is portable, simple to manufacture, convenient to operate, cost effective, rapid and sensitive. The simplicity of the specimen processing, the speed of image acquisition, and fast diagnostic operations enable the deployment of the proposed technique for performing extensive on-spot screening of COVID-19 in public places.

Esmailpour Mahboube, Mohammadimasoudi Mohammad, Shemirani Mohammadreza G, Goudarzi Ali, Heidari Beni Mohammad-Hossein, Shahsavarani Hosein, Aghajan Hamid, Mehrbod Parvaneh, Salehi-Vaziri Mostafa, Fotouhi Fatemeh

2022-Dec

COVID-19, Diagnostic, Influenza types A and B, Liquid crystals, Machine learning

General General

Deep Learning for Covid-19 Forecasting: state-of-the-art review.

In Neurocomputing

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning.

Kamalov Firuz, Rajab Khairan, Cherukuri Aswani, Elnagar Ashraf, Safaraliev Murodbek

2022-Sep-08

CNN, Covid-19 forecasting, GNN, LSTM, MLP, deep learning, survey

General General

The effect of COVID-19 lockdown on atmospheric total particle numbers, nanoparticle numbers and mass concentrations in the UK.

In Atmospheric pollution research

The main aim of the COVID-19 lockdown was to curtail the person-to-person transmission of COVID-19. However, it also acted as an air quality intervention. The effect of the lockdown has been extensively analysed on NO2, O3, PM10 and PM2.5, however, little has been done on how total (TPN) and nanoparticle numbers (NPN) have been affected by the lockdown. This paper quantifies the effect of the lockdown on TPN and NPN in the UK, and compares how the effect varies between rural, urban background and traffic sites. Furthermore, the effect on particle numbers is compared with particle mass concentrations, mainly PM10 and PM2.5. Two approaches are used: (a) comparing measured levels of the pollutants in 2019 with 2020 during the lockdown periods; and (b) comparing the predictions of machine learning with measured concentrations using business as usual (BAU) scenario during the lockdown period. P100 (particle size ≤100 nm) increased by 39% at Chilbolton Observatory (CHO) and decreased by 13% and 14% at London Honor Oak Park (LHO) and London Marylebone Road (LMR), respectively. Particles from 101 to 200 nm (P200) showed a similar trend to P100, however, average levels of particles 201-605 nm (P605) decreased at all sites. TPN, PM10 and PM2.5 concentrations decreased at LMR and LHO sites. Estimated PM10, PM2.5 and TPN decreased at all three sites, however, the amount of change varied from site to site. Pollutant concentrations increased back the to pre-pandemic levels, suggesting more sustainable interventions for permanent air quality improvement.

Munir Said, Chen Haibo, Crowther Richard

2022-Oct

COVID-19 lockdown, Machine learning, Nanoparticles, PM10 and PM2.5, Total particle number

General General

Time Series Prediction for Food sustainability

ArXiv Preprint

With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone. Doing so can improve people's livelihoods, health, and ecosystems for the present and future generations. Sustainable development, a paradigm of the United Nations, is rooted in food, crop, livestock, forest, population, and even the emission of gases. By understanding the overall usage of natural resources in different countries in the past, it is possible to forecast the demand in each country. The proposed solution consists of implementing a machine learning system using a statistical regression model that can predict the top k products that would endure a shortage in each country in a specific period in the future. The prediction performance in terms of absolute error and root mean square error show promising results due to its low errors. This solution could help organizations and manufacturers understand the productivity and sustainability needed to satisfy the global demand.

Fiona Victoria Stanley Jothiraj

2022-09-14

General General

Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies

ArXiv Preprint

The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heat discussion as users turn to share their attitudes on social media. In this paper, we consider a more realistic scenario on stance detection (i.e., cross-target and zero-shot settings) for the pandemic and propose an adversarial learning-based stance classifier to automatically identify the public attitudes toward COVID-19-related health policies. Specifically, we adopt adversarial learning which allows the model to train on a large amount of labeled data and capture transferable knowledge from source topics, so as to enable generalize to the emerging health policy with sparse labeled data. Meanwhile, a GeoEncoder is designed which encourages model to learn unobserved contextual factors specified by each region and represents them as non-text information to enhance model's deeper understanding. We evaluate the performance of a broad range of baselines in stance detection task for COVID-19-related policies, and experimental results show that our proposed method achieves state-of-the-art performance in both cross-target and zero-shot settings.

eng Xie, Zhong Zhang, Xuechen Zhao, Jiaying Zou, Bin Zhou, Yusong Tan

2022-09-10

Radiology Radiology

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-ray.

In IEEE journal of biomedical and health informatics

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration. In this work, we hereby propose a semantic-powered explainable model-free few-shot learning scheme to quickly and precisely diagnose COVID-19 with higher reliability and transparency. Specifically, we design a Report Image Explanation Cell (RIEC) to exploit clinically indicators derived from radiology reports as interpretable driver to introduce prior knowledge at training. Meanwhile, multi-task colla-borative diagnosis strategy (MCDS) is developed to construct [Formula: see text]-way [Formula: see text]-shot tasks, which adopts a cyclic and collaborative training approach for producing better generalization performance on new tasks. Extensive experiments demonstrate that the proposed scheme achieves competitive results (accuracy of 98.91%, precision of 98.95%, recall of 97.94% and F1-score of 98.57%) to diagnose COVID-19 and other pneumonia infected categories, even with only 200 paired CXR images and radiology reports for training. Furthermore, statistical results of comparative experiments show that our scheme provides an interpretable window into the COVID-19 diagnosis to improve the performance of the small sample size, the reliability and transparency of black-box deep learning mod-els. Our source codes will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/SPEMFSL-Diagnosis-COVID-19.

Wang Yihang, Jiang Chunjuan, Wu Youqing, Lv Tianxu, Sun Heng, Liu Yuan, Li Lihua, Pan Xiang

2022-Sep-08

General General

Improvement of students' enthusiasm by introduction of barrage into online teaching during COVID-19 pandemic.

In Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology

The outbreak of the COVID-19 pandemic results in the turning from offline teaching to online teaching. Students enjoy short videos and like barrage during the pandemic. We found that the introduction of barrage into online teaching is of great help to improve the students' attention and enthusiasm. In order to verify the correctness of this conjecture, we launched a questionnaire survey. According to the preliminary conclusions, we found that student's think that such adaption is not only interesting, but also can promote the interaction, and therefore improve the learning effect. It should conform to the trend of teaching development in the new era.

Li Yan, Liu Xingyou, Du Xiaojing, Shen Jie

2022-Sep-08

COVID-19, barrage, interactive, online teaching, student education

Pathology Pathology

Application of machine learning approaches to analyse student success for contact learning and emergency remote teaching and learning during the COVID-19 era in speech-language pathology and audiology.

In The South African journal of communication disorders = Die Suid-Afrikaanse tydskrif vir Kommunikasieafwykings

BACKGROUND :  The onset of the COVID-19 pandemic across the globe resulted in countries taking several measures to curb the spread of the disease. One of the measures taken was the locking down of countries, which entailed restriction of movement both locally and internationally. To ensure continuation of the academic year, emergency remote teaching and learning (ERTL) was launched by several institutions of higher learning in South Africa, where the norm was previously face-to-face or contact teaching and learning. The impact of this change is not known for the speech-language pathology and audiology (SLPA) students. This motivated this study.

OBJECTIVES :  This study aimed to evaluate the impact of the COVID-19 pandemic on SLPA undergraduate students during face-to-face teaching and learning, ERTL and transitioning towards hybrid teaching and learning.

METHOD :  Using course marks for SLPA undergraduate students, K means clustering and Random Forest classification were used to analyse students' performance and to detect patterns between students' performance and the attributes that impact student performance.

RESULTS :  Analysis of the data set indicated that funding is one of the main attributes that contributed significantly to students' performance; thus, it became one of the priority features in 2020 and 2021 during COVID-19.

CONCLUSION :  The clusters of students obtained during the analysis and their attributes can be used in identification of students that are at risk of not completing their studies in the minimum required time and early interventions can be provided to the students.

Madahana Milka C, Khoza-Shangase Katijah, Moroe Nomfundo, Nyandoro Otis, Ekoru John

2022-Aug-30

COVID-19, artificial intelligence, audiology, blended learning, contact, education, emergency remote teaching, hybrid learning, machine learning, speech–language pathology, teaching

General General

A proposed artificial intelligence-based real-time speech-to-text to sign language translator for South African official languages for the COVID-19 era and beyond: In pursuit of solutions for the hearing impaired.

In The South African journal of communication disorders = Die Suid-Afrikaanse tydskrif vir Kommunikasieafwykings

BACKGROUND :  The emergence of the coronavirus disease 2019 (COVID-19) pandemic has resulted in communication being heightened as one of the critical aspects in the implementation of interventions. Delays in the relaying of vital information by policymakers have the potential to be detrimental, especially for the hearing impaired.

OBJECTIVES :  This study aims to conduct a scoping review on the application of artificial intelligence (AI) for real-time speech-to-text to sign language translation and consequently propose an AI-based real-time translation solution for South African languages from speech-to-text to sign language.

METHODS :  Electronic bibliographic databases including ScienceDirect, PubMed, Scopus, MEDLINE and ProQuest were searched to identify peer-reviewed publications published in English between 2019 and 2021 that provided evidence on AI-based real-time speech-to-text to sign language translation as a solution for the hearing impaired. This review was done as a precursor to the proposed real-time South African translator.

RESULTS :  The review revealed a dearth of evidence on the adoption and/or maximisation of AI and machine learning (ML) as possible solutions for the hearing impaired. There is a clear lag in clinical utilisation and investigation of these technological advances, particularly in the African continent.

CONCLUSION :  Assistive technology that caters specifically for the South African community is essential to ensuring a two-way communication between individuals who can hear clearly and individuals with hearing impairments, thus the proposed solution presented in this article.

Madahana Milka C, Khoza-Shangase Katijah, Moroe Nomfundo, Mayombo Daniel, Nyandoro Otis, Ekoru John

2022-Aug-19

COVID-19, South Africa, artificial intelligence, hearing impaired, machine learning, sign language, speech, text, translation

General General

COVID-19 CT image segmentation method based on swin transformer.

In Frontiers in physiology

Owing to its significant contagion and mutation, the new crown pneumonia epidemic has caused more than 520 million infections worldwide and has brought irreversible effects on the society. Computed tomography (CT) images can clearly demonstrate lung lesions of patients. This study used deep learning techniques to assist doctors in the screening and quantitative analysis of this disease. Consequently, this study will help to improve the diagnostic efficiency and reduce the risk of infection. In this study, we propose a new method to improve U-Net for lesion segmentation in the chest CT images of COVID-19 patients. 750 annotated chest CT images of 150 patients diagnosed with COVID-19 were selected to classify, identify, and segment the background area, lung area, ground glass opacity, and lung parenchyma. First, to address the problem of a loss of lesion detail during down sampling, we replaced part of the convolution operation with atrous convolution in the encoder structure of the segmentation network and employed convolutional block attention module (CBAM) to enhance the weighting of important feature information. Second, the Swin Transformer structure is introduced in the last layer of the encoder to reduce the number of parameters and improve network performance. We used the CC-CCII lesion segmentation dataset for training and validation of the model effectiveness. The results of ablation experiments demonstrate that this method achieved significant performance gain, in which the mean pixel accuracy is 87.62%, mean intersection over union is 80.6%, and dice similarity coefficient is 88.27%. Further, we verified that this model achieved superior performance in comparison to other models. Thus, the method proposed herein can better assist doctors in evaluating and analyzing the condition of COVID-19 patients.

Sun Weiwei, Chen Jungang, Yan Li, Lin Jinzhao, Pang Yu, Zhang Guo

2022

COVID-19, CT image, deep learning, detection and recognition, lesion segmentation

Public Health Public Health

Risk Factors Associated with Mortality in COVID-19 Hospitalized Patients: Data from the Middle East.

In International journal of clinical practice

This study aimed to assess the risk factors for COVID-19 mortality among hospitalized patients in Jordan. All COVID-19 patients admitted to a tertiary hospital in Jordan from September 20, 2020, to August 8, 2021, were included in this study. Demographics, clinical characteristics, comorbidities, and laboratory results were extracted from the patients' electronic records. Multivariable logistic and machine learning (ML) methods were used to study variable importance. Out of 1,613 COVID-19 patients, 1,004 (62.2%) were discharged from the hospital (survived), while 609 (37.8%) died. Patients who were of elderly age (>65 years) (OR, 2.01; 95% CI, 1.28-3.16), current smokers (OR, 1.61; 95%CI, 1.17-2.23), and had severe or critical illness at admission ((OR, 1.56; 95%CI, 1.05-2.32) (OR, 2.94; 95%CI, 2.02-4.27); respectively), were at higher risk of mortality. Comorbidities including chronic kidney disease (OR, 2.90; 95% CI, 1.90-4.43), deep venous thrombosis (OR, 2.62; 95% CI, 1.08-6.35), malignancy (OR, 2.22; 95% CI, 1.46-3.38), diabetes (OR, 1.31; 95% CI, 1.04-1.65), and heart failure (OR, 1.51; 95% CI, 1.02-2.23) were significantly associated with increased risk of mortality. Laboratory abnormalities associated with mortality included hypernatremia (OR, 11.37; 95% CI, 4.33-29.81), elevated aspartate aminotransferase (OR, 1.81; 95% CI, 1.42-2.31), hypoalbuminemia (OR, 1.75; 95% CI, 1.37-2.25), and low platelets level (OR, 1.43; 95% CI, 1.05-1.95). Several demographic, clinical, and laboratory risk factors for COVID-19 mortality were identified. This study is the first to examine the risk factors associated with mortality using ML methods in the Middle East. This will contribute to a better understanding of the impact of the disease and improve the outcome of the pandemic worldwide.

Karasneh Reema A, Khassawneh Basheer Y, Al-Azzam Sayer, Al-Mistarehi Abdel-Hameed, Lattyak William J, Aldiab Motasem, Kabbaha Suad, Hasan Syed Shahzad, Conway Barbara R, Aldeyab Mamoon A

2022

General General

Novel insight on marker genes and pathogenic peripheral neutrophil subtypes in acute pancreatitis.

In Frontiers in immunology ; h5-index 100.0

Acute pancreatitis is a common critical and acute gastrointestinal disease worldwide, with an increasing percentage of morbidity. However, the gene expression pattern in peripheral blood has not been fully analyzed. In addition, the mechanism of coronavirus disease 2019 (COVID-19)-induced acute pancreatitis has not been investigated. Here, after bioinformatic analysis with machine-learning methods of the expression data of peripheral blood cells and validation in local patients, two functional gene modules in peripheral blood cells of acute pancreatitis were identified, and S100A6, S100A9, and S100A12 were validated as predictors of severe pancreatitis. Additionally, through a combination analysis of bulk sequencing and single-cell sequencing data of COVID-19 patients, a pivotal subtype of neutrophils with strong activation of the interferon-related pathway was identified as a pivotal peripheral blood cell subtype for COVID-19-induced acute pancreatitis. These results could facilitate the prognostic prediction of acute pancreatitis and research on COVID-19-induced acute pancreatitis.

Zhang Deyu, Wang Meiqi, Zhang Yang, Xia Chuanchao, Peng Lisi, Li Keliang, Yin Hua, Li Shiyu, Yang Xiaoli, Su Xiaoju, Huang Haojie

2022

COVID-19, WGCNA, acute pancreatitis, biomarkers, neutrophil, single-cell sequencing

General General

The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation.

In Methods of information in medicine

OBJECTIVE : To provide high-quality data for COVID-19 research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart.

MATERIALS AND METHODS : Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a Natural Language Processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes, so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR.

RESULTS : The concepts for COVID-19 positive cohort, COVID-19 index date, COVID-19 related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the Positive Predictive Value (PPV) in the pre-pandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes towards using DAPR for chart review. They assessed the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed.

DISCUSSION AND CONCLUSION : Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR's benefit can be expanded to other domains. We plan to operationalize it for wider research groups.

Park Heekyong, Wang Taowei David, Wattanasin Nich, Castro Victor M, Gainer Vivian, Goryachev Sergey, Murphy Shawn

2022-Sep-07

General General

Validity of at-home rapid antigen lateral flow assay and artificial intelligence read to detect SARS-CoV-2.

In Diagnostic microbiology and infectious disease

BACKGROUND : The gold standard for COVID-19 diagnosis-reverse-transcriptase polymerase chain reaction (RT-PCR)- is expensive and often slow to yield results whereas lateral flow tests can lack sensitivity.

METHODS : We tested a rapid, lateral flow antigen (LFA) assay with artificial intelligence read (LFAIR) in subjects from COVID-19 treatment trials (N = 37; daily tests for 5 days) and from a population-based study (N = 88; single test). LFAIR was compared to RT-PCR from same-day samples.

RESULTS : Using each participant's first sample, LFAIR showed 86.2% sensitivity (95% CI 73.6%-98.8) and 94.3% specificity (88.8%-99.7%) compared to RT-PCR. Adjusting for days since symptom onset and repeat testing, sensitivity was 97.8% (89.9%-99.5%) on the first symptomatic day and decreased with each additional day. Sensitivity improved with artificial intelligence (AI) read (86.2%) compared to the human eye (71.4%).

CONCLUSION : LFAIR showed improved accuracy compared to LFA alone. particularly early in infection.

Richardson Shannon, Kohn Michael A, Bollyky Jenna, Parsonnet Julie

2022-Jul-07

Artificial intelligence, COVID-19, Diagnostic accuracy, Rapid antigen test, SARS-CoV-2, Validity

General General

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

In Briefings in bioinformatics

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

Ren Zhong-Hao, You Zhu-Hong, Yu Chang-Qing, Li Li-Ping, Guan Yong-Jian, Guo Lu-Xiang, Pan Jie

2022-Sep-06

biomedical knowledge graph, deep learning, drug–drug interactions, graph neural network, multi-feature aggregation

General General

An AI-based disease detection and prevention scheme for COVID-19.

In Computers & electrical engineering : an international journal

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches.

Tanwar Sudeep, Kumari Aparna, Vekaria Darshan, Kumar Neeraj, Sharma Ravi

2022-Sep-02

AI, ARIMA, COVID-19, Disease prediction, Disease prevention, Healthcare 4.0, LSTM

General General

An Efficient Deep Neural Network Framework for COVID-19 Lung Infection Segmentation.

In Information sciences

Since the outbreak of Coronavirus Disease 2019 (COVID-19) in 2020, it has significantly affected the global health system. The use of deep learning technology to automatically segment pneumonia lesions from Computed Tomography (CT) images can greatly reduce the workload of physicians and expand traditional diagnostic methods. However, there are still some challenges to tackle the task, including obtaining high-quality annotations and subtle differences between classes. In the present study, a novel deep neural network based on Resnet architecture is proposed to automatically segment infected areas from CT images. To reduce the annotation cost, a Vector Quantized Variational AutoEncoder (VQ-VAE) branch is added to reconstruct the input images for purpose of regularizing the shared decoder and the latent maps of the VQ-VAE are utilized to further improve the feature representation. Moreover, a novel proportions loss is presented for mitigating class imbalance and enhance the generalization ability of the model. In addition, a semi-supervised mechanism based on adversarial learning to the network has been proposed, which can utilize the information of the trusted region in unlabeled images to further regularize the network. Extensive experiments on the COVID-SemiSeg are performed to verify the superiority of the proposed method, and the results are in line with expectations.

Jin Ge, Liu Chuancai, Chen Xu

2022-Sep-02

Adversarial network, COVID-19, Infection segmentation, Proportions loss, Semi-supervised learning, VQ-VAE

General General

Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data

ArXiv Preprint

COVID-19 has affected more than 223 countries worldwide. There is a pressing need for non invasive, low costs and highly scalable solutions to detect COVID-19, especially in low-resource countries where PCR testing is not ubiquitously available. Our aim is to develop a deep learning model identifying COVID-19 using voice data recordings spontaneously provided by the general population (voice recordings and a short questionnaire) via their personal devices. The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings. Methods: We used the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app. Voice features were extracted using a Mel-spectrogram analysis. Based on the voice data, we developed deep learning classification models to detect positive COVID-19 cases. These models included Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). We compared their predictive power to baseline classification models, namely Logistic Regression and Support Vector Machine. Results: LSTM based on a Mel-frequency cepstral coefficients (MFCC) features achieved the highest accuracy (89%,) with a sensitivity and specificity of respectively 89% and 89%, The results achieved with the proposed model suggest a significant improvement in the prediction accuracy of COVID-19 diagnosis compared to the results obtained in the state of the art. Conclusion: Deep learning can detect subtle changes in the voice of COVID-19 patients with promising results. As an addition to the current testing techniques this model may aid health professionals in fast diagnosis and tracing of COVID-19 cases using simple voice analysis

Wafaa Aljbawi, Sami O. Simmons, Visara Urovi

2022-09-08

General General

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19.

In Computers in biology and medicine

BACKGROUND : To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.

METHODS : We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.

RESULTS : The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities.

CONCLUSIONS : Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

deAndrés-Galiana Enrique J, Fernández-Martínez Juan Luis, Álvarez-Machancoses Óscar, Bea Guillermina, Galmarini Carlos M, Kloczkowski Andrzej

2022-Aug-30

Coronavirus, Drug repositioning, Machine learning, SARS-CoV-2, Side effects, Small scale genetic signature

General General

A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic.

In PLoS computational biology

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.

Hilton Joe, Riley Heather, Pellis Lorenzo, Aziza Rabia, Brand Samuel P C, K Kombe Ivy, Ojal John, Parisi Andrea, Keeling Matt J, Nokes D James, Manson-Sawko Robert, House Thomas

2022-Sep-06

General General

The Use of Automated Machine Translation to Translate Figurative Language in a Clinical Setting: Analysis of a Convenience Sample of Patients Drawn From a Randomized Controlled Trial.

In JMIR mental health

BACKGROUND : Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option.

OBJECTIVE : This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation.

METHODS : A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs.

RESULTS : AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing.

CONCLUSIONS : AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting.

TRIAL REGISTRATION : ClinicalTrials.gov NCT03538860; https://clinicaltrials.gov/ct2/show/NCT03538860.

Tougas Hailee, Chan Steven, Shahrvini Tara, Gonzalez Alvaro, Chun Reyes Ruth, Burke Parish Michelle, Yellowlees Peter

2022-Sep-06

AI, AI interpretation, AMT, ASR, ATP, FLD, LEP, artificial intelligence, assessment, asynchronous telepsychiatry, automated, automated machine translation, automated speech recognition, automated translation, figurative language device, language barriers, language concordant, language discordant, limited English proficiency, psychiatry, speech recognition, telepsychiatry, translation

General General

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features.

In Computational intelligence and neuroscience

Currently, under the impact of the COVID-19, college students are facing increasingly elevated employment pressure and higher education pressure. This can easily cause a huge psychological burden on them, causing affective cognition problems such as anxiety and depression. In the long run, this is not conducive to students' physical and mental health, nor is it conducive to the healthy development of the school and even the whole society. Therefore, it is imperative to build a novel adaptive affective cognition analysis model for college students. In particular, in the context of smart cities and smart China, many universities have opened the smart campus mode, which provides a huge data resource for our research. Due to problems of the low real-time evaluation and single data source in traditional questionnaire evaluation methods, evaluation errors are prone to occur, which in turn interferes with subsequent treatment. Therefore, for the purpose of alleviating the above deficiencies and improving the efficiency and accuracy of the affective cognition analysis model of college students, this paper studies the adaptive affective cognition analysis method of college students on basis of deep learning. First, because students' psychological problems are often not sudden, on the contrary, most of these abnormalities will leave traces in their daily activities. Therefore, this paper constructs a multisource dataset with the access control data, network data, and learning data collected from the smart campus platform to describe the affective cognition status of students. Second, the multisource dataset is divided into two categories: image and text, and the CNN model is introduced to mine the psychological characteristics of college students, so as to provide a reference for the subsequent affective cognition state assessment. Finally, simulation tests are developed to confirm the viability of the technique suggested in this research. The experiments demonstrate that the accuracy of the assessment model is significantly increased because it can fully reflect the heterogeneity and comprehensiveness of the data. This also highlights that the new method has a wide range of potential applications in the modern campus setting and is also helpful in fostering the accuracy and depth of college students' work on their affective cognition.

Feng Huali

2022

General General

Machine-learning method for analyzing and predicting the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic in the Lviv region.

In Journal of reliable intelligent environments

The purpose of this paper is to develop a machine-learning model for analyzing and predicting the number of hospitalizations of children in the Lviv region during the fourth wave of the COVID-19 pandemic. This wave is characterized by dominance of a new strain of the virus-Omicron-that spreads faster than previous ones and often affects children. Their high sociability and a low level of vaccination in Ukraine resulted in a sharp increase in the number of hospitalizations. The complexity of the research is also related to the geolocation of the Lviv region. This article analyzes and predicts the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic for the first time for the Lviv region. Data were obtained from publicly available resources. Public Domain Software-the Python programming language and the Pandas library-was used for software implementation of the machine-learning method: the developed model consists of two components-analysis and prediction. The analysis of the number of hospitalized children was performed using the Pearson correlation coefficient. Short- and medium-term predictions were made with the use of non-iterative SGTM neural-like structures that were taught in supervised mode and tested in online mode. The RMS and maximum ones that were reduced to the range of error values of short-term (up to a week) and medium-term (up to 2 weeks) predictions did not exceed 0.48% and 0.61% and 1.81% and 2.83%, respectively. The developed model can also be used for predicting other COVID-19 parameters.

Pavliuk Olena, Kolesnyk Halyna

2022-Sep-01

COVID, Correlation, Pandemic, Prediction, SGTM neural-like structures, The Lviv region

General General

A feasibility study of COVID-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry.

In Journal of breath research

BACKGROUND : SARS-CoV-2 has caused a tremendous threat to global health. PCR and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, FDA emergency approved VOC as an alternative test for COVID-19 detection.

METHODS AND MATERIALS : In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by HPPI-TOFMS for volatile organic components (VOCs). Machine learning (ML) algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation.

RESULTS : The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls.

CONCLUSIONS : This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.

Zhang Peize, Ren Tantan, Chen Haibin, Li Qingyun, He Mengqi, Feng Yong, Wang Lei, Huang Ting, Yuan Jing, Deng Guofang, Lu Hongzhou

2022-Sep-02

COVID-19, breath test, machine learning, volatile organic compounds

General General

A Comparison on LSTM Deep Learning Method and Random Walk Model Used on Financial and Medical Applications: An Example in COVID-19 Development Prediction.

In Computational intelligence and neuroscience

This study aims to establish the model of the cryptocurrency price trend based on a financial theory using the Long Short-Term Memory (LSTM) networks model with multiple combinations between the window length and the predicting horizons. The Random Walk model is also applied with different parameter settings. The object of this study is the cryptocurrency and medical issues, primarily the Bitcoin and Ethereum and the COVID-19. Quantitative analysis is adopted as the method of this dissertation. The research tool is Python programming language, and the TensorFlow package is employed to model and analyze research topics. The results of this study show the limitations of the LSTM and Random Walk model for price prediction while demonstrating the different characteristics of both models with different parameter settings, providing a balance between the model's accuracy and the model's practicality.

Yao Yifan, Li Xinxin, Li Qing

2022

General General

COVID-19 survivors: Multi-disciplinary efforts in psychiatry and medical humanities for long-term realignment.

In World journal of psychiatry

The coronavirus disease 2019 pandemic represents an enduring transformation in health care and education with the advancement of smart universities, telehealth, adaptive research protocols, personalized medicine, and self-controlled or artificial intelligence-controlled learning. These changes, of course, also cover mental health and long-term realignment of coronavirus disease 2019 survivors. Fatigue or anxiety, as the most prominent psychiatric "long coronavirus disease 2019" symptoms, need a theory-based and empirically-sound procedure that would help us grasp the complexity of the condition in research and treatment. Considering the systemic character of the condition, such strategies have to take the whole individual and their sociocultural context into consideration. Still, at the moment, attempts to build an integrative framework for providing meaning and understanding for the patients of how to cope with anxiety when they are confronted with empirically reduced parameters (e.g., severe acute respiratory syndrome coronavirus type 2) or biomarkers (e.g., the FK506 binding protein 5) are rare. In this context, multidisciplinary efforts are necessary. We therefore join in a plea for an establishment of 'translational medical humanities' that would allow a more straightforward intervention of humanities (e.g., the importance of the therapist variable, continuity, the social environment, etc) into the disciplinary, medial, political, and popular cultural debates around health, health-care provision, research (e.g., computer scientists for simulation studies), and wellbeing.

Löffler-Stastka Henriette, Pietrzak-Franger Monika

2022-Jul-19

Long COVID, Medical Humanities, Multi-disciplinarity, Psychiatric sequelae, Resilience

General General

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

In Journal of photochemistry and photobiology. B, Biology

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Karunakaran Varsha, Joseph Manu M, Yadev Induprabha, Sharma Himanshu, Shamna Kottarathil, Saurav Sumeet, Sreejith Remanan Pushpa, Anand Veena, Beegum Rosenara, Regi David S, Iype Thomas, Sarada Devi K L, Nizarudheen A, Sharmad M S, Sharma Rishi, Mukhiya Ravindra, Thouti Eshwar, Yoosaf Karuvath, Joseph Joshy, Sujatha Devi P, Savithri S, Agarwal Ajay, Singh Sanjay, Maiti Kaustabh Kumar

2022-Aug-19

Artificial intelligence, COVID-19, Diagnosis, Label-free, Saliva, Surface enhanced Raman spectroscopy

General General

PAN-cODE: COVID-19 Forecasting using Conditional Latent ODEs.

In Journal of the American Medical Informatics Association : JAMIA

The coronavirus disease 2019 (COVID-19) pandemic has caused millions of deaths around the world and revealed the need for data-driven models of pandemic spread. Accurate pandemic caseload forecasting allows informed policy decisions on the adoption of non-pharmaceutical interventions (NPIs) to reduce disease transmission. Using COVID-19 as an example, we present PAN-cODE, a deep learning method to forecast daily increases in pandemic infections and deaths. By using a deep conditional latent variable model, PAN-cODE can generate alternative caseload trajectories based on alternate adoptions of NPIs, allowing stakeholders to make policy decisions in an informed manner. PAN-cODE also allows caseload estimation for regions that are unseen during model training. We demonstrate that, despite using less detailed data and having fully automated training, PAN-cODE's performance is comparable to state-of-the-art methods on four-week-ahead and six-week-ahead forecasting. Finally, we highlight the ability of PAN-cODE to generate realistic alternative outcome trajectories on select US regions.

Shi Ruian, Zhang Haoran, Morris Quaid

2022-Sep-01

deep learning, latent variable models, pandemic prediction, time series forecasting

General General

Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay.

In Lab on a chip

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 103 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.

Potter Colin J, Hu Yanmei, Xiong Zhen, Wang Jun, McLeod Euan

2022-Sep-01

Public Health Public Health

Asian hate speech detection on Twitter during COVID-19.

In Frontiers in artificial intelligence

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and hate against Asians in western countries, especially in the United States. An analysis of official preliminary police data by the Center for the Study of Hate & Extremism at California State University shows that Anti-Asian hate crime in 16 of America's largest cities increased by 149% in 2020. In this study, we first chose a baseline of Americans' hate crimes against Asians on Twitter. Then we present an approach to balance the biased dataset and consequently improve the performance of tweet classification. We also have downloaded 10 million tweets through the Twitter API V-2. In this study, we have used a small portion of that, and we will use the entire dataset in the future study. In this article, three thousand tweets from our collected corpus are annotated by four annotators, including three Asian and one Asian-American. Using this data, we built predictive models of hate speech using various machine learning and deep learning methods. Our machine learning methods include Random Forest, K-nearest neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression, Decision Tree, and Naive Bayes. Our Deep Learning models include Basic Long-Term Short-Term Memory (LSTM), Bidirectional LSTM, Bidirectional LSTM with Drop out, Convolution, and Bidirectional Encoder Representations from Transformers (BERT). We also adjusted our dataset by filtering tweets that were ambiguous to the annotators based on low Fleiss Kappa agreement between annotators. Our final result showed that Logistic Regression achieved the best statistical machine learning performance with an F1 score of 0.72, while BERT achieved the best performance of the deep learning models, with an F1-Score of 0.85.

Toliyat Amir, Levitan Sarah Ita, Peng Zheng, Etemadpour Ronak

2022

Asian hate crime, COVID-19, Twitter, machine learning, natural language processing

General General

Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.

In Computational intelligence and neuroscience

The outbreak of the COVID-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the COVID-19 epidemic. Radiological imaging such as computed tomography (CT) and chest X-rays (CXR) is considered an effective way to diagnose COVID-19. However, it needs an expert's knowledge and consumes more time. At the same time, artificial intelligence (AI) and medical images are discovered to be helpful in effectively assessing and providing treatment for COVID-19 infected patients. In particular, deep learning (DL) models act as a vital part of a high-performance classification model for COVID-19 recognition on CXR images. This study develops a heap-based optimization with the deep transfer learning model for detection and classification (HBODTL-DC) of COVID-19. The proposed HBODTL-DC system majorly focuses on the identification of COVID-19 on CXR images. To do so, the presented HBODTL-DC model initially exploits the Gabor filtering (GF) technique to enhance the image quality. In addition, the HBO algorithm with a neural architecture search network (NasNet) large model is employed for the extraction of feature vectors. Finally, Elman Neural Network (ENN) model gets the feature vectors as input and categorizes the CXR images into distinct classes. The experimental validation of the HBODTL-DC model takes place on the benchmark CXR image dataset from the Kaggle repository, and the outcomes are checked in numerous dimensions. The experimental outcomes stated the supremacy of the HBODTL-DC model over recent approaches with a maximum accuracy of 0.9992.

Fakieh Bahjat, Ragab Mahmoud

2022

Pathology Pathology

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

In Database : the journal of biological databases and curation

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.

Chen Qingyu, Allot Alexis, Leaman Robert, Islamaj Rezarta, Du Jingcheng, Fang Li, Wang Kai, Xu Shuo, Zhang Yuefu, Bagherzadeh Parsa, Bergler Sabine, Bhatnagar Aakash, Bhavsar Nidhir, Chang Yung-Chun, Lin Sheng-Jie, Tang Wentai, Zhang Hongtong, Tavchioski Ilija, Pollak Senja, Tian Shubo, Zhang Jinfeng, Otmakhova Yulia, Yepes Antonio Jimeno, Dong Hang, Wu Honghan, Dufour Richard, Labrak Yanis, Chatterjee Niladri, Tandon Kushagri, Laleye Fréjus A A, Rakotoson Loïc, Chersoni Emmanuele, Gu Jinghang, Friedrich Annemarie, Pujari Subhash Chandra, Chizhikova Mariia, Sivadasan Naveen, Vg Saipradeep, Lu Zhiyong

2022-Aug-31

General General

How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study.

In The Journal of supercomputing

Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%).

Sarv Ahrabi Sima, Momenzadeh Alireza, Baccarelli Enzo, Scarpiniti Michele, Piazzo Lorenzo

2022-Aug-26

BiGAN, CAE, COVID-19 detection, Complexity-vs.-accuracy comparisons, CycleGAN, Hidden feature extraction, Unsupervised-vs.-weakly supervised learning

General General

Early warning system to predict energy prices: the role of artificial intelligence and machine learning.

In Annals of operations research

The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.

Alshater Muneer M, Kampouris Ilias, Marashdeh Hazem, Atayah Osama F, Banna Hasanul

2022-Aug-26

COVID-19, Early warning systems, Energy equity prices, Forecasting, Machine learning, United States

Public Health Public Health

Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data.

In Minds and machines

The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people's use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to examine public knowledge and behavior. This paper explores the ethical considerations of using artificial intelligence to monitor social media to understand the public's perspectives and behaviors surrounding COVID-19, including potential risks and benefits of an AI-driven approach. Importantly, investigators and ethics committees have a role in ensuring that researchers adhere to ethical principles of respect for persons, beneficence, and justice in a way that moves science forward while ensuring public safety and confidence in the process.

Flores Lidia, Young Sean D

2022-Aug-25

Artificial intelligence, COVID-19, big data, ethics, social media

General General

A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality rate with the effect of vaccine and immunity. The hospitalization and intense demand decreased. However, there is no definite information about when this disease will end or how dangerous the different variants could be. In addition, it is not possible to end the risk of variants that will continue to circulate among animals in nature. After this stage, drug-virus interactions should be examined in order to be able to prepare against possible new types of viruses and variants and to rapidly-produce drugs or vaccines against possible viruses. Despite experimental methods that are expensive, laborious, and time-consuming, geometric deep learning(GDL) is an alternative method that can be used to make this process faster and cheaper. In this study, we propose a new model based on geometric deep learning for the prediction of drug-virus interaction against COVID-19. First, we use the antiviral drug data in the SMILES molecular structure representation to generate too many features and better describe the structure of chemical species. Then the data is converted into a molecular representation and then into a graphical structure that the GDL model can understand. The node feature vectors are transferred to a different space with the Message Passing Neural Network (MPNN) for the training process to take place. We develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is actualized. The results indicate that the proposed method outperforms existing methods with 97% accuracy in predicting drug-virus interactions.

Das Bihter, Kutsal Mucahit, Das Resul

2022-Oct-15

Antiviral drugs, COVID-19, Drug-target combination, Geometric deep learning, Graph neural networks, Message passing neural network

General General

Designing a new fast solution to control isolation rooms in hospitals depending on artificial intelligence decision.

In Biomedical signal processing and control

Decreasing the COVID spread of infection among patients at physical isolation hospitals during the coronavirus pandemic was the main aim of all governments in the world. It was required to increase isolation places in the hospital's rules to prevent the spread of infection. To deal with influxes of infected COVID-19 patients' quick solutions must be explored. The presented paper studies converting natural rooms in hospitals into isolation sections and constructing new isolation cabinets using prefabricated components as alternative and quick solutions. Artificial Intelligence (AI) helps in the selection and making of a decision on which type of solution will be used. A Multi-Layer Perceptron Neural Network (MLPNN) model is a type of artificial intelligence technique used to design and implement on time, cost, available facilities, area, and spaces as input parameters. The MLPNN result decided to select a prefabricated approach since it saves 43% of the time while the cost was the same for the two approaches. Forty-five hospitals have implemented a prefabricated solution which gave excellent results in a short period of time at reduced costs based on found facilities and spaces. Prefabricated solutions provide a shorter time and lower cost by 43% and 78% in average values respectively as compared to retrofitting existing natural ventilation rooms.

Khaled Ahmed S, Mohammed Ali R, Maha Lashin M, Fayroz Sherif F

2023-Jan

Infected patients, Isolation Rooms, Negative pressure, Quarantine

General General

Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning.

In Computers in biology and medicine

Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes "patient status" metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as additional attributes in modeling. With these caveats, previous efforts have demonstrated that genotype-patient status models can be fit to GISAID data, particularly when country-of-origin is used as an additional feature. But are these models robust and biologically meaningful? This paper shows that, in fact, temporal and geographic biases in sequences submitted to GISAID, as well as the evolving pandemic response, particularly reduction in severe disease due to vaccination, create complex issues for model development and interpretation. This paper poses a potential solution: efficient mixed effects machine learning using GPBoost, treating country as a random effect group. Training and validation using temporally split GISAID data and emerging Omicron variants demonstrates that GPBoost models are more predictive of the impact of spike protein mutations on patient outcomes than fixed effect XGBoost, LightGBM, random forests, and elastic net logistic regression models.

Sokhansanj Bahrad A, Rosen Gail L

2022-Aug-17

Bioinformatics, COVID-19, Machine learning, SARS-CoV-2, Viral genomics

General General

Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature with Unsupervised Word Embeddings and Machine Learning.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In the massive and rapidly growing corpuses such as the COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that evaluates multiple aspects such as network topological features, communities and their temporal trends can make this process more efficient.

OBJECTIVE : We aim to show that new knowledge can be captured and tracked using the temporal change in the underlying unsupervised word embeddings of literature. Further imminent themes can be predicted using machine learning upon the evolving associations between words.

METHODS : Frequently occurring medical entities were extracted from the abstracts of more than 150,000 COVID-19 articles published on the WHO database, collected on a monthly interval starting from February 2020. Word embeddings trained on each month's literature were used to construct networks of entities with cosine similarities as edge weights. Topological features of the subsequent month's network were forecasted based on prior patterns and new links were predicted using supervised machine learning. Community detection and alluvial diagrams were used to track biomedical themes that evolved over the months.

RESULTS : We found that thromboembolic complications were detected as an emerging theme as early as August 2020. A shift towards symptoms of Long COVID complications was observed during March 2021 and neurological complications gained significance in June 2021. A prospective validation of the link prediction models achieved an AUROC score of 0.87. Predictive modeling revealed predisposing conditions, symptoms, cross-infection and neurological complications as a dominant research theme in COVID-19 publications based on patterns observed in previous months.

CONCLUSIONS : Machine learning-based prediction of emerging links can contribute towards steering research by capturing themes represented by groups of medical entities, based on patterns of semantic relationships over time.

CLINICALTRIAL :

Pal Ridam, Chopra Harshita, Awasthi Raghav, Bandhey Harsh, Nagori Aditya, Sethi Tavpritesh

2022-Feb-11

General General

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation.

In Journal of chemical information and modeling

Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G1, G) can be generated for the graph representation G of protein-protein complex by using different graphs G1, which reveal G1-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.

Liu Xiang, Feng Huitao, Wu Jie, Xia Kelin

2022-Aug-30

General General

A Critical Popularization of Customized Curation Service for Cosmetics in Republic of Korea.

In Journal of cosmetic dermatology ; h5-index 25.0

BACKGROUND : Consumer and advanced consumption culture in modern society is an era that focuses on individual personality and value, and "my own customized products," or customized marketing strategies, are actively being developed throughout the industry. Recently, IT technologies that can support personalized services such as artificial intelligence, ubiquitous systems, and marketing automation have been recognized for their potential, directly or indirectly affecting distribution industries affected by personal consumption culture. Accordingly, customized products or services, i.e., customization, are attracting attention as an effective methodology to cope with such market changes.

OBJECTIVES : Among the necessities used by modern women, cosmetics account for an endless interest in beauty and maintaining physical and mental health, and as the cosmetics market expands, it is considered that the cosmetics industry needs a clearer and in-depth study on the cosmetics sub-market to satisfy consumers' diverse needs.

METHODS : This review paper is a literature review, and a narrative review approach has been used for this study. A total of 300 to 400 references were selected using representative journal search websites such as PubMed, Google Scholar, Scopus, ResearchGate, LitCovid, DBPia and RISS, of which a total of 37 papers were selected in the final stage based on 2013 to 2022 using PRISMA flow diagram.

RESULTS : Therefore, this study suggested to indicate the changes in the cosmetics market due to the emergence of cosmetics curation services after the COVID-19 pandemic, advanced changes in consumer purchase patterns following the 4th Industrial Revolution, and significant future prospects of cosmetics curation services.

CONCLUSION : As the beauty and cosmetology industry is expected to develop in the future, it will grow as a centerpiece of the beauty industry and symbolizes nationalized cultural pride. Therefore, this review article will be continuing to promote customization as a premium beauty service in Republic of Korea through corporate analysis.

Park Eunjeong, Kwon Ki Han

2022-Aug-30

4th industrial revolution, COVID-19 Pandemic, Customized cosmetics, Customized cosmetics business system, Customized curation, Product consumption markets

General General

A Machine-Learning Analysis of the Impacts of the COVID-19 Pandemic on Small Business Owners and Implications for Canadian Government Policy Response.

In Canadian public policy. Analyse de politiques

This study applies a machine-learning technique to a dataset of 38,000 textual comments from Canadian small business owners on the impacts of coronavirus disease 2019 (COVID-19). Topic modelling revealed seven topics covering the short- and longer-term impacts of the pandemic, government relief programs and loan eligibility issues, mental health, and other impacts on business owners. The results emphasize the importance of policy response in aiding small business crisis management and offer implications for theory and policy. Moreover, the study provides an example of using a machine-learning-based automated content analysis in the fields of crisis management, small business, and public policy.

Isabelle Diane A, Han Yu Jade, Westerlund Mika

2022-Jun-01

COVID-19 crisis management, Canada, impacts, small business, topic modelling

General General

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.

In Computational intelligence and neuroscience

Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use. The Chest X-ray (CXR) and CT images were effectively utilized in screening purposes that could provide relevant data on localized regions affected by the infection. A step towards automated screening and diagnosis using CXR and CT could be of considerable importance in these turbulent times. The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures. Further feature selection strategy using Random Forest (RF) then selected features used to create Machine Learning (ML) representation with Support Vector Machine (SVM) and a Random Forest (RF) to make different COVID-19 from viral pneumonia (VP). The results obtained clearly indicate that the intensity and WT-based features vary in the two pathologies that are better differentiated with the combined features trained using SVM and RF classifiers. Classifier performance measures like an Area Under the Curve (AUC) of 0.97 and by and large classification accuracy of 0.9 using the RF model clearly indicate that the methodology implemented is useful in characterizing COVID-19 and Viral Pneumonia.

Mahaboob Basha Shaik, Lira Neto Aloísio Vieira, Alshathri Samah, Elaziz Mohamed Abd, Hashmitha Mohisin Shaik, De Albuquerque Victor Hugo C

2022

General General

COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans.

In Computer methods and programs in biomedicine update

With the increase in severity of COVID-19 pandemic situation, the world is facing a critical fight to cope up with the impacts on human health, education and economy. The ongoing battle with the novel corona virus, is showing much priority to diagnose and provide rapid treatment to the patients. The rapid growth of COVID-19 has broken the healthcare system of the affected countries, creating a shortage in ICUs, test kits, ventilation support system. etc. This paper aims at finding an automatic COVID-19 detection approach which will assist the medical practitioners to diagnose the disease quickly and effectively. In this paper, a deep convolutional neural network, 'COV-RadNet' is proposed to detect COVID positive, viral pneumonia, lung opacity and normal, healthy people by analyzing their Chest Radiographic (X-ray and CT scans) images. Data augmentation technique is applied to balance the dataset 'COVID 19 Radiography Dataset' to make the classifier more robust to the classification task. We have applied transfer learning approach using four deep learning based models: VGG16, VGG19, ResNet152 and ResNext 101 to detect COVID-19 from chest X-ray images. We have achieved 97% classification accuracy using our proposed COV-RadNet model for COVID/Viral Pneumonia/Lungs Opacity/Normal, 99.5% accuracy to detect COVID/Viral Pneumonia/Normal and 99.72% accuracy to detect COVID and non-COVID people. Using chest CT scan images, we have found 99.25% accuracy to classify between COVID and non-COVID classes. Among the performance of the pre-trained models, ResNext 101 has shown the highest accuracy of 98.5% for multiclass classification (COVID, viral pneumonia, Lungs opacity and normal).

Islam Md Khairul, Habiba Sultana Umme, Khan Tahsin Ahmed, Tasnim Farzana

2022-Aug-25

COVID-19, CT Scan, Chest X-ray, Convolutional Neural Network, Data Augmentation

Surgery Surgery

Boundary-Aware Network for Kidney Parsing

ArXiv Preprint

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65$\%$ for kidney structure segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net.

Shishuai Hu, Yiwen Ye, Zehui Liao, Yong Xia

2022-08-29

General General

Multi-dimensional Racism Classification during COVID-19: Stigmatization, Offensiveness, Blame, and Exclusion

ArXiv Preprint

Transcending the binary categorization of racist texts, our study takes cues from social science theories to develop a multi-dimensional model for racism detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of BERT and topic modeling, this categorical detection enables insights into the underlying subtlety of racist discussion on digital platforms during COVID-19. Our study contributes to enriching the scholarly discussion on deviant racist behaviours on social media. First, a stage-wise analysis is applied to capture the dynamics of the topic changes across the early stages of COVID-19 which transformed from a domestic epidemic to an international public health emergency and later to a global pandemic. Furthermore, mapping this trend enables a more accurate prediction of public opinion evolvement concerning racism in the offline world, and meanwhile, the enactment of specified intervention strategies to combat the upsurge of racism during the global public health crisis like COVID-19. In addition, this interdisciplinary research also points out a direction for future studies on social network analysis and mining. Integration of social science perspectives into the development of computational methods provides insights into more accurate data detection and analytics.

Xin Pei, Deval Mehta

2022-08-29

General General

Effectiveness of front line and emerging fungal disease prevention and control interventions and opportunities to address appropriate eco-sustainable solutions.

In The Science of the total environment

Fungal pathogens contribute to significant disease burden globally; however, the fact that fungi are eukaryotes has greatly complicated their role in fungal-mediated infections and alleviation. Antifungal drugs are often toxic to host cells and there is increasing evidence of adaptive resistance in animals and humans. Existing fungal diagnostic and treatment regimens have limitations that has contributed to the alarming high mortality rates and prolonged morbidity seen in immunocompromised cohorts caused by opportunistic invasive infections as evidenced during HIV and COVID-19 pandemics. There is a need to develop real-time monitoring and diagnostic methods for fungal pathogens and to create a greater awareness as to the contribution of fungal pathogens in disease causation. Greater information is required on the appropriate selection and dose of antifungal drugs including factors governing resistance where there is commensurate need to discover more appropriate and effective solutions. Popular azole fungal drugs are widely detected in surface water and sediment due to incomplete removal in wastewater treatment plants where they are resistant to microbial degradation and may cause toxic effects on aquatic organisms such as algae and fish. UV has limited effectiveness in destruction of anti-fungal drugs where there is increased interest in the combination approaches such as novel use of pulsed-plasma gas-discharge technologies for environmental waste management. There is growing interest in developing alternative and complementary green eco-biocides and disinfection innovation. Fungi present challenges for cleaning, disinfection and sterilization of reusable medical devices such as endoscopes where they (example, Aspergillus and Candida species) can be protected when harboured in build-up biofilm from lethal processing. Information on the efficacy of established disinfection and sterilization technologies to address fungal pathogens including bottleneck areas that present high risk to patients is lacking. There is a need to address risk mitigation and modelling to inform efficacy of appropriate intervention technologies that must consider all contributing factors where there is potential to adopt digital technologies to enable real-time analysis of big data, such as use of artificial intelligence and machine learning. International consensus on standardised protocols for developing and reporting on appropriate alternative eco-solutions must be reached, particularly in order to address fungi with increasing drug resistance where research and innovation can be enabled using a One Health approach.

Garvey Mary, Meade Elaine, Rowan Neil J

2022-Aug-24

Disinfection- sterilization, Drug resistance, Environmental toxicity, Pathogenic fungi, Sustainability

Radiology Radiology

HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.

In Computers in biology and medicine

the automatic segmentation of lung infections in CT slices provides a rapid and effective strategy for diagnosing, treating, and assessing COVID-19 cases. However, the segmentation of the infected areas presents several difficulties, including high intraclass variability and interclass similarity among infected areas, as well as blurred edges and low contrast. Therefore, we propose HADCNet, a deep learning framework that segments lung infections based on a dual hybrid attention strategy. HADCNet uses an encoder hybrid attention module to integrate feature information at different scales across the peer hierarchy to refine the feature map. Furthermore, a decoder hybrid attention module uses an improved skip connection to embed the semantic information of higher-level features into lower-level features by integrating multi-scale contextual structures and assigning the spatial information of lower-level features to higher-level features, thereby capturing the contextual dependencies of lesion features across levels and refining the semantic structure, which reduces the semantic gap between feature maps at different levels and improves the model segmentation performance. We conducted fivefold cross-validations of our model on four publicly available datasets, with final mean Dice scores of 0.792, 0.796, 0.785, and 0.723. These results show that the proposed model outperforms popular state-of-the-art semantic segmentation methods and indicate its potential use in the diagnosis and treatment of COVID-19.

Chen Ying, Zhou Taohui, Chen Yi, Feng Longfeng, Zheng Cheng, Liu Lan, Hu Liping, Pan Bujian

2022-Aug-20

COVID-19 infection, Deep learning, Dual hybrid attention strategy, HADCNet, Segmentation

General General

DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing

bioRxiv Preprint

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein-ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein-ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the state-of-the-art methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein-ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein-ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.

Wei, B.; Zhang, Y.; Gong, X.

2022-08-28

Public Health Public Health

Evaluating the ability of the NLHA2 and artificial neural network models to predict COVID-19 severity, and comparing them with the four existing scoring systems.

In Microbial pathogenesis

To improve the identification and subsequent intervention of COVID-19 patients at risk for ICU admission, we constructed COVID-19 severity prediction models using logistic regression and artificial neural network (ANN) analysis and compared them with the four existing scoring systems (PSI, CURB-65, SMARTCOP, and MuLBSTA). In this prospective multi-center study, 296 patients with COVID-19 pneumonia were enrolled and split into the General-Ward-Care group (N = 238) and the ICU-Admission group (N = 58). The PSI model (AUC = 0.861) had the best results among the existing four scoring systems, followed by SMARTCOP (AUC = 0.770), motified-MuLBSTA (AUC = 0.761), and CURB-65 (AUC = 0.712). Data from 197 patients (training set) were analyzed for modeling. The beta coefficients from logistic regression were used to develop a severity prediction model and risk score calculator. The final model (NLHA2) included five covariates (consumes alcohol, neutrophil count, lymphocyte count, hemoglobin, and AKP). The NLHA2 model (training: AUC = 0.959; testing: AUC = 0.857) had similar results to the PSI model, but with fewer variable items. ANN analysis was used to build another complex model, which had higher accuracy (training: AUC = 1.000; testing: AUC = 0.907). Discrimination and calibration were further verified through bootstrapping (2000 replicates), Hosmer-Lemeshow goodness of fit testing, and Brier score calculation. In conclusion, the PSI model is the best existing system for predicting ICU admission among COVID-19 patients, while two newly-designed models (NLHA2 and ANN) performed better than PSI, and will provide a new approach for the development of prognostic evaluation system in a novel respiratory viral epidemic.

Dong Yue, Wang Kai, Zou Xu, Tan Xiaoping, Zang Yi, Li Xinyu, Ren Xiaoting, Xie Desheng, Jie Zhijun, Chen Xiaohua, Zeng Yingying, Shi Jindong

2022-Aug-22

COVID-19, CURB-65, Machine learning, MuLBSTA, PSI, SMARTCOP

Public Health Public Health

Actions Speak Louder Than Words: A Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake.

In JMIR formative research

BACKGROUND : The lack of trust in vaccines is a major contributor to vaccine hesitancy. To overcome vaccine hesitancy for the COVID-19 vaccine, the Australian government launched multiple public health campaigns to encourage vaccine uptake. This sentiment analysis examines the effect of public health campaigns and COVID-19-related events on sentiment and vaccine uptake.

OBJECTIVE : This study aimed to examine the relationship between sentiment and COVID-19 vaccine uptake and government actions that impacted public sentiment about the vaccine.

METHODS : Using machine learning methods, we collected 137,523 publicly available English language tweets published in Australia between February and October 2021 that contained COVID-19 vaccine-related keywords. Machine learning methods were used to extract topics and sentiments relating to COVID-19 vaccination. The relationship between public vaccination sentiment on Twitter and vaccine uptake were examined.

RESULTS : The majority of collected tweets expressed negative (91,052, 66%), rather than positive (21,686, 16%) or neutral (24,785, 18%), sentiments. Topics discussed within the study timeframe included the role of the government in the vaccination rollout, availability and accessibility of the vaccine, and vaccine efficacy. There was a significant positive correlation between negative sentiment and the number of vaccine doses administered daily (r(267) = 0.15, p<.05), with positive sentiment showing the inverse effect. Public health campaigns, lockdowns and anti-vaccination protests were associated with increased negative sentiment, while vaccination mandates had no significant effect on sentiment.

CONCLUSIONS : The study findings demonstrate that negative sentiment was more prevalent on Twitter during the Australian vaccination rollout, but vaccine uptake remained high. Australians expressed anger at the slow rollout and the limited availability of the vaccine during the study period. Public health campaigns, lockdowns and anti-vaccination rallies increased negative sentiment. In contrast, news of increased vaccine availability for the public and government acquisition of more doses were key government actions that reduced negative sentiment. These findings can be used to inform government communication planning.

CLINICALTRIAL :

Yousef Murooj, Dietrich Timo, Rundle-Thiele Sharyn

2022-Aug-23

General General

Metabolomics Markers of COVID-19 Are Dependent on Collection Wave.

In Metabolites

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2-7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.

Lewis Holly-May, Liu Yufan, Frampas Cecile F, Longman Katie, Spick Matt, Stewart Alexander, Sinclair Emma, Kasar Nora, Greener Danni, Whetton Anthony D, Barran Perdita E, Chen Tao, Dunn-Walters Deborah, Skene Debra J, Bailey Melanie J

2022-Jul-30

COVID-19, LC-MS, machine learning, targeted metabolomics

Radiology Radiology

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.

In Journal of cardiovascular development and disease

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Khanna Narendra N, Maindarkar Mahesh, Puvvula Anudeep, Paul Sudip, Bhagawati Mrinalini, Ahluwalia Puneet, Ruzsa Zoltan, Sharma Aditya, Munjral Smiksha, Kolluri Raghu, Krishnan Padukone R, Singh Inder M, Laird John R, Fatemi Mostafa, Alizad Azra, Dhanjil Surinder K, Saba Luca, Balestrieri Antonella, Faa Gavino, Paraskevas Kosmas I, Misra Durga Prasanna, Agarwal Vikas, Sharma Aman, Teji Jagjit, Al-Maini Mustafa, Nicolaides Andrew, Rathore Vijay, Naidu Subbaram, Liblik Kiera, Johri Amer M, Turk Monika, Sobel David W, Pareek Gyan, Miner Martin, Viskovic Klaudija, Tsoulfas George, Protogerou Athanasios D, Mavrogeni Sophie, Kitas George D, Fouda Mostafa M, Kalra Manudeep K, Suri Jasjit S

2022-Aug-15

COVID-19, artificial intelligence, carotid, coronary, coronavirus, pulmonary, renal, vascular damage

Cardiology Cardiology

Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device.

In Biosensors

Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients' hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions.

Verma Vijay Kumar, Lin Wen-Yen

2022-Aug-05

COPD, COVID-19, activity index, hospital readmission, machine learning, physical activity, readmission prediction

General General

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering.

In Biosensors

In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model's uncertainty is unacceptably high.

Ikponmwoba Eloghosa, Ukorigho Okezzi, Moitra Parikshit, Pan Dipanjan, Gartia Manas Ranjan, Owoyele Opeoluwa

2022-Aug-02

COVID-19, Gaussian processes, machine learning, surface-enhanced Raman spectroscopy

General General

Evaluation of COVID-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020.

In Frontiers in psychiatry

Background : COVID-19 was named a global pandemic by the World Health Organization in March 2020. Governments across the world issued various restrictions such as staying at home. These restrictions significantly influenced mental health worldwide. This study aims to document the prevalence of mental health problems and their relationship with the quality and quantity of social relationships affected by the pandemic during the United States national lockdown.

Methods : Sample data was employed from the COVID-19 Impact Survey on April 20-26, 2020, May 4-10, 2020, and May 30-June 8, 2020 from United States Dataset. A total number of 8790, 8975, and 7506 adults participated in this study for April, May and June, respectively. Participants' mental health evaluations were compared clinically by looking at the quantity and quality of their social ties before and during the pandemic using machine learning techniques. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, Naive Bayes, and logistic regression.

Results : The result for each contributing feature has been analyzed separately in detail. On the other hand, the influence of each feature was studied to evaluate the impact of COVID-19 on mental health. The overall result of our research indicates that people who had previously been diagnosed with any type of mental illness were most affected by the new constraints during the pandemic. These people were among the most vulnerable due to the imposed changes in lifestyle.

Conclusion : This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions.

Sadegh-Zadeh Seyed-Ali, Bahrami Mahboobe, Najafi Amirreza, Asgari-Ahi Meisam, Campion Russell, Hajiyavand Amir M

2022

COVID-19 pandemic, machine learning, mental health, prediction model, psychiatry issues, social behaviours, statistic analysis

General General

Role of tannic acid against SARS-cov-2 cell entry by targeting the interface region between S-protein-RBD and human ACE2.

In Frontiers in pharmacology

Coronavirus disease 2019 (COVID-19) was caused by a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 utilizes human angiotensin converting enzyme 2 (hACE2) as the cellular receptor of its spike glycoprotein (SP) to gain entry into cells. Consequently, we focused on the potential of repurposing clinically available drugs to block the binding of SARS-CoV-2 to hACE2 by utilizing a novel artificial-intelligence drug screening approach. Based on the structure of S-RBD and hACE2, the pharmacophore of SARS-CoV-2-receptor-binding-domain (S-RBD) -hACE2 interface was generated and used to screen a library of FDA-approved drugs. A total of 20 drugs were retrieved as S-RBD-hACE2 inhibitors, of which 16 drugs were identified to bind to S-RBD or hACE2. Notably, tannic acid was validated to interfere with the binding of S-RBD to hACE2, thereby inhibited pseudotyped SARS-CoV-2 entry. Experiments involving competitive inhibition revealed that tannic acid competes with S-RBD and hACE2, whereas molecular docking proved that tannic acid interacts with the essential residues of S-RBD and hACE2. Based on the known antiviral activity and our findings, tannic acid might serve as a promising candidate for preventing and treating SARS-CoV-2 infection.

Chen Xi, Wang Ziyuan, Wang Jing, Yao Yifan, Wang Qian, Huang Jiahao, Xiang Xianping, Zhou Yifan, Xue Yintong, Li Yan, Gao Xiang, Wang Lijun, Chu Ming, Wang Yuedan

2022

COVID-19, SARS-cov-2, SARS-cov-2-RBD, hACE2, mutant strain, spike protein, tannic acid

General General

Exploring socioeconomic status as a global determinant of COVID-19 prevalence, using exploratory data analytic and supervised machine learning techniques.

In JMIR formative research

BACKGROUND : The COVID-19 pandemic represents the most unprecedented global challenge in recent times. As the global community attempts to manage the pandemic long-term, it is pivotal to understand what factors drive prevalence rates, and to predict the future trajectory of the virus.

OBJECTIVE : This study has two objectives. Firstly, it tests the statistical relationship between socioeconomic status and COVID-19 prevalence. Secondly, it utilises machine learning techniques to predict cumulative COVID-19 cases in a multi-country sample of 182 countries. Taken together, these objectives will shed light upon socioeconomic status as a global risk factor of the COVID-19 pandemic.

METHODS : This research utilised exploratory data analysis and supervised machine learning methods. Exploratory analysis included variable distribution, variable correlations and outlier detection. Following this, three supervised regression techniques were applied: linear regression, random forest, and adaptive boosting. Results were evaluated using k-fold cross validation and subsequently compared to analyse algorithmic suitability. The analysis involved two models. Firstly, the algorithms were trained to predict 2021 COVID-19 prevalence using only 2020 reported case data. Following this, socioeconomic indicators were added as features and the algorithms were trained again. The Human Development Index metrics of life expectancy, mean years of schooling, expected years of schooling, and Gross National Income were used to approximate socioeconomic status.

RESULTS : All variables correlated positively with 2021 COVID-19 prevalence, with R2 values ranging from 0.55-0.85. Using socioeconomic indicators, COVID-19 prevalence was predicted with a reasonable degree of accuracy. Using 2020 reported case rates as a lone predictor to predict 2021 prevalence rates, the average predictive accuracy of the algorithms was low (R2=0.543). When the socioeconomic indicators were added alongside 2020 prevalence rates as features, average predictive performance improved considerably (R2=0.721) and all error statistics decreased. This suggested that adding socioeconomic indicators alongside 2020 reported case data optimised prediction of COVID-19 prevalence to a considerable degree. Linear regression was the strongest learner with R2=0.693 on the first model and R2=0.763 on the second model, followed by random forest (0.481 and 0.722) and AdaBoost (0.454 and 0.679). Following this, the second model was retrained using a selection of additional COVID-19 risk factors (population density, median age, and vaccination uptake) instead of the HDI metrics. Average accuracy dropped to 0.649 however, which highlights the value of socioeconomic status as a predictor of COVID-19 cases in the chosen sample.

CONCLUSIONS : Results show that socioeconomic status should be an important variable to consider in future epidemiological modelling, and highlights the reality of the COVID-19 pandemic as a social phenomenon as well as a healthcare phenomenon. This paper also puts forward new considerations about the application of statistical and machine learning techniques to understand and combat the COVID-19 pandemic.

CLINICALTRIAL :

Winston Luke, McCann Michael, Onofrei George

2022-Apr-27

General General

Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study.

In Medical journal of the Islamic Republic of Iran

Background: Owing to the shortage of ventilators, there is a crucial demand for an objective and accurate prognosis for 2019 coronavirus disease (COVID-19) critical patients, which may necessitate a mechanical ventilator (MV). This study aimed to construct a predictive model using machine learning (ML) algorithms for frontline clinicians to better triage endangered patients and priorities who would need MV. Methods: In this retrospective single-center study, the data of 482 COVID-19 patients from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms including, multi-layer perception (MLP), logistic regression (LR), J-48 decision tree, and Naïve Bayes (NB). First, the most important clinical variables were identified using the Chi-square test at P < 0.01. Then, by comparing the ML algorithms' performance using some evaluation criteria, including TP-Rate, FP-Rate, precision, recall, F-Score, MCC, and Kappa, the best performing one was identified. Results: Predictive models were trained using 15 validated features, including cough, contusion, oxygen therapy, dyspnea, loss of taste, rhinorrhea, blood pressure, absolute lymphocyte count, pleural fluid, activated partial thromboplastin time, blood glucose, white cell count, cardiac diseases, length of hospitalization, and other underline diseases. The results indicated the J-48 with F-score = 0.868 and AUC = 0.892 yielded the best performance for predicting intubation requirement. Conclusion: ML algorithms are potentials to improve traditional clinical criteria to forecast the necessity for intubation in COVID-19 in-hospital patients. Such ML-based prediction models may help physicians with optimizing the timing of intubation, better sharing of MV resources and personnel, and increase patient clinical status.

Nopour Raoof, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi

2022

COVID-19, Coronavirus, Intubation, Machine Learning, Mechanical Ventilator, Prognosis

General General

Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions.

In Expert systems with applications

The ongoing COVID-19 pandemic has created an unprecedented predicament for global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, and healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment risks can substantially reduce that nervousness. Thenceforth, this paper proposes a few Deep Learning (DL) approaches to mitigate shipment risks by predicting "if a shipment can be exported from one source to another", despite the restrictions imposed by the COVID-19 pandemic. The proposed DL methodologies have four main stages: data capturing, de-noising or pre-processing, feature extraction, and classification. The feature extraction stage depends on two main variants of DL models. The first variant involves three recurrent neural networks (RNN) structures (i.e., long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU)), and the second variant is the temporal convolutional network (TCN). In terms of the classification stage, six different classifiers are applied to test the entire methodology. These classifiers are SoftMax, random trees (RT), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). The performance of the proposed DL models is evaluated based on an online dataset (taken as a case study). The numerical results show that one of the proposed models (i.e., TCN) is about 100% accurate in predicting the risk of shipment to a particular destination under COVID-19 restrictions. Unarguably, the aftermath of this work will help the decision-makers to predict supply chain risks proactively to increase the resiliency of the SCs.

Bassiouni Mahmoud M, Chakrabortty Ripon K, Hussain Omar K, Rahman Humyun Fuad

2022-Aug-19

COVID-19, Classifiers, Convolutional network, Deep learning, Supply chain risk, Temporal convolutional network

General General

Microblog data analysis of emotional reactions to COVID-19 in China.

In Journal of psychosomatic research

To explore the emotional attitudes of microblog users in the different COVID-19 stages in China, this study used data mining and machine-learning methods to crawl 112,537 Sina COVID-19- related microblogs and conduct sentiment and group difference analyses. It was found that: (1) the microblog users' emotions shifted from negative to positive from the second COVID-19 pandemic phase; (2) there were no significant differences in the microblog users' emotions in the different regions; (3) males were more optimistic than females in the early stages of the pandemic; however, females were more optimistic than males in the last three stages; and (4) females posted more microblogs and expressed more sadness and fear while males expressed more anger and disgust. This research captured online information in real-time, with the results providing a reference for future research into public opinion and emotional reactions to crises.

Jin Yuchang, Yan Aoxue, Sun Tengwei, Zheng Peixuan, An Junxiu

2022-Jun-30

Basic emotions, COVID-19, Data mining, Sentiment analysis technology, Sina microblogs

Cardiology Cardiology

Clinical characteristics of patients with confirmed and asymptomatic SARS-CoV-2 infection in China.

In PloS one ; h5-index 176.0

OBJECTIVE : To examine the clinical characteristics of patients with asymptomatic novel coronavirus disease 2019 (COVID-19) and compare them with those of patients with mild disease.

DESIGN : A retrospective cohort study.

SETTING : Multiple medical centers in Wuhan, Hubei, China.

PARTICIPANTS : A total of 3,263 patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between February 4, 2020, and April 15, 2020.

MAIN OUTCOME MEASURES : Patient demographic characteristics, medical history, vital signs, and laboratory and chest computed tomography (CT) findings.

RESULTS : A total of 3,173 and 90 patients with mild and moderate, and asymptomatic COVID-19, respectively, were included. A total of 575 (18.2%) symptomatic patients and 4 (4.4%) asymptomatic patients developed the severe illness. All asymptomatic patients recovered; no deaths were observed in this group. The median duration of viral shedding in asymptomatic patients was 17 (interquartile range, 9.25-25) days. Patients with higher levels of ultrasensitive C-reactive protein (odds ratio [OR] = 1.025, 95% confidence interval [CI], 1.01-1.04), lower red blood cell volume distribution width (OR = 0.68, 95% CI 0.51-0.88), lower creatine kinase Isoenzyme(0.94, 0.89-0.98) levels, or lower lesion ratio (OR = 0.01, 95% CI 0.00-0.33) at admission were more likely than their counterparts to have asymptomatic disease.

CONCLUSIONS : Patients with younger ages and fewer comorbidities are more likely to be asymptomatic. Asymptomatic patients had similar laboratory characteristics and longer virus shedding time than symptomatic patients; screen and isolation during their infection were helpful to reduce the risk of SARS-CoV-2 transmission.

Li Zongren, Zhong Qin, Li Wenyuan, Zhang Dawei, Wang Wenjun, Yang Feifei, He Kunlun

2022

Radiology Radiology

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

In European journal of radiology open

Objectives : When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models.

Methods : We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias.

Results : We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.

Conclusions : The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Jia Lu-Lu, Zhao Jian-Xin, Pan Ni-Ni, Shi Liu-Yan, Zhao Lian-Ping, Tian Jin-Hui, Huang Gang

2022

2D, two-dimensional, 3D, three-dimensional, AI, artificial intelligence, AUC, area under the curve, Artificial Intelligence, CNN, Convolutional neural network, COVID-19, COVID-19, Coronavirus disease 2019, CRP, C-reactive protein, CT, Computed tomography, CXR, Chest X-Ray, Diagnostic Imaging, GGO, ground-glass opacities, KNN, K-nearest neighbor, LASSO, least absolute shrinkage and selection operator, MEERS-COV, Middle East respiratory syndrome coronavirus, ML, machine learning, Machine learning, PLR, negative likelihood ratio, PLR, positive likelihood ratio, Pneumonia, ROI, regions of interest, RT-PCR, Reverse transcriptase polymerase chain reaction, SARS, severe acute respiratory syndrome, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2, SROC, summary receiver operating characteristic, SVM, Support vector machine

General General

A walk in the black-box: 3D visualization of large neural networks in virtual reality.

In Neural computing & applications

Within the last decade Deep Learning has become a tool for solving challenging problems like image recognition. Still, Convolutional Neural Networks (CNNs) are considered black-boxes, which are difficult to understand by humans. Hence, there is an urge to visualize CNN architectures, their internal processes and what they actually learn. Previously, virtual realityhas been successfully applied to display small CNNs in immersive 3D environments. In this work, we address the problem how to feasibly render large-scale CNNs, thereby enabling the visualization of popular architectures with ten thousands of feature maps and branches in the computational graph in 3D. Our software "DeepVisionVR" enables the user to freely walk through the layered network, pick up and place images, move/scale layers for better readability, perform feature visualization and export the results. We also provide a novel Pytorch module to dynamically link PyTorch with Unity, which gives developers and researchers a convenient interface to visualize their own architectures. The visualization is directly created from the PyTorch class that defines the Pytorch model used for training and testing. This approach allows full access to the network's internals and direct control over what exactly is visualized. In a use-case study, we apply the module to analyze models with different generalization abilities in order to understand how networks memorize images. We train two recent architectures, CovidResNet and CovidDenseNet on the Caltech101 and the SARS-CoV-2 datasets and find that bad generalization is driven by high-frequency features and the susceptibility to specific pixel arrangements, leading to implications for the practical application of CNNs. The code is available on Github https://github.com/Criscraft/DeepVisionVR.

Linse Christoph, Alshazly Hammam, Martinetz Thomas

2022-Aug-18

Deep convolutional neural network visualization, Explainable artificial intelligence, Human-understandable AI systems, Virtual reality

General General

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.

In Computational intelligence and neuroscience

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.

Mohammed Mazin Abed, Al-Khateeb Belal, Yousif Mohammed, Mostafa Salama A, Kadry Seifedine, Abdulkareem Karrar Hameed, Garcia-Zapirain Begonya

2022

General General

Investigation of vaccination game approach in spreading covid-19 epidemic model with considering the birth and death rates.

In Chaos, solitons, and fractals

In this study, an epidemic model for spreading COVID-19 is presented. This model considers the birth and death rates in the dynamics of spreading COVID-19. The birth and death rates are assumed to be the same, so the population remains constant. The dynamics of the model are explained in two phases. The first is the epidemic phase, which spreads during a season based on the proposed SIR/V model and reaches a stable state at the end of the season. The other one is the "vaccination campaign", which takes place between two seasons based on the rules of the vaccination game. In this stage, each individual in the population decides whether to be vaccinated or not. Investigating the dynamics of the studied model during a single epidemic season without consideration of the vaccination game shows waves in the model as experimental knowledge. In addition, the impact of the parameters is studied via the rules of the vaccination game using three update strategies. The result shows that the pandemic speeding can be changed by varying parameters such as efficiency and cost of vaccination, defense against contagious, and birth and death rates. The final epidemic size decreases when the vaccination coverage increases and the average social payoff is modified.

Vivekanandhan Gayathri, Zavareh Mahdi Nourian, Natiq Hayder, Nazarimehr Fahimeh, Rajagopal Karthikeyan, Svetec Milan

2022-Aug-18

Covid-19, Epidemic model, Game theory, Vaccination game

Public Health Public Health

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.

In Biomedical signal processing and control

At the end of 2019, a novel coronavirus, COVID-19, was ravaging the world, wreaking havoc on public health and the global economy. Today, although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for COVID-19 clinical diagnosis, it is a time-consuming and labor-intensive procedure. Simultaneously, an increasing number of individuals are seeking for better alternatives to RT-PCR. As a result, automated identification of COVID-19 lung infection in computed tomography (CT) images may help traditional diagnostic approaches in determining the severity of the disease. Unfortunately, a shortage of labeled training sets makes using AI deep learning algorithms to accurately segregate diseased regions in CT scan challenging. We design a simple and effective weakly supervised learning strategy for COVID-19 CT image segmentation to overcome the segmentation issue in the absence of adequate labeled data, namely LLC-Net. Unlike others weakly supervised work that uses a complex training procedure, our LLC-Net is relatively easy and repeatable. We propose a Local Self-Coherence Mechanism to accomplish label propagation based on lesion area labeling characteristics for weak labels that cannot offer comprehensive lesion areas, hence forecasting a more complete lesion area. Secondly, when the COVID-19 training samples are insufficient, the Scale Transform for Self-Correlation is designed to optimize the robustness of the model to ensure that the CT images are consistent in the prediction results from different angles. Finally, in order to constrain the segmentation accuracy of the lesion area, the Lesion Infection Edge Attention Module is used to improve the information expression ability of edge modeling. Experiments on public datasets demonstrate that our method is more effective than other weakly supervised methods and achieves a new state-of-the-art performance.

Sun Wanchun, Feng Xin, Liu Jingyao, Ma Hui

2023-Jan

COVID-19, Local Coherence, Segmentation, Weakly Supervised

General General

A deep learning-based COVID-19 classification from chest X-ray image: case study.

In The European physical journal. Special topics

The novel corona virus disease (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and more than 6 millions of people died in last 2 years. Early detection of COVID-19 can mitigate and control its spread. Reverse transcription polymerase chain reaction (RT-CPR), Chest X-ray (CXR) scan, and Computerized Tomography (CT) scan are used to identify the COVID-19. Chest X-ray image analysis is relatively time efficient than compared with RT-CPR and CT scan. Its cost-effectiveness make it a good choice for COVID-19 Classification. We propose a deep learning based Convolutional Neural Network model for detection of COVID-19 from CXR. Chest X-ray images are collected from various sources dataset for training with augmentation and evaluating our model, which is widely used for COVID-19 detection and diagnosis. A Deep Convolutional neural network (CNN) based model for analysis of COVID-19 with data augmentation is proposed, which uses the patient's chest X-ray images for the diagnosis of COVID-19 with an aim to help the physicians to assist the diagnostic process among high workload conditions. The overall accuracy of 93 percent for COVID-19 Classification is achieved by choosing best optimizer.

Appasami G, Nickolas S

2022-Aug-18

General General

EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

ArXiv Preprint

Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE.

Feng Xie, Zhong Zhang, Liang Li, Bin Zhou, Yusong Tan

2022-08-23

General General

Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach.

In Environmental research ; h5-index 67.0

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provide a unique time window to examine the changes in air quality attributable to airport closure. It is also interested to know whether such an intervention produce extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 μg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 μg/m3, accounted for a marginal reduction of 18.6% of the overall NO2 change driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.002 μg/m3 NO2 per air-flight. Our results highlight that attention need to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.

Han Bo, Yao Tingwei, Li Guojian, Song Yuqin, Zhang Yiye, Dai Qili, Yu Jian

2022-Aug-16

Air quality, Airport emission, COVID-19, Lockdown, NO(2)

oncology Oncology

Rapid tissue prototyping with micro-organospheres.

In Stem cell reports

In vitro tissue models hold great promise for modeling diseases and drug responses. Here, we used emulsion microfluidics to form micro-organospheres (MOSs), which are droplet-encapsulated miniature three-dimensional (3D) tissue models that can be established rapidly from patient tissues or cells. MOSs retain key biological features and responses to chemo-, targeted, and radiation therapies compared with organoids. The small size and large surface-to-volume ratio of MOSs enable various applications including quantitative assessment of nutrient dependence, pathogen-host interaction for anti-viral drug screening, and a rapid potency assay for chimeric antigen receptor (CAR)-T therapy. An automated MOS imaging pipeline combined with machine learning overcomes plating variation, distinguishes tumorspheres from stroma, differentiates cytostatic versus cytotoxic drug effects, and captures resistant clones and heterogeneity in drug response. This pipeline is capable of robust assessments of drug response at individual-tumorsphere resolution and provides a rapid and high-throughput therapeutic profiling platform for precision medicine.

Wang Zhaohui, Boretto Matteo, Millen Rosemary, Natesh Naveen, Reckzeh Elena S, Hsu Carolyn, Negrete Marcos, Yao Haipei, Quayle William, Heaton Brook E, Harding Alfred T, Bose Shree, Driehuis Else, Beumer Joep, Rivera Grecia O, van Ineveld Ravian L, Gex Donald, DeVilla Jessica, Wang Daisong, Puschhof Jens, Geurts Maarten H, Yeung Athena, Hamele Cait, Smith Amber, Bankaitis Eric, Xiang Kun, Ding Shengli, Nelson Daniel, Delubac Daniel, Rios Anne, Abi-Hachem Ralph, Jang David, Goldstein Bradley J, Glass Carolyn, Heaton Nicholas S, Hsu David, Clevers Hans, Shen Xiling

2022-Aug-10

CAR-T, SARS-COV-2, cytostatic, cytotoxic, deep learning, demulsification, drug resistant, micro-organospheres, organoid, patient derived organoid

General General

Emerging technologies for combating pandemics.

In Expert review of medical devices

INTRODUCTION : Covid-19, alongside previous pandemics, has highlighted the need for the continued development of technologies that are at our disposal. Emerging technologies are those that show true promise in achieving such a goal and have begun to form sturdy independent research areas. Technological advances in healthcare must continually develop to ensure that the world is prepared for any future diseases that may ensue. As such, a strategic review into 39 manuscripts since 2019 has been conducted to determine the prominence of emerging technologies since the beginning of the Covid-19 pandemic.

AREAS COVERED : Relating to their use in a pandemic state, additive manufacturing (AM), biofabrication, microfluidics, biomedical microelectromechanical systems (BioMEMS), and artificial intelligence (AI) are described. Applications over the past 2-3 years, as well as future developments, are considered throughout.

EXPERT OPINION : All the technologies mentioned in this review are sure to develop further, having shown their importance and value during the covid-19 pandemic. As research continues within the area, their efficacy will increase to the point where it likely will become gold standard for pandemic control. Combining certain technologies mentioned has also proved to have had great success in improving the final results obtained.

Weaver Edward, Uddin Shahid, Lamprou Dimitrios A

2022-Aug-19

3D printing, Artificial Intelligence, BioMEMS, Biofabrication, Covid-19, Microfluidics, Microneedles, Pandemics

Public Health Public Health

DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.

In Computational and mathematical methods in medicine

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images.

Liu Jingyao, Zhao Jiashi, Zhang Liyuan, Miao Yu, He Wei, Shi Weili, Li Yanfang, Ji Bai, Zhang Ke, Jiang Zhengang

2022

General General

Distinguishing features of Long COVID identified through immune profiling.

In medRxiv : the preprint server for health sciences

SARS-CoV-2 infection can result in the development of a constellation of persistent sequelae following acute disease called post-acute sequelae of COVID-19 (PASC) or Long COVID 1-3 . Individuals diagnosed with Long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions 1-3 ; however, the basic biological mechanisms responsible for these debilitating symptoms are unclear. Here, 215 individuals were included in an exploratory, cross-sectional study to perform multi-dimensional immune phenotyping in conjunction with machine learning methods to identify key immunological features distinguishing Long COVID. Marked differences were noted in specific circulating myeloid and lymphocyte populations relative to matched control groups, as well as evidence of elevated humoral responses directed against SARS-CoV-2 among participants with Long COVID. Further, unexpected increases were observed in antibody responses directed against non-SARS-CoV-2 viral pathogens, particularly Epstein-Barr virus. Analysis of circulating immune mediators and various hormones also revealed pronounced differences, with levels of cortisol being uniformly lower among participants with Long COVID relative to matched control groups. Integration of immune phenotyping data into unbiased machine learning models identified significant distinguishing features critical in accurate classification of Long COVID, with decreased levels of cortisol being the most significant individual predictor. These findings will help guide additional studies into the pathobiology of Long COVID and may aid in the future development of objective biomarkers for Long COVID.

Klein Jon, Wood Jamie, Jaycox Jillian, Lu Peiwen, Dhodapkar Rahul M, Gehlhausen Jeff R, Tabachnikova Alexandra, Tabacof Laura, Malik Amyn A, Kamath Kathy, Greene Kerrie, Monteiro Valter Silva, Peña-Hernandez Mario, Mao Tianyang, Bhattacharjee Bornali, Takahashi Takehiro, Lucas Carolina, Silva Julio, Mccarthy Dayna, Breyman Erica, Tosto-Mancuso Jenna, Dai Yile, Perotti Emily, Akduman Koray, Tzeng Tiffany J, Xu Lan, Yildirim Inci, Krumholz Harlan M, Shon John, Medzhitov Ruslan, Omer Saad B, van Dijk David, Ring Aaron M, Putrino David, Iwasaki Akiko

2022-Aug-10

General General

Using artificial intelligence for personal protective equipment guidance for healthcare workers in the COVID-19 pandemic and beyond.

In Communicable diseases intelligence (2018)

Background : Current procedures for effective personal protective equipment (PPE) usage rely on the availability of trained observers or 'buddies' who, during the COVID-19 pandemic, are not always available. The application of artificial intelligence (AI) has the potential to overcome this limitation by assisting in complex task analysis. To date, AI use for PPE protocols has not been studied. In this paper we validate the performance of an AI PPE system in a hospital setting.

Methods : A clinical cohort study of 74 healthcare workers (HCW) at a 144-bed University teaching hospital. Participants were recruited to use the AI system for PPE donning and doffing. Performance was validated by the current gold standard double-buddy system across seven donning and ten doffing steps based on local infection control guidelines.

Results : The AI-PPE platform was 98.9% sensitive on doffing and 85.3% sensitive on donning, when compared to remediated double buddy. On average, buddy correction of PPE was required 3.8 ± 1.5% of the time. The average time taken to don was 240 ± 51.5 seconds and doff was 241 ± 35.3 seconds.

Conclusion : This study demonstrates the ability of an AI model to analyse PPE donning and doffing with real-time feedback for remediation. The AI platform can identify complex multi-task PPE donning and doffing in a single validated system. This AI system can be employed to train, audit, and thereby improve compliance whilst reducing reliance on limited HCW resources. Further studies may permit the development of this educational tool into a medical device with other industry uses for safety.

Preda Veronica A, Jayapadman Anand, Zacharakis Alexandra, Magrabi Farah, Carney Terry, Petocz Peter, Wilson Michael

2022-Aug-18

\n artificial intelligence, \n healthcare worker, \n infections, \n pandemic, \n patient safety, \n personal protective equipment

General General

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis from CT Images.

In IEEE journal of biomedical and health informatics

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited chest CT training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with 3×3 filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to heavy ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets even in the presence of limited training CT data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19. The codes and models are available for users at https://github.com/AmoghJ001/MFL_Net.

Joshi Amogh Manoj, Nayak Deepak Ranjan

2022-Aug-18

General General

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population.

In Frontiers in medicine

Background : Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.

Methods : Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.

Results : The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.

Conclusion : The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Matysek Adrian, Studnicka Aneta, Smith Wade Menpes, Hutny Michał, Gajewski Paweł, Filipiak Krzysztof J, Goh Jorming, Yang Guang

2022

COVID-19, SARS-CoV-2, blood biomarkers, machine learning, vitamin D

General General

Repurchase intentions of new e-commerce users in the COVID-19 context: The mediation role of brand love.

In Frontiers in psychology ; h5-index 92.0

The use of e-commerce has exploded due to the impact of COVID-19. People with no experience in e-commerce prior to the COVID-19 pandemic began online shopping for their safety following the pandemic outbreak. As such, these newly joined customers have played a vital role in the rapid development of e-commerce. Maintaining these customers and increasing their repurchase intention is a core issue for e-commerce platform companies. Thus, using new e-commerce users as the participants, this study investigated the structural relationship between brand experience, brand emotional factors (brand attachment and brand love), brand loyalty, and repurchase intention with brand love as the mediator. Research on the multidimensional brand experience (i.e., sensory, emotional, behavioral, and cognitive) from Chinese customers' perspective is still lacking, and our study attempts to fill this gap. A structured questionnaire and hypotheses were designed based on studies and survey of 310 respondents from China in this study. The study results show that, first, the four dimensions of brand experience have a significant positive correlation with brand emotion, with brand cognitive experience having the greatest impact on consumer brand emotion. Second, the influence of brand emotion on brand loyalty is positive and significant, and brand attachment has a stronger influence than brand love on brand loyalty. In addition, brand loyalty has a positive effect on repurchase intention. Finally, brand love plays a mediating role on the relationship between brand attachment and brand loyalty. To enhance customers' brand attachment and love for e-commerce platforms, companies must enhance customers' interest and curiosity in their products. And companies will improve their services to customers by introducing artificial intelligence algorithms to increase customers' repurchase intention, which will ultimately increasing their profitability. This study contributes to the development of e-commerce platform companies.

Ding Yi, Tu Ruonan, Xu Yahong, Park Sung Kyu

2022

brand attachment, brand experience, brand love, brand loyalty, new e-commerce users, repurchase intention

General General

IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention.

In ACS nano ; h5-index 203.0

Nanomedicine-based and unmodified drug interventions to address COVID-19 have evolved over the course of the pandemic as more information is gleaned and virus variants continue to emerge. For example, some early therapies (e.g., antibodies) have experienced markedly decreased efficacy. Due to a growing concern of future drug resistant variants, current drug development strategies are seeking to find effective drug combinations. In this study, we used IDentif.AI, an artificial intelligence-derived platform, to investigate the drug-drug and drug-dose interaction space of six promising experimental or currently deployed therapies at various concentrations: EIDD-1931, YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs were tested in vitro against a live B.1.1.529 (Omicron) virus first in monotherapy and then in 50 strategic combinations designed to interrogate the interaction space of 729 possible combinations. Key findings and interactions were then further explored and validated in an additional experimental round using an expanded concentration range. Overall, we found that few of the tested drugs showed moderate efficacy as monotherapies in the actionable concentration range, but combinatorial drug testing revealed significant dose-dependent drug-drug interactions, specifically between EIDD-1931 and YH-53, as well as nirmatrelvir and YH-53. Checkerboard validation analysis confirmed these synergistic interactions and also identified an interaction between EIDD-1931 and favipiravir in an expanded range. Based on the platform nature of IDentif.AI, these findings may support further explorations of the dose-dependent drug interactions between different drug classes in further pre-clinical and clinical trials as possible combinatorial therapies consisting of unmodified and nanomedicine-enabled drugs, to combat current and future COVID-19 strains and other emerging pathogens.

Blasiak Agata, Truong Anh T L, Wang Peter, Hooi Lissa, Chye De Hoe, Tan Shi-Bei, You Kui, Remus Alexandria, Allen David Michael, Chai Louis Yi Ann, Chan Conrad E Z, Lye David C B, Tan Gek-Yen G, Seah Shirley G K, Chow Edward Kai-Hua, Ho Dean

2022-Aug-17

Artificial Intelligence, COVID-19, Combinatorial Therapy, Drug Combinations, Drug Discovery, IDentif.AI, SARS-CoV-2

Public Health Public Health

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program.

In JAMA health forum

Importance : Overcoming social barriers to COVID-19 testing is an important issue, especially given the demographic disparities in case incidence rates and testing. Delivering culturally appropriate testing resources using data-driven approaches in partnership with community-based health workers is promising, but little data are available on the design and effect of such interventions.

Objectives : To assess and evaluate a door-to-door COVID-19 testing initiative that allocates visits by community health workers by selecting households in areas with a high number of index cases, by using uncertainty sampling for areas where the positivity rate may be highest, and by relying on local knowledge of the health workers.

Design Setting and Participants : This cohort study was performed from December 18, 2020, to February 18, 2021. Community health workers visited households in neighborhoods in East San Jose, California, based on index cases or uncertainty sampling while retaining discretion to use local knowledge to administer tests. The health workers, also known as promotores de salud (hereinafter referred to as promotores) spent a mean of 4 days a week conducting door-to-door COVID-19 testing during the 2-month study period. All residents of East San Jose were eligible for COVID-19 testing. The promotores were selected from the META cooperative (Mujeres Empresarias Tomando Acción [Entrepreneurial Women Taking Action]).

Interventions : The promotores observed self-collection of anterior nasal swab samples for SARS-CoV-2 reverse transcriptase-polymerase chain reaction tests.

Main Outcomes and Measures : A determination of whether door-to-door COVID-19 testing was associated with an increase in the overall number of tests conducted, the demographic distribution of the door-to-door tests vs local testing sites, and the difference in positivity rates among the 3 door-to-door allocation strategies.

Results : A total of 785 residents underwent door-to-door testing, and 756 were included in the analysis. Among the 756 individuals undergoing testing (61.1% female; 28.2% aged 45-64 years), door-to-door COVID-19 testing reached different populations than standard public health surveillance, with 87.6% (95% CI, 85.0%-89.8%) being Latinx individuals. The closest available testing site only reached 49.0% (95% CI, 48.3%-49.8%) Latinx individuals. Uncertainty sampling provided the most effective allocation, with a 10.8% (95% CI, 6.8%-16.0%) positivity rate, followed by 6.4% (95% CI, 4.1%-9.4%) for local knowledge, and 2.6% (95% CI, 0.7%-6.6%) for index area selection. The intervention was also associated with increased overall testing capacity by 60% to 90%, depending on the testing protocol.

Conclusions and Relevance : In this cohort study of 785 participants, uncertainty sampling, which has not been used conventionally in public health, showed promising results for allocating testing resources. Community-based door-to-door interventions and leveraging of community knowledge were associated with reduced demographic disparities in testing.

Chugg Ben, Lu Lisa, Ouyang Derek, Anderson Benjamin, Ha Raymond, D’Agostino Alexis, Sujeer Anandi, Rudman Sarah L, Garcia Analilia, Ho Daniel E

2021-Aug

Dermatology Dermatology

COVID-19 and Artificial Intelligence: Experts and Dermatologists Perspective.

In Journal of cosmetic dermatology ; h5-index 25.0

Artificial intelligence (AI) has an important role to play in future healthcare offerings. Machine learning and artificial neural networks are subsets of AI that refer to the incorporation of human intelligence into computers to think and behave like humans. While it has not yet achieved its full potential, AI is being used to combat coronavirus disease on multiple fronts. AI has made its impact in predicting disease onset by issuing early warnings and alerts, monitoring, forecasting the spread of disease and supporting therapy. In addition, AI has helped us to build a model of a virtual protein structure and has played a role in teaching as well as social control. The coronavirus pandemic has rendered the entire world immobile, crashing economies, industries as well as healthcare. Telemedicine or tele-dermatology for dermatologists has become one of the most common solutions to tackle this crisis while adhering to social distancing for consultations. Full potential of AI is yet to be realized. Expert data collection, analysis, and implementation are needed to improve this advancement.

Goldust Yaser, Sameem Farah, Mearaj Samia, Gupta Atula, Patil Anant, Goldust Mohamad

2022-Aug-17

Artificial Intelligence, COVID-19, Dermatology, Telemedicine

General General

Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2).

In Health and technology

This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms - i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.

Radanliev Petar, De Roure David

2022-Aug-12

Covid-19, Disease X, Healthcare systems, Self-adaptative artificial intelligence, Self-optimising artificial intelligence

Public Health Public Health

Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh.

In Parasite epidemiology and control

Digital technologies are the need of today to predict, prevent and control emerging infectious diseases. Bangladesh is one of the world's poorest and most densely populated countries and faces a double burden of two deadly diseases, COVID-19 and dengue. In response to both these diseases, the absence of a digital healthcare system and insufficient preparedness, lack of public awareness pose unique challenges and a large threat to the population, resulting in epidemics of escalating severity. This paper suggests a digital health care and surveillance system based on the internet of things (IoT) and artificial intelligence (AI) for timely identification of COVID-19 and dengue cases and improving the prevention and control strategies in the country.

Rahman Md Siddikur, Safa Nujhat Tabassum, Sultana Sahara, Salam Samira, Karamehic-Muratovic Ajlina, Overgaard Hans J

2022-Aug

COVID-19, Dengue, Health care, Prediction, Surveillance

General General

Towards an AI-Driven Marketplace for Small Businesses During COVID-19.

In SN computer science

With the introduction of new COVID-19 variants such as Delta and Omicron, small businesses have been tasked with navigating a constantly changing business environment. Furthermore, due to supply chain issues, shortages of various critical products negatively affect businesses of all sizes and industries. However, continued innovation in Computer Science, specifically in sub-fields of Artificial Intelligence (AI), such as natural language processing (NLP), has created significant value for businesses through helpful data-driven features. To this end, we propose a platform utilizing AI-driven tools to help build an effective business-to-business (B2B) platform. The proposed platform aims to automate much of the market research which goes into selecting products and platform users during times of distress while still providing an intuitive e-commerce interface. There are three primary novel components to this platform. The first of these components is the Buyer's Club (BC), which allows customers to pool resources to purchase bulk orders at a reduced cost. The second component is an automated system utilizing Natural Language Processing (NLP) to detect trending disaster news topics. Disaster topic detection can be applied to inform buyers and suppliers on adapting to changing market conditions and has been shown to match closely with Google Trends data. The third component is a regulation matching system, using a custom data set to help inform customers when purchasing products. Such guidance is necessary to comply with a regulatory environment that will be irregular for the foreseeable future.

Coltey Erik, Alonso Daniela, Vassigh Shahin, Chen Shu-Ching

2022

Artificial intelligence, COVID-19, Data mining, Data retrieval, Mobile, Natural language processing, User interface

General General

Human sentiments monitoring during COVID-19 using AI-based modeling.

In Procedia computer science

The whole world is facing health challenges due to wide spread of COVID-19 pandemic. To control the spread of COVID-19, the development of its vaccine is the need of hour. Considering the importance of the vaccines, many industries have put their efforts in vaccine development. The higher immunity against the COVID can be achieved by high intake of the vaccines. Therefore, it is important to analysis the people's behaviour and sentiments towards vaccines. Today is the era of social media, where people mostly share their emotions, experience, or opinions about any trending topic in the form of tweets, comments or posts. In this study, we have used the freely available COVID-19 vaccines dataset and analysed the people reactions on the vaccine campaign using artificial intelligence methods. We used TextBlob() function of python and found out the polarity of the tweets. We applied the BERT model and classify the tweets into negative and positive classes based on their polarity values. The classification results show that BERT has achieved maximum values of precision, recall and F score for both positive and negative sentiment classification.

Umair Areeba, Masciari Elio

2022

AI based modeling, COVID-19, Sentiments monitoring, Social media data analysis, Vaccine hesitancy, Vaccines campaign

General General

Highlighting artificial intelligence roles in business area Amid the COVID-19 crisis.

In Procedia computer science

PURPOSE : The fast development of technology and data has fueled the use of artificial intelligence (AI) in the business area, but there has been no comprehensive review to guide and assess this evolution, especially in the context of Covid-19 crisis. Our objective is to highlight the nature and scale of AI research in the business area, during the COVID-19 Pandemic.

METHODS : We performed a scoping review and searched two literature databases (Scopus and MDPI) for terms related to AI and Covid-19 by focusing on scientific papers published in the field of business. We used multiple tools (Endnote, Covidence) for titles and abstracts selection, followed by full-text screening. The studies must include research on artificial intelligence and Covid-19, and then be published in English-language, between March 2020 and March 2022.

RESULTS : 31 studies met eligibility criteria (of 391 studies selected). Most of the published articles refer to conceptual analysis or quantitative works, the rest of the articles used a literature review except 4 articles published using a qualitative method of analysis. In addition, we observe an evolution of the total number of publications for the 31 articles included in the analysis.

CONCLUSIONS : Studying AI in the business field amid the covid-19 crisis is at an early stage of maturity, especially with the use of new AI technologies.). For the field to progress, more studies are needed in the next few years.

Aziki Abdellatif, Fadili Moulay Hachem

2022

AI;Business area, Artificial intelligence, Machine learning, covid-19 pandemic

General General

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.

In Experimental biology and medicine (Maywood, N.J.)

Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.

Xia Tong, Han Jing, Mascolo Cecilia

2022-Aug-16

Respiratory abnormity, artificial intelligence, auscultation, automatic disease diagnosis, machine learning, respiratory sound

Surgery Surgery

A Novel Convolutional Neural Network Model as an Alternative Approach to Bowel Preparation Evaluation Before Colonoscopy in the COVID-19 Era: A Multicenter, Single-Blinded, Randomized Study.

In The American journal of gastroenterology

OBJECTIVE : Adequate bowel preparation is key to a successful colonoscopy, which is necessary for detecting adenomas and preventing colorectal cancer. We developed an artificial intelligence (AI) platform using a convolutional neural network (CNN) model (AI-CNN model) to evaluate the quality of bowel preparation before colonoscopy.

DESIGN : This was a colonoscopist-blinded, randomized study. Enrolled patients were randomized into an experimental group, in which our AI-CNN model was used to evaluate the quality of bowel preparation (AI-CNN group), or a control group, which performed self-evaluation per routine practice (Control group). The primary outcome was the consistency (homogeneity) between the results of the two methods. The secondary outcomes included the quality of bowel preparation according to the Boston Bowel Preparation Scale (BBPS), the polyp detection rate (PDR), and the adenoma detection rate (ADR).

RESULTS : A total of 1,434 patients were enrolled (AI-CNN, n = 730; Control, n = 704). No significant difference was observed between the evaluation results ("pass" or "not pass") of the groups in terms of the adequacy of bowel preparation as represented by BBPS scores. The mean BBPS scores, PDRs, and ADRs were similar between the groups. These results indicated that the AI-CNN model and routine practice were generally consistent in the evaluation of bowel preparation quality. However, the mean BBPS score of patients with "pass" results were significantly higher in the AI-CNN group than in the Control group, indicating that the AI-CNN model may further improve the quality of bowel preparation in patients exhibiting adequate bowel preparation.

CONCLUSION : The novel AI-CNN model, which demonstrated comparable outcomes to the routine practice, may serve as an alternative approach for evaluating bowel preparation quality before colonoscopy.

Lu Yang-Bor, Lu Si-Cun, Huang Yung-Ning, Cai Shun-Tian, Le Puo-Hsien, Hsu Fang-Yu, Hu Yan-Xing, Hsieh Hui-Shan, Chen Wei-Ting, Xia Gui-Li, Xu Hong-Zhi, Gong Wei

2022-Jul-04

Public Health Public Health

Profiles of Burnout and Response to the COVID-19 Pandemic Among General Surgery Residents at a Large Academic Training Program.

In Surgical innovation

BACKGROUND : COVID-19 has placed demands on General Surgery residents, who are already at high risk of burnout. This study examined the pandemic's impact on burnout and wellness among General Surgery residents at a large training program.

METHODS : General Surgery residents at our institution completed a survey focused on self-reported burnout, mental health, perceptions of wellness resources, and changes in activities during the pandemic. Burnout was measured using the Maslach Burnout Inventory (MBI). Unsupervised machine learning (k-means clustering) was used to identify profiles of burnout and comparisons between profiles were made.

RESULTS : Of 82 eligible residents, 51 completed the survey (62% response rate). During COVID-19, 63% of residents had self-described burnout, 43% had depression, 18% acknowledged binge drinking/drug use, and 8% had anxiety. There were no significant differences from pre-pandemic levels (p all >.05). Few residents perceived available wellness resources as effective (6%). Based on MBI scores, the clustering analysis identified three clusters, characterized as "overextended", "engaged", and "ineffective". Engaged residents had the least concerning MBI scores and were significantly more likely to exercise, retain social contact during the pandemic, and had less self-reported anxiety or depression. Research residents were overrepresented in the ineffective cluster (46%), which had high rates of self-reported burnout (77%) and was characterized by the lowest personal accomplishment scores. Rates of self-reported burnout for overextended and engaged residents were 73% and 48%, respectively.

CONCLUSION : Surgical residents have high rates of self-reported burnout and depression during the COVID-19 pandemic. Clusters of burnout may offer targets for individualized intervention.

Nguyen May-Anh, Castelo Matthew, Greene Brittany, Lu Justin, Brar Savtaj, Reel Emma, Cil Tulin D

2022-Aug-16

COVID-19, burnout, general surgery, internship and residency

General General

Evaluation of the role of vaccination in the COVID-19 pandemic based on the data from the 50 U.S. States.

In Computational and structural biotechnology journal

Vaccination is considered as the ultimate weapon to end the pandemic. However, the role of vaccines in the pandemic remains controversial. To explore the impact of vaccination on the COVID-19 pandemic, we used logistic regression models to predict numbers of population-adjusted confirmed cases, deaths, intensive care unit (ICU) cases, case fatality rates and ICU admission rates of COVID-19 in the 50 U.S. states, based on 17 related variables. The logistic regression analysis showed that percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths and case fatality rates but showed no significant correlation with numbers of confirmed cases or ICU cases, or ICU admission rates. The Spearman correlation analysis showed that the percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths, ICU cases, ICU case rates, and case fatality rates but showed no significant correlation with numbers of confirmed cases. The number of deaths and mortality in the group after the vaccine usage were significantly lower than those in the group before the vaccine usage. However, after delta became the dominant strain, there were no longer significant differences in the number of deaths and the mortality rate between before and after delta became the dominant strain, although vaccines were used in both periods. Vaccination can significantly reduce COVID-19 deaths and mortality, while it cannot reduce the risk of COVID-19 infection. In addition to vaccination, other measures, such as social distancing, remain important in containing COVID-19 transmission and lower the risk of COVID-19 severe outcomes.

Nie Rongfang, Abdelrahman Zeinab, Liu Zhixian, Wang Xiaosheng

2022

AUC, The area under the receiver operating characteristic curve, CDC, The Centers for Disease Control and Prevention, COVID-19, COVID-19, Coronavirus disease 2019, ICU, Intensive care unit, Machine learning, Protective and risk factors, SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2, Vaccination, Virus variant

General General

Modeling the optimization of COVID-19 pooled testing: How many samples can be included in a single test?

In Informatics in medicine unlocked

Objectives : This study tries to answer the crucial question of how many biological samples can be optimally included in a single test for COVID-19 pooled testing.

Methods : It builds a novel theoretical model which links the local population to be tested in a region, the number of biological samples included in a single test, the "attitude" toward resource cost saving and time taken in a single test, as well as the corresponding resource cost function and time function, together. The numerical simulation results are then used to formulate the resource cost function as well as the time function. Finally, a loss function to be minimized is constructed and the optimal number of samples included is calculated.

Results : In a numerical example, we consider a region of 1 million population which needs to be tested for the infection of COVID-19. The solution calculates the optimal number of biological samples included in a single test as 4.254 when the time taken is given the weight of 50% under the infection probability of 10%. Other combinations of numerical results are also presented.

Conclusions : As we can see in our simulation results, given the infection probability at 10%, setting the number of biological samples included in a single test (in the integer level) at [4,6] is reasonable for a wide range of the subjective attitude between time and resource costs. Therefore, in the current practice, 5-mixed samples would sound better than the commonly used 10-mixed samples.

Liu Lu

2022

COVID-19, Machine learning, Pooled testing, Resource cost function, Time function

General General

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection.

In Computational and structural biotechnology journal

Background : A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection.

Methods : The surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information.

Results : A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5.

Conclusions : In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

Sufriyana Herdiantri, Salim Hotimah Masdan, Muhammad Akbar Reza, Wu Yu-Wei, Su Emily Chia-Yu

2022

Biomarker, COVID-19, Machine learning, Preeclampsia, Transcriptome

General General

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19.

In SN computer science

The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

Thirumahal R, Sudha Sadasivam G, Shruti P

2022

Attribute mapping, Data heterogeneity, Global and local schemas, Healthcare domain, Ontology based data retrieval

General General

An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution.

In Computational intelligence and neuroscience

The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients' chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world.

Biradar Vidyadevi G, Alqahtani Mejdal A, Nagaraj H C, Ahmed Emad A, Tripathi Vikas, Botto-Tobar Miguel, Atiglah Henry Kwame

2022

General General

Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN.

In Computational intelligence and neuroscience

Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact precision. The typical detection method for COVID-19 infection is the RT-PCR but it is a rather expensive method which is also invasive and has a high margin of error. Radiographies are a good alternative for COVID-19 detection given the experience of the radiologist and his learning capabilities. To make an accurate detection from chest X-Rays, deep learning technologies can be involved to analyze the radiographs, learn distinctive patterns of coronavirus' presence, find these patterns in the tested radiograph, and determine whether the sample is actually COVID-19 positive or negative. In this study, we propose a model based on deep learning technology using Convolutional Neural Networks and training it on a dataset containing a total of over 35,000 chest X-Ray images, nearly 16,000 for COVID-19 positive images, 15,000 for normal images, and 5,000 for pneumonia-positive images. The model's performance was assessed in terms of accuracy, precision, recall, and F1-score, and it achieved 99% accuracy, 0.98 precision, 1.02 recall, and 99.0% F1-score, thus outperforming other deep learning models from other studies.

Alharbi Rawan Saqer, Alsaadi Hadeel Aysan, Manimurugan S, Anitha T, Dejene Minilu

2022

General General

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing.

In Computational intelligence and neuroscience

Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

Sadad Tariq, Bukhari Syed Ahmad Chan, Munir Asim, Ghani Anwar, El-Sherbeeny Ahmed M, Rauf Hafiz Tayyab

2022

General General

Multistage Framework for Automatic Face Mask Detection Using Deep Learning.

In Computational intelligence and neuroscience

The whole world is fighting as one against a deadly virus. COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the previous one. Scores of human lives are lost while the post-COVID-19 complications are on a rise. Monitoring the behaviour of people in public places and offices is necessary to mitigate the transmission of COVID-19 among humans. In this work, a low-cost, lightweight two-stage face mask detection model is proposed. In the first stage, the model checks if a face mask is worn. In the second stage, it detects if the mask is worn appropriately, by classifying and labelling them. The proposed models are trained to detect faces with and without masks for varied inputs such as images, recorded videos, and live streaming videos where it can efficiently detect multiple faces at once. The efficacy of the proposed approach is tested against conventional datasets as well as our proposed dataset, which includes no masks, surgical masks, and nonsurgical masks. In this work, multiple CNN models like MobileNetV2, ResNet50V2, and InceptionV3 have been considered for training and are evaluated based on transfer learning. We further rely on MobileNetV2 as the backbone model since it has an accuracy of 98.44%.

K N Sowmya, P M Rekha, Kumari Trishala, Debtera Baru

2022

General General

DCNN-FuzzyWOA: Artificial Intelligence Solution for Automatic Detection of COVID-19 Using X-Ray Images.

In Computational intelligence and neuroscience

Artificial intelligence (AI) techniques have been considered effective technologies in diagnosing and breaking the transmission chain of COVID-19 disease. Recent research uses the deep convolution neural network (DCNN) as the discoverer or classifier of COVID-19 X-ray images. The most challenging part of neural networks is the subject of their training. Descent-based (GDB) algorithms have long been used to train fullymconnected layer (FCL) at DCNN. Despite the ability of GDBs to run and converge quickly in some applications, their disadvantage is the manual adjustment of many parameters. Therefore, it is not easy to parallelize them with graphics processing units (GPUs). Therefore, in this paper, the whale optimization algorithm (WOA) evolved by a fuzzy system called FuzzyWOA is proposed for DCNN training. With accurate and appropriate tuning of WOA's control parameters, the fuzzy system defines the boundary between the exploration and extraction phases in the search space. It causes the development and upgrade of WOA. To evaluate the performance and capability of the proposed DCNN-FuzzyWOA model, a publicly available database called COVID-Xray-5k is used. DCNN-PSO, DCNN-GA, and LeNet-5 benchmark models are used for fair comparisons. Comparative parameters include accuracy, processing time, standard deviation (STD), curves of ROC and precision-recall, and F1-Score. The results showed that the FuzzyWOA training algorithm with 20 epochs was able to achieve 100% accuracy, at a processing time of 880.44 s with an F1-Score equal to 100%. Structurally, the i-6c-2s-12c-2s model achieved better results than the i-8c-2s-16c-2s model. However, the results of using FuzzyWOA for both models have been very encouraging compared to particle swarm optimization, genetic algorithm, and LeNet-5 methods.

Saffari Abbas, Khishe Mohammad, Mohammadi Mokhtar, Hussein Mohammed Adil, Rashidi Shima

2022

General General

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model.

In Frontiers in public health

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky-Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19.

Khan Dost Muhammad, Ali Muhammad, Iqbal Nadeem, Khalil Umair, Aljohani Hassan M, Alharthi Amirah Saeed, Afify Ahmed Z

2022

ARIMA, COVID-19, augmented Dicky-Fuller test, ensemble empirical mode decomposition, error trend seasonal model, prediction

Public Health Public Health

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.

In Frontiers in public health

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

Cheatham Susan, Kummervold Per E, Parisi Lorenza, Lanfranchi Barbara, Croci Ileana, Comunello Francesca, Rota Maria Cristina, Filia Antonietta, Tozzi Alberto Eugenio, Rizzo Caterina, Gesualdo Francesco

2022

Transformer model, artificial intelligence, machine learning, vaccination hesitancy, vaccines

Public Health Public Health

Improved COVID-19 detection with chest x-ray images using deep learning.

In Multimedia tools and applications

The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.

Gupta Vedika, Jain Nikita, Sachdeva Jatin, Gupta Mudit, Mohan Senthilkumar, Bajuri Mohd Yazid, Ahmadian Ali

2022-Aug-09

COVID-19, Chest X-ray, Convolutional neural network (CNN), Deep learning, Multi-class classification, Transfer learning

General General

An investigation on trust in AI-enabled collaboration: Application of AI-Driven chatbot in accommodation-based sharing economy.

In Electronic commerce research and applications

Several measures taken to control the spread of the COVID-19 pandemic have severely disrupted the accommodation sharing sector. This study attempts to find solutions to aid the recovery of the accommodation sharing sector via team efforts. Accordingly, we focus on the integration of artificial intelligence (AI) and collaboration. Despite the significant developments in AI technologies, there exists no research considering the application of AI in team collaboration. Utilizing the design science research method and collaboration engineering, we developed an AI-driven prototype system, AI-Driven, for collaboration process recommendation. Qualitative results show that the newly developed tool for collaboration process recommendation has achieved satisfactory performance. Furthermore, we investigated the antecedents and outcomes of trust in the AI-driven collaboration context. From a practical perspective, we propose several solutions to the challenges looming over the accommodation sharing sector according to collaboration deliverables. Furthermore, a system prototype was developed to facilitate collaboration process recommendation and provide procedural guidance.

Cheng Xusen, Zhang Xiaoping, Yang Bo, Fu Yaxin

Accommodation-based sharing economy, Collaboration engineering, Design science research, Human-AI interaction, collaboration process, Trust

Public Health Public Health

Automatic segmentation of COVID-19 from computed tomography images using modified U-Net model-based majority voting approach.

In Neural computing & applications

The coronavirus disease (COVID-19) is an important public health problem that has spread rapidly around the world and has caused the death of millions of people. Therefore, studies to determine the factors affecting the disease, to perform preventive actions and to find an effective treatment are at the forefront. In this study, a deep learning and segmentation-based approach is proposed for the detection of COVID-19 disease from computed tomography images. The proposed model was created by modifying the encoder part of the U-Net segmentation model. In the encoder part, VGG16, ResNet101, DenseNet121, InceptionV3 and EfficientNetB5 deep learning models were used, respectively. Then, the results obtained with each modified U-Net model were combined with the majority vote principle and a final result was reached. As a result of the experimental tests, the proposed model obtained 85.03% Dice score, 89.13% sensitivity and 99.38% specificity on the COVID-19 segmentation test dataset. The results obtained in the study show that the proposed model will especially benefit clinicians in terms of time and cost.

Uçar Murat

2022-Aug-06

COVID-19, Deep learning, Majority voting, Segmentation

General General

Multiclass sentiment analysis on COVID-19-related tweets using deep learning models.

In Neural computing & applications

COVID-19 is an infectious disease with its first recorded cases identified in late 2019, while in March of 2020 it was declared as a pandemic. The outbreak of the disease has led to a sharp increase in posts and comments from social media users, with a plethora of sentiments being found therein. This paper addresses the subject of sentiment analysis, focusing on the classification of users' sentiment from posts related to COVID-19 that originate from Twitter. The period examined is from March until mid-April of 2020, when the pandemic had thus far affected the whole world. The data is processed and linguistically analyzed with the use of several natural language processing techniques. Sentiment analysis is implemented by utilizing seven different deep learning models based on LSTM neural networks, and a comparison with traditional machine learning classifiers is made. The models are trained in order to distinguish the tweets between three classes, namely negative, neutral and positive.

Vernikou Sotiria, Lyras Athanasios, Kanavos Andreas

2022-Aug-06

Big data, COVID-19, Deep learning, LSTM, Natural language processing, Sentiment analysis, Social media, Twitter, Word embeddings

Radiology Radiology

Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry.

In Respirology (Carlton, Vic.)

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

Vliegenthart Rozemarijn, Fouras Andreas, Jacobs Colin, Papanikolaou Nickolas

2022-Aug-14

computed tomography, deep learning, lung cancer, lung nodules, machine learning, radiomics, x-ray velocimetry

General General

Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers.

In Frontiers in psychology ; h5-index 92.0

COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children's and young people's mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children's and youths' mental states, a combination of heterogeneous data from practically all feature groups is required.

Luo Xuan, Huang Youlian

2022

COVID-19, artificial intelligence, clustering, feature selection, machine learning, mental health, mood state

Public Health Public Health

Economic Resilience in Times of Public Health Shock: The Case of the US States.

In Research in economics = Ricerche economiche

Does adoption of social distancing policies during a health crisis, e.g., COVID-19, hurt economies compared to those that did not? Based on a machine learning approach in the intermediate stage We applied the generalized synthetic control method to assess the economic impact. We do so by exploiting the variations in states' responses to policy. Cross-validation, a popular machine learning technique, is used in the preliminary stage to create the "counterfactual" for the adopting states-how these states "would have behaved" if they had not adopted lockdown/stay-at-home orders. We categorize states that have undertaken social distancing policies as the treatment group and those that have not as control, and we use the state time-period for fixed effects, adjusted to eliminate potential selection bias and endogeneity. We find significant and intuitively explicable policy impacts on some states, such as West Virginia, but none at the aggregate level, suggesting that the social distancing policy might not hurt the overall economy as anticipated by some quarters. We construct a resilience index using the magnitude and significance of the impact of the social distancing measures to identify the states that exhibited stronger resilience and ranked them based on their responses. These findings provide policymakers and businesses with insights that may assist them in better preparing for shocks.

Osman Syed Muhammad Ishraque, Islam Faridul, Sakib Nazmus

2022-Aug-10

COVID-19, Economic Resilience, Generalized Synthetic Control, Machine Learning

Internal Medicine Internal Medicine

Population-wide persistent hemostatic changes after vaccination with ChAdOx1-S.

In Frontiers in cardiovascular medicine

Various vaccines were developed to reduce the spread of the Severe Acute Respiratory Syndrome Cov-2 (SARS-CoV-2) virus. Quickly after the start of vaccination, reports emerged that anti-SARS-CoV-2 vaccines, including ChAdOx1-S, could be associated with an increased risk of thrombosis. We investigated the hemostatic changes after ChAdOx1-S vaccination in 631 health care workers. Blood samples were collected 32 days on average after the second ChAdOx1-S vaccination, to evaluate hemostatic markers such as D-dimer, fibrinogen, α2-macroglobulin, FVIII and thrombin generation. Endothelial function was assessed by measuring Von Willebrand Factor (VWF) and active VWF. IL-6 and IL-10 were measured to study the activation of the immune system. Additionally, SARS-CoV-2 anti-nucleoside and anti-spike protein antibody titers were determined. Prothrombin and fibrinogen levels were significantly reduced after vaccination (-7.5% and -16.9%, p < 0.0001). Significantly more vaccinated subjects were outside the normal range compared to controls for prothrombin (42.1% vs. 26.4%, p = 0.026) and antithrombin (23.9% vs. 3.6%, p = 0.0010). Thrombin generation indicated a more procoagulant profile, characterized by a significantly shortened lag time (-11.3%, p < 0.0001) and time-to-peak (-13.0% and p < 0.0001) and an increased peak height (32.6%, p = 0.0015) in vaccinated subjects compared to unvaccinated controls. Increased VWF (+39.5%, p < 0.0001) and active VWF levels (+24.1 %, p < 0.0001) pointed toward endothelial activation, and IL-10 levels were significantly increased (9.29 pg/mL vs. 2.43 pg/mL, p = 0.032). The persistent increase of IL-10 indicates that the immune system remains active after ChAdOx1-S vaccination. This could trigger a pathophysiological mechanism causing an increased thrombin generation profile and vascular endothelial activation, which could subsequently result in and increased risk of thrombotic events.

de Laat Bas, Stragier Hendrik, de Laat-Kremers Romy, Ninivaggi Marisa, Mesotten Dieter, Thiessen Steven, Van Pelt Kristien, Roest Mark, Penders Joris, Vanelderen Pascal, Huskens Dana, De Jongh Raf, Laenen Margot Vander, Fivez Tom, Ten Cate Hugo, Heylen Rene, Heylen Line, Steensels Deborah

2022

COVID-19, ChAdOx1-S, hemostasis, thrombin generation, vaccination

General General

An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays.

In Applied soft computing

Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm. We first extract deep features from the pre-trained ResNet50, fine-tuned on a target Pneumonia dataset. Then, we propose a feature selection technique based on particle swarm optimization (PSO), which is modified using a memory-based adaptation parameter, and enriched by incorporating an altruistic behavior into the agents. We name our feature selection method as adaptive and altruistic PSO (AAPSO). The proposed method successfully eliminates non-informative features obtained from the ResNet50 model, thereby improving the Pneumonia detection ability of the overall framework. Extensive experimentation and thorough analysis on a publicly available Pneumonia dataset establish the superiority of the proposed method over several other frameworks used for Pneumonia detection. Apart from Pneumonia detection, AAPSO is further evaluated on some standard UCI datasets, gene expression datasets for cancer prediction and a COVID-19 prediction dataset. The overall results are satisfactory, thereby confirming the usefulness of AAPSO in dealing with varied real-life problems. The supporting source codes of this work can be found at https://github.com/rishavpramanik/AAPSO.

Pramanik Rishav, Sarkar Sourodip, Sarkar Ram

2022-Aug-10

Altruism, Chest X-ray, Deep learning, Feature selection, Particle swarm optimization, Pneumonia

General General

A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms.

In Socio-economic planning sciences

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.

Kamran Mehdi A, Kia Reza, Goodarzian Fariba, Ghasemi Peiman

2022-Aug-08

Artificial intelligence algorithm, Desirability for the vaccination, Distribution-location-allocation, Distributive justice, Simulation model, Stochastic optimization, Vaccine supply chain

General General

Anatomical Feature-Based Lung Ultrasound Image Quality Assessment Using Deep Convolutional Neural Network.

In IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium

Lung ultrasound (LUS) has been used for point-of-care diagnosis of respiratory diseases including COVID-19, with advantages such as low cost, safety, absence of radiation, and portability. The scanning procedure and assessment of LUS are highly operator-dependent, and the appearance of LUS images varies with the probe's position, orientation, and contact force. Karamalis et al. introduced the concept of ultrasound confidence maps based on random walks to assess the ultrasound image quality algorithmically by estimating the per-pixel confidence in the image data. However, these confidence maps do not consider the clinical context of an image, such as anatomical feature visibility and diagnosability. This work proposes a deep convolutional network that detects important anatomical features in an LUS image to quantify its clinical context. This work introduces an Anatomical Feature-based Confidence (AFC) Map, quantifying an LUS image's clinical context based on the visible anatomical features. We developed two U-net models, each segmenting one of the two classes crucial for analyzing an LUS image, namely 1) Bright Features: Pleural and Rib Lines and 2) Dark Features: Rib Shadows. Each model takes the LUS image as input and outputs the segmented regions with confidence values for the corresponding class. The evaluation dataset consists of ultrasound images extracted from videos of two sub-regions of the chest above the anterior axial line from three human subjects. The feature segmentation models achieved an average Dice score of 0.72 on the model's output for the testing data. The average of non-zero confidence values in all the pixels was calculated and compared against the image quality scores. The confidence values were different between different image quality scores. The results demonstrated the relevance of using an AFC Map to quantify the clinical context of an LUS image.

Ravishankar Surya M, Tsumura Ryosuke, Hardin John W, Hoffmann Beatrice, Zhang Ziming, Zhang Haichong K

2021-Sep

Confidence Map, Deep Learning, Image Quality Assessment, Lung Ultrasound

General General

Predicting student performance using sequence classification with time-based windows.

In Expert systems with applications

A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90% for course-specific models.

Deeva Galina, De Smedt Johannes, Saint-Pierre Cecilia, Weber Richard, De Weerdt Jochen

2022-Dec-15

Behavioral patterns, Feature engineering, Machine learning, Sequence mining, Success prediction

General General

Adapting recurrent neural networks for classifying public discourse on COVID-19 symptoms in Twitter content.

In Soft computing

The COVID-19 infection, which began in December 2019, has claimed many lives and impacted all aspects of human life. With time, COVID-19 was identified as a pandemic outbreak by the World Health Organization (WHO), putting massive pressure on global health. During this ongoing pandemic, the exponential growth of social media platforms has provided valuable resources for distributing information, as well as a source for self-reported disease symptoms in public discourse. Therefore, there is an urgent need for effective approaches to detect self-reported symptoms or cases in social media content. In this study, we scrapped public discourse on COVID-19 symptoms in Twitter content. For this, we developed a huge dataset of COVID-19 self-reported symptoms and gold-annotated the tweets into four categories: confirmed, death, suspected, and recovered. Then, we use a machine and deep machine learning models, each with its own set of features, such as feature representation. Furthermore, the experimentations were achieved with recurrent neural networks (RNNs) variants and compared their performance with traditional machine learning algorithms. Experimental results report that optimizing the area under the curve (AUC) enhances model performance, and the long short-term memory (LSTM) has the highest accuracy in detecting COVID-19 symptoms in real-time public messaging. Thus, the LSTM classifier in the proposed pipeline achieves a classification accuracy of 90.7%, outperforming existing state-of-the-art algorithms for multi-class classification.

Amin Samina, Alharbi Abdullah, Uddin M Irfan, Alyami Hashem

2022-Aug-10

COVID-19, Classification, Coronavirus, Deep learning, Pandemic, Recurrent neural networks, Twitter

General General

Role of ethno-phytomedicine knowledge in healthcare of COVID-19: advances in traditional phytomedicine perspective.

In Beni-Suef University journal of basic and applied sciences

Background : Since the outbreak of the COVID-19 virus, ethnomedicinal plants have been used in diverse geographical locations for their purported prophylactic and pharmacological effects. Medicinal plants have been relied on by people around the globe for centuries, as 80% of the world's population rely on herbal medicines for some aspect of their primary health care needs, according to the World Health Organization.

Main body : This review portrays advances in traditional phytomedicine by bridging the knowledge of ethno-phytomedicine and COVID-19 healthcare. Ethnomedicinal plants have been used for symptoms related to COVID-19 as antiviral, anti-infective, anti-inflammatory, anti-oxidant, antipyretic, and lung-gut immune boosters. Traditionally used medicinal plants have the ability to inhibit virus entry and viral assembly, bind to spike proteins, membrane proteins, and block viral replications and enzymes. The efficacy of traditional medicinal plants in the terms of COVID-19 management can be evaluated by in vitro, in vivo as well as different in silico techniques (molecular docking, molecular dynamics simulations, machine learning, etc.) which have been applied extensively to the quest and design of effective biotherapeutics rapidly. Other advances in traditional phytomedicines against COVID-19 are controlled clinical trials, and notably the roles in the gut microbiome. Targeting the gut microbiome via medicinal plants as prebiotics is also found to be an alternative and potential strategy in the search for a COVID-19 combat strategy.

Conclusions : Since medicinal plants are the sources of modern biotherapeutics development, it is essential to build collaborations among ethnobotanists, scientists, and technologists toward developing the most efficient and the safest adjuvant therapeutics against the pandemic of the twenty-first century, COVID-19.

Nasir Ahmed Md, Hughes Kerry

2022

Ethno-phytomedicine, Ethnomedicine, Gut microbiome, Immunomodulation, Medicinal plants, Phytomedicine, SARS-CoV-2, Traditional medicine

General General

CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

In Health and technology

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Jawahar Malathy, L Jani Anbarasi, Ravi Vinayakumar, Prassanna J, Jasmine S Graceline, Manikandan R, Sekaran Rames, Kannan Suthendran

2022-Aug-04

COVID-19, Chest X-ray, Convolutional neural network, Deep learning

General General

Machine learning-based predictive modeling of resilience to stressors in pregnant women during COVID-19: A prospective cohort study.

In PloS one ; h5-index 176.0

During the COVID-19 pandemic, pregnant women have been at high risk for psychological distress. Lifestyle factors may be modifiable elements to help reduce and promote resilience to prenatal stress. We used Machine-Learning (ML) algorithms applied to questionnaire data obtained from an international cohort of 804 pregnant women to determine whether physical activity and diet were resilience factors against prenatal stress, and whether stress levels were in turn predictive of sleep classes. A support vector machine accurately classified perceived stress levels in pregnant women based on physical activity behaviours and dietary behaviours. In turn, we classified hours of sleep based on perceived stress levels. This research adds to a developing consensus concerning physical activity and diet, and the association with prenatal stress and sleep in pregnant women. Predictive modeling using ML approaches may be used as a screening tool and to promote positive health behaviours for pregnant women.

Nichols Emily S, Pathak Harini S, Bgeginski Roberta, Mottola Michelle F, Giroux Isabelle, Van Lieshout Ryan J, Mohsenzadeh Yalda, Duerden Emma G

2022

General General

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies.

In Critical reviews in food science and nutrition ; h5-index 70.0

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.

Hassoun Abdo, Alhaj Abdullah Nour, Aït-Kaddour Abderrahmane, Ghellam Mohamed, Beşir Ayşegül, Zannou Oscar, Önal Begüm, Aadil Rana Muhammad, Lorenzo Jose M, Mousavi Khaneghah Amin, Regenstein Joe M

2022-Aug-11

Artificial intelligence, Industry 4.0, Internet of things, IoT, big data, blockchain, digital transformation, food fraud, real-time surveillance, smart sensors

General General

Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.

In Archives of disease in childhood ; h5-index 49.0

OBJECTIVE : The COVID-19 pandemic and subsequent government restrictions have had a major impact on healthcare services and disease transmission, particularly those associated with acute respiratory infection. This study examined non-identifiable routine electronic patient record data from a specialist children's hospital in England, UK, examining the effect of pandemic mitigation measures on seasonal respiratory infection rates compared with forecasts based on open-source, transferable machine learning models.

METHODS : We performed a retrospective longitudinal study of respiratory disorder diagnoses between January 2010 and February 2022. All diagnoses were extracted from routine healthcare activity data and diagnosis rates were calculated for several diagnosis groups. To study changes in diagnoses, seasonal forecast models were fit to prerestriction period data and extrapolated.

RESULTS : Based on 144 704 diagnoses from 31 002 patients, all but two diagnosis groups saw a marked reduction in diagnosis rates during restrictions. We observed 91%, 89%, 72% and 63% reductions in peak diagnoses of 'respiratory syncytial virus', 'influenza', 'acute nasopharyngitis' and 'acute bronchiolitis', respectively. The machine learning predictive model calculated that total diagnoses were reduced by up to 73% (z-score: -26) versus expected during restrictions and increased by up to 27% (z-score: 8) postrestrictions.

CONCLUSIONS : We demonstrate the association between COVID-19 related restrictions and significant reductions in paediatric seasonal respiratory infections. Moreover, while many infection rates have returned to expected levels postrestrictions, others remain supressed or followed atypical winter trends. This study further demonstrates the applicability and efficacy of routine electronic record data and cross-domain time-series forecasting to model, monitor, analyse and address clinically important issues.

Bowyer Stuart A, Bryant William A, Key Daniel, Booth John, Briggs Lydia, Spiridou Anastassia, Cortina-Borja Mario, Davies Gwyneth, Taylor Andrew M, Sebire Neil J

2022-Aug-10

information technology, respiratory

General General

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images.

In Biocybernetics and biomedical engineering

Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.

Fang Lingling, Wang Xin

2022-Aug-05

Adaptive region enhancement, COVID-19, Deep learning, Dense block, Mixed dataset

General General

Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.

In International journal of applied earth observation and geoinformation : ITC journal

From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.

Zhang Yecheng, Zhang Qimin, Zhao Yuxuan, Deng Yunjie, Zheng Hao

2022-Aug

Coronavirus disease, Deep learning, Design improvement, Incidence prediction, Spatial risk factors

General General

Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19.

In Infectious Disease Modelling

With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period and normal times, respectively. We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40-59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60-79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

Matsunaga Nobuaki, Kamata Keisuke, Asai Yusuke, Tsuzuki Shinya, Sakamoto Yasuaki, Ijichi Shinpei, Akiyama Takayuki, Yu Jiefu, Yamada Gen, Terada Mari, Suzuki Setsuko, Suzuki Kumiko, Saito Sho, Hayakawa Kayoko, Ohmagari Norio

2022-Aug-05

COVID-19, Japan, Machine learning, Risk prediction, Severity

General General

Effective hybrid deep learning model for COVID-19 patterns identification using CT images.

In Expert systems

Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over-and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. These three described pre-trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID-19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state-of-the-art studies. Because of its effectiveness in accurately screening COVID-19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals.

Ibrahim Dheyaa Ahmed, Zebari Dilovan Asaad, Mohammed Hussam J, Mohammed Mazin Abed

2022-May-01

COVID‐19 identification, CT scan images, deep learning models, feature fusion

General General

AI bot to detect fake COVID-19 vaccine certificate.

In IET information security

As the world is now fighting against rampant virus COVID-19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non-COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture-based feature extraction for extracting logo, symbol and for the signature we extract Crest-Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1-score, and computation time over state-of-art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201-LBP) outperforms with an accuracy of 0.94.

Arif Muhammad, Shamsudheen Shermin, Ajesh F, Wang Guojun, Chen Jianer

2022-May-11

COVID‐19, artificial intelligence, deep learning, forged certificate, vaccine certificate

Dermatology Dermatology

Partnering with a senior living community to optimise teledermatology via full body skin screening during the COVID-19 pandemic: A pilot programme.

In Skin health and disease

Background : Elderly patients in senior communities faced high barriers to care during the COVID-19 pandemic, including increased vulnerability to COVID-19, long quarantines for clinic visits, and difficulties with telemedicine adoption.

Objective : To pilot a new model of dermatologic care to overcome barriers for senior living communities during the COVID-19 pandemic and assess patient satisfaction.

Methods : From 16 November 2020 to 9 July 2021, this quality improvement programme combined in-residence full body imaging with real-time outlier lesion identification and virtual teledermatology. Residents from the Sequoias Portola Valley Senior Living Retirement Community (Portola Valley, California) voluntarily enroled in the Stanford Skin Scan Programme. Non-physician clinical staff with a recent negative COVID-19 test travelled on-site to obtain in-residence full body photographs using a mobile app-based system on an iPad called SkinIO that leverages deep learning to analyse patient images and suggest suspicious, outlier lesions for dermoscopic photos. A single dermatologist reviewed photographs with the patient and provided recommendations via a video visit. Objective measures included follow-up course and number of skin cancers detected. Subjective findings were obtained through patient experience surveys.

Results : Twenty-seven individuals participated, three skin cancers were identified, with 11 individuals scheduled for a follow up in-person visit and four individuals starting home treatment. Overall, 88% of patients were satisfied with the Skin Scan programme, with 77% likely to recommend the programme to others. 92% of patients agreed that the Skin Scan photographs were representative of their skin. In the context of the COVID-19 pandemic, 100% of patients felt the process was safer or comparable to an in-person visit. Despite overall appreciation for the programme, 31% of patients reported that they would prefer to see dermatologist in-person after the pandemic.

Conclusions : This programme offers a framework for how a hybrid skin scan programme may provide high utility for individuals with barriers to accessing in-person clinics.

Trinh Pavin, Yekrang Kiana, Phung Michelle, Pugliese Silvina, Chang Anne Lynn S, Bailey Elizabeth E, Ko Justin M, Sarin Kavita Y

2022-Jun-27

General General

LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.

In International journal of imaging systems and technology

The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.

Kumar Sachin, Shastri Sourabh, Mahajan Shilpa, Singh Kuljeet, Gupta Surbhi, Rani Rajneesh, Mohan Neeraj, Mansotra Vibhakar

2022-Jun-11

COVID‐19, LiteCovidNet, chest X‐ray, classification, deep neural network

Public Health Public Health

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.

In International journal of imaging systems and technology

Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT-PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time-consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre-trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state-of-the-art segmentation methods was also performed. The experiments were carried out using a fivefold cross-validation technique on a publicly available database containing 100 single-slice CT scans from >40 patients with COVID-19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross-validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

Polat Hasan

2022-Jun-11

COVID‐19, DeepLabV3 +, ResNet, computed tomography, deep learning, segmentation

Public Health Public Health

Progress in COVID research and developments during pandemic.

In View (Beijing, China)

The pandemic respiratory disease COVID-19 has spread over the globe within a small span of time. Generally, there are two important points are being highlighted and considered towards the successful diagnosis and treatment process. The first point includes the reduction of the rate of infections and the next one is the decrease of the death rate. The major threat to public health globally progresses due to the absence of effective medication and widely accepted immunization for the COVID-19. Whereas, understanding of host susceptibility, clinical features, adaptation of COVID-19 to new environments, asymptomatic infection is difficult and challenging. Therefore, a rapid and an exact determination of pathogenic viruses play an important role in deciding treatments and preventing pandemic to save the people's lives. It is urgent to fix a standardized diagnostic approach for detecting the COVID-19. Here, this systematic review describes all the current approaches using for screening and diagnosing the COVID-19 infectious patient. The renaissance in pathogen due to host adaptability and new region, facing creates several obstacles in diagnosis, drug, and vaccine development process. The study shows that adaptation of accurate and affordable diagnostic tools based on candidate biomarkers using sensor and digital medicine technology can deliver effective diagnosis services at the mass level. Better prospects of public health management rely on diagnosis with high specificity and cost-effective manner along with multidisciplinary research, specific policy, and technology adaptation. The proposed healthcare model with defined road map represents effective prognosis system.

Shukla Sudheesh K, Patra Santanu, Das Trupti R, Kumar Dharmesh, Mishra Anshuman, Tiwari Ashutosh

2022-Jul-20

COVID science and technology, COVID‐19 diagnosis, artificial intelligence, corona virus, pandemic years, respiratory tract infection, serological test

General General

Real-time COVID-19 detection over chest x-ray images in edge computing.

In Computational intelligence

Severe Coronavirus Disease 2019 (COVID-19) has been a global pandemic which provokes massive devastation to the society, economy, and culture since January 2020. The pandemic demonstrates the inefficiency of superannuated manual detection approaches and inspires novel approaches that detect COVID-19 by classifying chest x-ray (CXR) images with deep learning technology. Although a wide range of researches about bran-new COVID-19 detection methods that classify CXR images with centralized convolutional neural network (CNN) models have been proposed, the latency, privacy, and cost of information transmission between the data resources and the centralized data center will make the detection inefficient. Hence, in this article, a COVID-19 detection scheme via CXR images classification with a lightweight CNN model called MobileNet in edge computing is proposed to alleviate the computing pressure of centralized data center and ameliorate detection efficiency. Specifically, the general framework is introduced first to manifest the overall arrangement of the computing and information services ecosystem. Then, an unsupervised model DCGAN is employed to make up for the small scale of data set. Moreover, the implementation of the MobileNet for CXR images classification is presented at great length. The specific distribution strategy of MobileNet models is followed. The extensive evaluations of the experiments demonstrate the efficiency and accuracy of the proposed scheme for detecting COVID-19 over CXR images in edge computing.

Xu Weijie, Chen Beijing, Shi Haoyang, Tian Hao, Xu Xiaolong

2022-Apr-30

CNN, COVID‐19, CXR images, edge computing

General General

A deep learning-based approach for diagnosing COVID-19 on chest x-ray images, and a test study with clinical experts.

In Computational intelligence

Pneumonia is among the common symptoms of the virus that causes COVID-19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x-ray (CXR) is a fast, low-cost, and practical method widely used in this field. The fact that different pathogens other than COVID-19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID-19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID-19, and other viral-induced pneumonia. In the classifications performed with five different state-of-art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.

Sevli Onur

2022-May-17

COVID‐19 diagnosis, chest x‐ray analysis, convolutional neural network, pneumonia detection

General General

Covid-19 detection from radiographs by feature-reinforced ensemble learning.

In Concurrency and computation : practice & experience

The coronavirus (Covid-19) epidemic continues to have a negative influence on the global population's well-being and health. Scientists in many fields around the world are working non-stop to find a solution to the prevention of this epidemic. In the field of computer science, this struggle is supported by studies on especially the analysis of X-ray and CT images with artificial intelligence. In this study, two different ensemble learning models, including deep learning and a combination of machine learning methods, are presented for the detection of SARS-CoV-2 infection from X-ray images. The main purpose of this study is to increase the classification ability of Residual Convolutional Neural Network (ResCNN), which is used as a deep learning method, with the assist of machine learning algorithms and extracted features from images. The proposed models were validated on a total of 5228 chest X-ray images categorized as Normal, Pneumonia, and Covid-19. The images in the dataset were sized in four different ways, 32 × 32, 64 × 64, 128 × 128, and 256 × 256, in order to analyze the validity of the proposed models in more detail. These four datasets were partitioned with the 10-fold cross-validation technique and converted into a total of 40 training and test data. Both proposed models use features derived from the ResCNN as the basis and test a certain number of machine learning algorithms with a majority voting technique by dividing them into subsets. In the architecture of the second model, it combines the features extracted from the Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) methods in addition to the features obtained from the ResCNN. It has been seen that the classification ability of both proposed models is better than the ResCNN in the experiments. In particular, the second model gives a similar classification score even though it is tested with images four-times smaller (e.g., 32 × 32 vs. 128 × 128) than those used in the ResCNN. This shows that the model can give ideal results with lower computational cost.

Elen Abdullah

2022-Jul-06

Covid‐19, X‐ray images, convolutional neural network, histogram‐oriented gradients, local binary patterns, machine learning

General General

Computational Scientific Discovery in Psychology.

In Perspectives on psychological science : a journal of the Association for Psychological Science

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

Bartlett Laura K, Pirrone Angelo, Javed Noman, Gobet Fernand

2022-Aug-09

AI, computational scientific discovery, creativity, philosophy of science, psychology

General General

Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19.

In Mathematical biosciences and engineering : MBE

COVID-19 is an infectious disease caused by a newly discovered coronavirus, which has become a worldwide pandemic greatly impacting our daily life and work. A large number of mathematical models, including the susceptible-exposed-infected-removed (SEIR) model and deep learning methods, such as long-short-term-memory (LSTM) and gated recurrent units (GRU)-based methods, have been employed for the analysis and prediction of the COVID-19 outbreak. This paper describes a SEIR-LSTM/GRU algorithm with time-varying parameters that can predict the number of active cases and removed cases in the US. Time-varying reproductive numbers that can illustrate the progress of the epidemic are also produced via this process. The investigation is based on the active cases and total cases data for the USA, as collected from the website "Worldometer". The root mean square error, mean absolute percentage error and r2 score were utilized to assess the model's accuracy.

Feng Lin, Chen Ziren, Jr Harold A Lay, Furati Khaled, Khaliq Abdul

2022-Jun-20

** COVID-19 , GRU , LSTM , SEIR , data-driven , time-varying parameters , time-varying reproduction number **

General General

When blame avoidance backfires: Responses to performance framing and outgroup scapegoating during the COVID-19 pandemic.

In Governance (Oxford, England)

Public officials use blame avoidance strategies when communicating performance information. While such strategies typically involve shifting blame to political opponents or other governments, we examine how they might direct blame to ethnic groups. We focus on the COVID-19 pandemic, where the Trump administration sought to shift blame by scapegoating (using the term "Chinese virus") and mitigate blame by positively framing performance information on COVID-19 testing. Using a novel experimental design that leverages machine learning techniques, we find scapegoating outgroups backfired, leading to greater blame of political leadership for the poor administrative response, especially among conservatives. Backlash was strongest for negatively framed performance data, demonstrating that performance framing shapes blame avoidance outcomes. We discuss how divisive blame avoidance strategies may alienate even supporters.

Porumbescu Gregory, Moynihan Donald, Anastasopoulos Jason, Olsen Asmus Leth

2022-Jun-03

General General

Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.

In Wiley interdisciplinary reviews. Data mining and knowledge discovery

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.

Dasgupta Abhijit, Bakshi Abhisek, Mukherjee Srijani, Das Kuntal, Talukdar Soumyajeet, Chatterjee Pratyayee, Mondal Sagnik, Das Puspita, Ghosh Subhrojit, Som Archisman, Roy Pritha, Kundu Rima, Sarkar Akash, Biswas Arnab, Paul Karnelia, Basak Sujit, Manna Krishnendu, Saha Chinmay, Mukhopadhyay Satinath, Bhattacharyya Nitai P, De Rajat K

EHR, deep learning, drug affinity, social media, x‐ray/CT/HRCT

Public Health Public Health

Challenges of data sharing in European Covid-19 projects: A learning opportunity for advancing pandemic preparedness and response.

In The Lancet regional health. Europe

The COVID-19 pandemic saw a massive investment into collaborative research projects with a focus on producing data to support public health decisions. We relay our direct experience of four projects funded under the Horizon2020 programme, namely ReCoDID, ORCHESTRA, unCoVer and SYNCHROS. The projects provide insight into the complexities of sharing patient level data from observational cohorts. We focus on compliance with the General Data Protection Regulation (GDPR) and ethics approvals when sharing data across national borders. We discuss procedures for data mapping; submission of new international codes to standards organisation; federated approach; and centralised data curation. Finally, we put forward recommendations for the development of guidelines for the application of GDPR in case of major public health threats; mandatory standards for data collection in funding frameworks; training and capacity building for data owners; cataloguing of international use of metadata standards; and dedicated funding for identified critical areas.

Tacconelli Evelina, Gorska Anna, Carrara Elena, Davis Ruth Joanna, Bonten Marc, Friedrich Alex W, Glasner Corinna, Goossens Herman, Hasenauer Jan, Abad Josep Maria Haro, Peñalvo José L, Sanchez-Niubo Albert, Sialm Anastassja, Scipione Gabriella, Soriano Gloria, Yazdanpanah Yazdan, Vorstenbosch Ellen, Jaenisch Thomas

2022-Oct

Cohort study, Data sharing, General Data Protection Regulation, Machine learning, Pandemic, Preparedeness, SARS-CoV-2

General General

Grayscale image statistics of COVID-19 patient CT scans characterize lung condition with machine and deep learning.

In Chronic diseases and translational medicine

Background : Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.

Method : Five hundred thirteen CT images relating to 57 patients (49 with COVID-19; 8 free of COVID-19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.

Results : The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians' visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians' assessments (99.81% AS accuracy; 1 error from 513 images).

Conclusion : Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

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

2022-May-31

COVID‐19 lung feature recognition, computed tomography analysis, confusion‐matrix analysis, grayscale image attributes, visual versus algorithmic classification

General General

COVID-19 detection using X-ray images and statistical measurements.

In Measurement : journal of the International Measurement Confederation

The COVID-19 pandemic spread all over the world, starting in China in late 2019, and significantly affected life in all aspects. As seen in SARS, MERS, COVID-19 outbreaks, coronaviruses pose a great threat to world health. The COVID-19 epidemic, which caused pandemics all over the world, continues to seriously threaten people's lives. Due to the rapid spread of COVID-19, many countries' healthcare sectors were caught off guard. This situation put a burden on doctors and healthcare professionals that they could not handle. All of the studies on COVID-19 in the literature have been done to help experts to recognize COVID-19 more accurately, to use more accurate diagnosis and appropriate treatment methods. The alleviation of this workload will be possible by developing computer aided early and accurate diagnosis systems with machine learning. Diagnosis and evaluation of pneumonia on computed tomography images provide significant benefits in investigating possible complications and in case follow-up. Pneumonia and lesions occurring in the lungs should be carefully examined as it helps in the diagnostic process during the pandemic period. For this reason, the first diagnosis and medications are very important to prevent the disease from progressing. In this study, a dataset consisting of Pneumonia and Normal images was used by proposing a new image preprocessing process. These preprocessed images were reduced to 15x15 unit size and their features were extracted according to their RGB values. Experimental studies were carried out by performing both normal values and feature reduction among these features. RGB values of the images were used in train and test processes for MLAs. In experimental studies, 5 different Machine Learning Algorithms (MLAs) (Multi Class Support Vector Machine (MC-SVM), k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB)). The following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.746377, 0.963768. Accuracy results in test operations were obtained as follows; 0.87755, 0.857143, 0.857143, 0.877551, 0.938776.

Avuçlu Emre

2022-Sep-30

Biomedical images, COVID-19, Feature extraction, Machine learning algorithms

Public Health Public Health

Changes in blood pressure and related risk factors among nurses working in a negative pressure isolation ward.

In Frontiers in public health

Objective : To observe changes in blood pressure (ΔBP) and explore potential risk factors for high ΔBP among nurses working in a negative pressure isolation ward (NPIW).

Methods : Data from the single-center prospective observational study were used. Based on a routine practice plan, female nurses working in NPIW were scheduled to work for 4 days/week in different shifts, with each day working continuously for either 5 or 6 h. BP was measured when they entered and left NPIW. Multivariable logistic regression was used to assess potential risk factors in relation to ΔBP ≥ 5 mm Hg.

Results : A total of 84 nurses were included in the analysis. The ΔBP was found to fluctuate on different working days; no significant difference in ΔBP was observed between the schedules of 5 and 6 h/day. The standardized score from the self-rating anxiety scale (SAS) was significantly associated with an increased risk of ΔBP ≥ 5 mm Hg (odds ratio [OR] = 1.12, 95% CI: 1.00-1.24). Working 6 h/day (vs. 5 h/day) in NPIW was non-significantly related to decreased risk of ΔBP (OR = 0.70), while ≥ 2 consecutive working days (vs. 1 working day) was non-significantly associated with increased risk of ΔBP (OR = 1.50).

Conclusion : This study revealed no significant trend for ΔBP by working days or working time. Anxiety was found to be significantly associated with increased ΔBP, while no <2 consecutive working days were non-significantly related to ΔBP. These findings may provide some preliminary evidence for BP control in nurses who are working in NPIW for Coronavirus Disease 2019 (COVID-19).

Wang Yaoyao, Tian Junzhang, Qu Hongying, Yu Lingna, Zhang Xiaoqin, Huang Lishan, Zhou Jianqun, Lian Wanmin, Wang Ruoting, Wang Lijun, Li Guowei, Tang Li

2022

COVID-19, blood pressure, negative pressure isolation ward, nurse, risk factors

General General

An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework.

In International journal of imaging systems and technology

COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.

Mannepalli Durga Prasad, Namdeo Varsha

2022-Jul

classification, deep learning, feature extraction, feature selection, optimization, preprocessing

General General

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

In Computational intelligence and neuroscience

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.

Jain Deepak Kumar, Singh Tarishi, Saurabh Praneet, Bisen Dhananjay, Sahu Neeraj, Mishra Jayant, Rahman Habibur

2022

General General

The effect of information-driven resource allocation on the propagation of epidemic with incubation period.

In Nonlinear dynamics

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

Zhu Xuzhen, Liu Yuxin, Wang Xiaochen, Zhang Yuexia, Liu Shengzhi, Ma Jinming

2022-Aug-02

Epidemic spreading, Exposed state, Information-driven resource allocation, Microscopic Markov chain, Multiplex network

Pathology Pathology

Digital pathology - Rising to the challenge.

In Frontiers in medicine

Digital pathology has gone through considerable technical advances during the past few years and certain aspects of digital diagnostics have been widely and swiftly adopted in many centers, catalyzed by the COVID-19 pandemic. However, analysis of requirements, careful planning, and structured implementation should to be considered in order to reap the full benefits of a digital workflow. The aim of this review is to provide a practical, concise and hands-on summary of issues relevant to implementing and developing digital diagnostics in the pathology laboratory. These include important initial considerations, possible approaches to overcome common challenges, potential diagnostic pitfalls, validation and regulatory issues and an introduction to the emerging field of image analysis in routine.

Dawson Heather

2022

artificial intelligence, digital pathology, image analysis, scanner acquisition, validation

General General

A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images.

In IEEE transactions on molecular, biological, and multi-scale communications

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due to the limited data, existing models suffer from incapable feature extraction and poor network convergence and optimization. Accordingly, a multi-stage residual network, MSRCovXNet, is proposed for effective detection of COVID-19 from chest x-ray (CXR) images. As a shallow yet effective classifier with the ResNet-18 as the feature extractor, MSRCovXNet is optimized by fusing two proposed feature enhancement modules (FEM), i.e., low-level and high-level feature maps (LLFMs and HLFMs), which contain respectively more local information and rich semantic information, respectively. For effective fusion of these two features, a single-stage FEM (MSFEM) and a multi-stage FEM (MSFEM) are proposed to enhance the semantic feature representation of the LLFMs and the local feature representation of the HLFMs, respectively. Without ensembling other deep learning models, our MSRCovXNet has a precision of 98.9% and a recall of 94% in detection of COVID-19, which outperforms several state-of-the-art models. When evaluated on the COVIDGR dataset, an average accuracy of 82.2% is achieved, leading other methods by at least 1.2%.

Fang Zhenyu, Ren Jinchang, MacLellan Calum, Li Huihui, Zhao Huimin, Hussain Amir, Fortino Giancarlo

2022-Mar

COVID-19, MSRCovXNet, ResNet-18, chest x-ray imaging, feature enhancement module

Cardiology Cardiology

Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19.

In Frontiers in cardiovascular medicine

Background : As automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms.

Methods : In this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae.

Results : Feasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival.

Conclusion : Automated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.

Pellikka Patricia A, Strom Jordan B, Pajares-Hurtado Gabriel M, Keane Martin G, Khazan Benjamin, Qamruddin Salima, Tutor Austin, Gul Fahad, Peterson Eric, Thamman Ritu, Watson Shivani, Mandale Deepa, Scott Christopher G, Naqvi Tasneem, Woodward Gary M, Hawkes William

2022

COVID-19, artificial intelligence, deformation imaging, echocardiography, machine learning, strain rate imaging

General General

MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans.

In Signal, image and video processing

In the field of diagnosis and treatment planning of Coronavirus disease 2019 (COVID-19), accurate infected area segmentation is challenging due to the significant variations in the COVID-19 lesion size, shape, and position, boundary ambiguity, as well as complex structure. To bridge these gaps, this study presents a robust deep learning model based on a novel multi-scale contextual information fusion strategy, called Multi-Level Context Attentional Feature Fusion (MLCA2F), which consists of the Multi-Scale Context-Attention Network (MSCA-Net) blocks for segmenting COVID-19 lesions from Computed Tomography (CT) images. Unlike the previous classical deep learning models, the MSCA-Net integrates Multi-Scale Contextual Feature Fusion (MC2F) and Multi-Context Attentional Feature (MCAF) to learn more lesion details and guide the model to estimate the position of the boundary of infected regions, respectively. Practically, extensive experiments are performed on the Kaggle CT dataset to explore the optimal structure of MLCA2F. In comparison with the current state-of-the-art methods, the experiments show that the proposed methodology provides efficient results. Therefore, we can conclude that the MLCA2F framework has the potential to dramatically improve the conventional segmentation methods for assisting clinical decision-making.

Bakkouri Ibtissam, Afdel Karim

2022-Aug-03

COVID-19 pneumonia, Context attentional features, Contextual information, Multi-level fusion, Multi-scale features, Segmentation

General General

A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles

bioRxiv Preprint

Monkeypox has emerged as a fast-spreading disease around the world and an outbreak has been reported in 75 countries so far. Although the clinical attributes of Monkeypox are similar to those of Smallpox, skin lesions and rashes caused by Monkeypox often resemble those of other types of pox, for example, chickenpox and cowpox. This scenario makes an early diagnosis of Monkeypox challenging for the healthcare professional just by observing the visual appearance of lesions and rashes. The rarity of Monkeypox before the current outbreak further created a knowledge gap among healthcare professionals around the world. To tackle this challenging situation, scientists are taking motivation from the success of supervised machine learning in COVID-19 detection. However, the lack of Monkeypox skin image data is making the bottleneck of using machine learning in Monkeypox detection from patient skin images. Therefore, in this project, we introduce the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. We used web-scraping to collect Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles infected skin as well as healthy skin images to build a comprehensive image database and made it publicly available. We believe that our database will facilitate the development of baseline machine learning algorithms for early detection of Monkeypox in clinical settings. Our dataset is available at the following Kaggle link: https://www.kaggle.com/datasets/arafathussain/monkeypox-skin-image-dataset-2022.

Islam, T.; Hussain, M. A.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-09

General General

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.

In Signal processing. Image communication

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

Chi Jianning, Zhang Shuang, Han Xiaoying, Wang Huan, Wu Chengdong, Yu Xiaosheng

2022-Aug-02

COVID-19, CT image, Convolutional neural networks, Deep learning, Infection segmentation

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 in 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, recently, we introduced the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. In addition, in this paper, we utilize this 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. A.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-09

General General

Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images.

In Multimedia tools and applications

The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million people globally. For its early diagnosis, researchers consider chest X-ray examinations as a standard screening technique in addition to RT-PCR test. Majority of research work till date focused only on application of deep learning approaches that is relevant but lacking in better pre-processing of CXR images. Towards this direction, this study aims to explore cumulative effects of image denoising and enhancement approaches on the performance of deep learning approaches. Regarding pre-processing, suitable methods for X-ray images, Histogram equalization, CLAHE and gamma correction have been tested individually and along with adaptive median filter, median filter, total variation filter and gaussian denoising filters. Proposed study compared eleven combinations in exploration of most coherent approach in greedy manner. For more robust analysis, we compared ten CNN architectures for performance evaluation with and without enhancement approaches. These models are InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, Vgg19, NASNetMobile, ResNet101, DenseNet121, DenseNet169, DenseNet201. These models are trained in 4-way (COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal) and 3-way classification scenario (COVID-19 vs Pneumonia vs Normal) on two benchmark datasets. The proposed methodology determines with TVF + Gamma, models achieve higher classification accuracy and sensitivity. In 4-way classification MobileNet with TVF + Gamma achieves top accuracy of 93.25% with 1.91% improvement in accuracy score, COVID-19 sensitivity of 98.72% and F1-score of 92.14%. In 3-way classification our DenseNet201 with TVF + Gamma gains accuracy of 91.10% with improvement of 1.47%, COVID-19 sensitivity of 100% and F1-score of 91.09%. Proposed study concludes that deep learning modes with gamma correction and TVF + Gamma has superior performance compared to state-of-the-art models. This not only minimizes overlapping between COVID-19 and virus pneumonia but advantageous in time required to converge best possible results.

Sharma Ajay, Mishra Pramod Kumar

2022-Aug-01

COVID-19 analysis, Chest X-ray, Deep learning, Image denoising, Image enhancement, Pneumonia classification

Public Health Public Health

Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models.

In Technological forecasting and social change

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

Middya Asif Iqbal, Roy Sarbani

2022-Oct

Covid-19, Deep learning, Spatio-temporal variation

General General

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.

In Structural chemistry

The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Niranjan Vidya, Setlur Anagha Shamsundar, Karunakaran Chandrashekar, Uttarkar Akshay, Kumar Kalavathi Murugan, Skariyachan Sinosh

2022-Aug-03

COVID-19, Computational sciences, Drug repurposing, Protein targets, SARS-CoV-2, Variants of concern

General General

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic.

In Applied soft computing

Quantifying and analysing excess mortality in crises such as the ongoing COVID-19 pandemic is crucial for policymakers. Traditional measures fail to take into account differences in the level, long-term secular trends, and seasonal patterns in all-cause mortality across countries and regions. This paper develops and empirically investigates the forecasting performance of a novel, flexible and dynamic ensemble learning strategy for the seasonal time series forecasting of monthly respiratory disease death data across a pool of 61 heterogeneous countries. The strategy is based on a Bayesian model ensemble (BME) of heterogeneous time series methods involving both the selection of the subset of best forecasters (model confidence set), the identification of the best holdout period for each contributed model, and the determination of optimal weights using out-of-sample predictive accuracy. A model selection strategy is also developed to remove the outlier models and to combine the models with reasonable accuracy in the ensemble. The empirical outcomes of this large set of experiments show that the accuracy of the BME approach is noticeably improved when selecting a flexible and dynamic holdout period. Additionally, the BME forecasts of respiratory disease deaths for each country are highly accurate and exhibit a high correlation (94%) with COVID-19 deaths in 2020.

Ashofteh Afshin, Bravo Jorge M, Ayuso Mercedes

2022-Aug-01

Ensemble Bayesian model averaging (EBMA), Ensemble learning, Forecasting, Healthcare, Layered learning, Machine learning, Multiple learning processes, Respiratory disease deaths, SARS-CoV-2, Time series

General General

A novel deep fusion strategy for COVID-19 prediction using multimodality approach.

In Computers & electrical engineering : an international journal

Over the last two years, the novel coronavirus has become a significant threat to the health of the public, and numerous approaches are developed to determine the symptoms of COVID-19. To deal with the complex symptoms of COVID-19, a Deep Learning-assisted Multi-modal Data Analysis (DMDA) approach is introduced to determine COVID-19 symptoms by utilizing acoustic and image-based data. Furthermore, the classified events are forwarded to the proposed Dynamic Fusion Strategy (DFS) for confirming the health status of the individual. Initially, the performance of the proposed solution is evaluated on both acoustic and image-based samples and the proposed solution attains the maximum accuracy of 96.88% and 98.76%, respectively. Similarly, the DFS has achieved an overall symptom determination accuracy of 98.72% which is highly acceptable for decision-making. Moreover, the proposed solution shows high reliability with an accuracy of 95.64% even in absence of any one of the data modalities during testing.

Manocha Ankush, Bhatia Munish

2022-Aug-03

Covid-19, Deep learning, Semi-supervised learning, Smart healthcare, Smart monitoring

General General

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.

In Health services research and managerial epidemiology

Introduction : The federal government legislated supplemental funding to support community health centers (CHCs) in response to the COVID-19 pandemic. Supplemental funding included standard base payments and adjustments for the number of total and uninsured patients served before the pandemic. However, not all CHCs share similar patient population characteristics and health risks.

Objective : To use machine learning to identify the most important factors for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 during the first year of the pandemic.

Methods : Our analytic sample included data from 1342 CHCs across the 50 states and D.C. in 2020. We trained a random forest (RF) classifier model, incorporating 5-fold cross-validation to validate the RF model while optimizing the model's hyperparameters. Final performance metrics were calculated following the application of the model that had the best fit to the held-out test set.

Results : CHCs with a high burden of COVID-19 had an average of 65.3 patients diagnosed with COVID-19 per 1000 patients in 2020. Our RF model had 80.9% accuracy, 80.1% precision, 25.0% sensitivity, and 98.1% specificity. The percentage of Hispanic patients served in 2020 was the most important feature for predicting whether CHCs had high COVID-19 burden.

Conclusions : Findings from our RF model suggest patient population race and ethnicity characteristics were most important for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 in 2020, though sensitivity was low. Enhanced support for CHCs serving large Hispanic patient populations may have an impact on addressing future COVID-19 waves.

Goldstein Evan V, Wilson Fernando A

COVID-19, community health, community health centers, health promotion, prevention, primary care

Public Health Public Health

Developing a Digital Technology System to Address COVID-19 Health Needs in Guatemala: A Scientific Diaspora Case Study.

In Frontiers in research metrics and analytics

Scientific diasporas are organized groups of professionals who work together to contribute to their country of origin. Since the start of the COVID-19 pandemic in 2020, scientific diasporas around the world have focused their efforts to support the public health response in their countries of origin. As the first cases of COVID-19 were reported in Guatemala in March of 2020, a team of four Guatemalan nationals, residing abroad and in-country, started collaborating to tackle COVID-19 misinformation and issues with healthcare services navigation. Their collaboration was facilitated by FUNDEGUA, a Guatemalan nonprofit, which provided a legal framework to establish partnerships and fundraise. The team created a digital technological system called ALMA (Asistente de Logística Médica Automatizada in Spanish). A female character named ALMA was created to personify the digital information services, through social media profiles, an interactive website, a free national multilingual call center, and an artificial intelligence-based chatbot. More members joined the nascent interdisciplinary diaspora through professional/personal references or social media. ALMA provided a platform for Guatemalan nationals to contribute with their skillset to their country during a global crisis through flexible schedules and short- or long-term involvement. As the team grew, the services for query resolution and information dissemination expanded as well. The ALMA initiative shows that scientific diasporas can provide an avenue for professionals to contribute to Guatemala, regardless of their residence and job commitments.

Alvarado Juan Roberto, Lainfiesta Ximena, Paniagua-Avila Alejandra, Asturias Gabriela

2022

COVID-19 pandemic, Guatemala, brain circulation, capacity building, chatbot, scientific diasporas, technology

General General

COVID-19: Machine learning for safe transportation.

In Concurrency and computation : practice & experience

Entire world has been affected by Covid-19 pandemic. In fighting against the Covid-19, social distancing and face mask have a paramount role in freezing the spread of the disease. People are asked to limit their interactions with each other, to reduce the spread of the disease. Here an alert system has to be maintained to caution people traveling in vehicles. Our proposed solution will work primarily on computer vision. The video stream is captured using a camera. Footage is processed using single shot detector algorithm for face mask detection. Second, YOLOv3 object detection algorithm is used to detect if social distancing is maintained or not inside the vehicle. If passengers do not follow the safety rules such as wearing a mask at any point of the time in the whole journey, alarm/alert is given via buzzer/speaker. This ensures that people abide by the safety rules without affecting their daily norms of transportation. It also helps the government to keep the situation under control.

Sankari Subbiah, Varshini Subramaniam Sankaran, Aafia Shifana Savvas Mohamed

2022-Aug-30

COVID‐19, YOLOv3, computer vision, face mask, single shot detector, social distancing

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 in 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, recently, we introduced the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. In addition, in this paper, we utilize this 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.

Hussain, M. A.; Islam, T.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-08

oncology Oncology

Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning.

In Molecular & cellular proteomics : MCP

The recent surge of COVID-19 hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of 100s of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and non-survivors. With increasing length of hospitalization, the survivors' samples showed a trend towards normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of ten proteins and five metabolites we could predict patient survival with 92% accuracy (AUC 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

Richard Vincent R, Gaither Claudia, Popp Robert, Chaplygina Daria, Brzhozovskiy Alexander, Kononikhin Alexey, Mohammed Yassene, Zahedi René P, Nikolaev Evgeny N, Borchers Christoph H

2022-Aug-03

General General

Answering hospital caregivers' questions at any time: proof of concept of an artificial intelligence-based chatbot in a French hospital.

In JMIR human factors

BACKGROUND : Access to accurate information in health is a key point for caregivers to avoid medication errors, especially with the reorganization of staff and drugs circuits during health crises such as COVID 19. It is therefore the role of the hospital pharmacy to answer caregivers' questions. Some may require the expertise of a pharmacist, some should be answered by pharmacy technicians, but others are simple and redundant, and automated responses may be given.

OBJECTIVE : We aimed at developing and implementing a chatbot to answer questions from hospital caregivers, 24 hours a day, about drugs and pharmacy organization, and evaluated this tool.

METHODS : The ADDIE model: Analysis, Design, Development, Implementation, Evaluation, was used by a multi-professional team composed of 3 hospital pharmacists, 2 members of the Innovation and Transformation Department, and the Information Technology (IT) service provider. Based on an analysis of the caregivers' needs about drugs and pharmacy organization, we designed and developed a chatbot. The tool was then evaluated before the implementation into the hospital intranet. Its relevance and conversations with testers were monitored via the IT provider's back office.

RESULTS : Needs analysis with 5 hospital pharmacists and 33 caregivers from 5 health services allowed us to identify 7 themes about drugs and pharmacy organization (such as opening hours and specific prescriptions). After a year of chatbot design and development, the test version obtained good evaluation scores: its speed was rated 8.2/10, usability 8.1/10, and appearance 7.5/10. Testers were generally satisfied (70%) and were hoping for the content to be enhanced.

CONCLUSIONS : The chatbot seems to be a relevant tool for hospital caregivers, helping them to get reliable and verified information they need on drugs and pharmacy organization. In the context of significant mobility of nursing staff during the health crisis due to COVID-19, the chatbot could be a suitable tool for transmitting relevant information related to drugs circuits or specific procedures. To our knowledge, this is the first time that such a tool has been designed for caregivers. Its development further continued by means of tests conducted with other users such as pharmacy technicians, and via the integration of additional data, before the implementation on the two hospital sites.

CLINICALTRIAL :

Daniel Thomas, de Chevigny Alix, Champrigaud Adeline, Valette Julie, Sitbon Marine, Jardin Meryam, Chevalier Delphine, Renet Sophie

2022-Aug-02

Public Health Public Health

Predictors of invasive mechanical ventilation in hospitalized COVID-19 patients: a retrospective study from Jordan.

In Expert review of respiratory medicine

OBJECTIVES : To identify early indicators for invasive mechanical ventilation utilization among COVID-19 patients.

METHODS : This retrospective study evaluated COVID-19 patients who were admitted to hospital from September 20, 2020, to August 8, 2021. Patients' clinical characteristics, demographics, comorbidities, and laboratory results were evaluated. Multivariable logistic regression and machine learning (ML) methods were employed to assess variable significance.

RESULTS : Among 1,613 confirmed COVID-19 patients, 365 patients (22.6%) received invasive mechanical ventilation (IMV). Factors associated with IMV included older age >65 years (OR,1.46; 95%CI, 1.13 - 1.89), current smoking status (OR, 1.71; 95%CI, 1.22-2.41), critical disease at admission (OR, 1.97; 95%CI, 1.28-3.03), and chronic kidney disease (OR, 2.07; 95%CI, 1.37-3.13). Laboratory abnormalities that were associated with increased risk for IMV included high leukocyte count (OR, 2.19; 95%CI, 1.68 - 2.87), low albumin (OR, 1.76; 95%CI, 1.33 - 2.34) and high AST (OR, 1.71; 95%CI, 1.31 - 2.22).

CONCLUSION : Our study suggests that there are several factors associated with the increased need for IMV among COVID-19 patients including older age, current smoking status, critical disease status on admission, and chronic kidney disease. In addition, laboratory markers such as high leukocyte count, low albumin and high AST were determined. These findings will help in early identification of patients at high risk for IMV and reallocation of hospital resources towards patients who need them the most to improve their outcomes.

Kabbaha Suad, Al-Azzam Sayer, Karasneh Reema A, Khassawneh Basheer Y, Al-Mistarehi Abdel-Hameed, Lattyak William J, Aldiab Motasem, Hasan Syed Shahzad, Conway Barbara R, Aldeyab Mamoon A

2022-Aug-05

COVID-19, Comorbidity, Invasive Mechanical Ventilation, Laboratory, Predictor, Risk Factor, SARS‐CoV‐2, Severity

General General

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis.

In Computational and mathematical methods in medicine

As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms. In the battle against the virus, however, researchers and decision-makers must contend with an ever-increasing volume of data, referred to as "big data." VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine-tune our model on lung ultrasound frames from healthy people as well as from patients with COVID-19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non-COVID-19. COVID-19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID-19.

Sangeetha S K B, Kumar M Sandeep, K Deeba, Rajadurai Hariharan, Maheshwari V, Dalu Gemmachis Teshite

2022

General General

COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology.

In Frontiers in public health

At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.

Nayan Nazrul Anuar, Jie Yi Choon, Suboh Mohd Zubir, Mazlan Nur-Fadhilah, Periyasamy Petrick, Abdul Rahim Muhammad Yusuf Zawir, Shah Shamsul Azhar

2022

COVID-19, diagnostic, machine learning, non-invasive, photoplethysmogram, prediction

Ophthalmology Ophthalmology

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method.

In Frontiers in molecular biosciences

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Li Hao, Huang Feiming, Liao Huiping, Li Zhandong, Feng Kaiyan, Huang Tao, Cai Yu-Dong

2022

COVID-19, classification algorithm, feature selection, immune cell, machine learning

Public Health Public Health

Artificial Intelligence in Accelerating Drug Discovery and Development.

In Recent patents on biotechnology

Drug discovery and development are critical processes that enable the treatment of a wide variety of health-related problems. These are time-consuming, tedious, complicated, and costly processes. Numerous difficulties arise throughout the entire process of drug discovery, from design to testing. Corona Virus Disease 2019 (COVID-19) recently posed a significant threat to global public health. SARS-Cov-2 and its variants are rapidly spreading in humans due to their high transmission rate. To effectively treat COVID-19, potential drugs and vaccines must be developed quickly. The advancement of artificial intelligence has shifted the focus of drug development away from traditional methods and toward bioinformatics tools. Computer-aided drug design techniques have demonstrated tremendous utility in dealing with massive amounts of biological data and developing efficient algorithms. Artificial intelligence enables more effective approaches to complex problems associated with drug discovery and development through the use of machine learning. Artificial intelligence-based technologies improve the pharmaceutical industry's ability to discover effective drugs. This review summarizes significant challenges encountered during the drug discovery and development processes, as well as the applications of artificial intelligence-based methods to overcome those obstacles in order to provide effective solutions to health problems. This may provide additional insight into the mechanism of action, resulting in the development of vaccines and potent substitutes for repurposed drugs that can be used to treat not only COVID-19 but also other ailments.

Tripathi Anushree, Misra Krishna, Dhanuka Richa, Singh Jyoti Prakash

2022-Aug-02

Artificial Intelligence (AI), Bioinformatics, COVID-19, Drug design, Machine Learning (ML)., Pharmaceutical applications

Radiology Radiology

Primary SARS-CoV-2 Pneumonia Screening in Adults: Analysis of the Correlation between High-Resolution Computed Tomography Pulmonary Patterns and Initial Oxygen Saturation Levels.

In Current medical imaging

BACKGROUND : Chest high-resolution computed tomography (HRCT) is mandatory for patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and a high respiratory rate (RR) because sublobar consolidation is the likely pathological pattern in addition to ground glass opacities (GGOs).

OBJECTIVE : The present study determined the correlation between the percentage extent of typical pulmonary lesions on HRCT, as a representation of severity, and the RR and peripheral oxygen saturation level (SpO2), as measured through pulse oximetry, in patients with reverse transcriptase polymerase chain reaction (RT-PCR)-confirmed primary (noncomplicated) SARS-CoV-2 pneumonia.

METHODS : The present retrospective study was conducted in 332 adult patients who presented withzdyspnea and hypoxemia and were admitted to Prince Mohammed bin Abdulaziz Hospital, Riyadh, Saudi Arabia between May 15, 2020 and December 15, 2020. All the patients underwent chest HRCT. Of the total, 198 patients with primary noncomplicated SARS-CoV-2 pneumonia were finally selected based on the typical chest HRCT patterns. The main CT patterns, GGO and sublobar consolidation, were individually quantified as a percentage of the total pulmonary involvement through algebraic summation of the percentage of the 19 pulmonary segments affected. Additionally, the statistical correlation strength between the total percentage pulmonary involvement and the age, initial RR, and percentage SpO2 of the patients was determined.

RESULTS : The mean ± standard deviation (SD) age of the 198 patients was 48.9 ± 11.4 years. GGO magnitude alone exhibited a significant weak positive correlation with patients' age (r = 0.2; p = 0.04). Sublobar consolidation extent exhibited a relatively stronger positive correlation with RR than GGO magnitude (r = 0.23; p = 0.002). A relatively stronger negative correlation was observed between the GGO extent and SpO2 (r = - 0.38; p = 0.002) than that between sublobar consolidation and SpO2 (r = - 0.2; p = 0.04). An increase in the correlation strength was demonstrated with increased case segregation with GGO extent (r = - 0.34; p = 0.01).

CONCLUSION : The correlation between the magnitudes of typical pulmonary lesion patterns, particularly GGO, which exhibited an incremental correlation pattern on chest HRCT, and the SpO2 percentage, may allow the establishment of an artificial intelligence program to differentiate primary SARS-CoV-2 pneumonia from other complications and associated pathology influencing SpO2.

Alonazi Batil, Mostafa Mohamed A, Farghaly Ahmed M, Zindani Salah A, Al-Watban Jehad A, Altaimi Feras, Almotairy Abdulrahim S, Fagiry Moram A, Mahmoud Mustafa Z

2022-Aug-02

Artificial intelligence, chest high-resolution computed tomography, ground glass opacities, primary SARS-CoV-2 pneumonia, respiratory rate

General General

Deep learning based COVID-19 detection using medical images: Is insufficient data handled well?

In Current medical imaging

The deep learning is a prominent method for automatic detection of COVID-19 disease using medical dataset. This paper aims to give the perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study on the available datasets comprising CT and X-ray images are presented in this paper, which can be very much useful in the context of deep learning framework for COVID-19 detection. Moreover, various data handling techniques that are very essential in deep learning models are discussed in detail. Advanced data handling techniques and approaches to modify deep learning models are suggested to handle the data insufficiency problem in deep learning based COVID-19 detection.

Babu Caren, O Rahul Manohar, Chandy D Abraham

2022-Aug-03

COVID-19, CT dataset, Chest X-ray dataset, data augmentation, deep learning, transfer learning.

Public Health Public Health

Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health.

In American journal of public health ; h5-index 90.0

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. Published online ahead of print August 4, 2022:e1-e10. https://doi.org/10.2105/AJPH.2022.306917).

Thorpe Lorna E, Chunara Rumi, Roberts Tim, Pantaleo Nicholas, Irvine Caleb, Conderino Sarah, Li Yuruo, Hsieh Pei Yang, Gourevitch Marc N, Levine Shoshanna, Ofrane Rebecca, Spoer Benjamin

2022-Aug-04

Public Health Public Health

Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic.

In Disaster medicine and public health preparedness

OBJECTIVE : Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day.

METHODS : Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.

RESULTS : 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO's pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.

CONCLUSIONS : AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

Martin Nina M, Poirier Lisa, Rosenblum Andrew J, Reznar Melissa M, Gittelsohn Joel, Barnett Daniel J

2022-Aug-04

Food Security, Machine Learning, Natural Language Processing, Topic Modeling, Twitter

General General

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

In Frontiers in immunology ; h5-index 100.0

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Liu Jun, Li Zhi-Bin, Lu Qi-Qi, Yu Yi, Zhang Shan-Qiang, Ke Pei-Feng, Zhang Fan, Li Ji-Cheng

2022

COVID-19, UPLC-MS/MS, machine learning, potential biomarkers, widely targeted metabolites

General General

Argument mining as rapid screening tool of COVID-19 literature quality: Preliminary evidence.

In Frontiers in public health

Background : The COVID-19 pandemic prompted the scientific community to share timely evidence, also in the form of pre-printed papers, not peer reviewed yet.

Purpose : To develop an artificial intelligence system for the analysis of the scientific literature by leveraging on recent developments in the field of Argument Mining.

Methodology : Scientific quality criteria were borrowed from two selected Cochrane systematic reviews. Four independent reviewers gave a blind evaluation on a 1-5 scale to 40 papers for each review. These scores were matched with the automatic analysis performed by an AM system named MARGOT, which detected claims and supporting evidence for the cited papers. Outcomes were evaluated with inter-rater indices (Cohen's Kappa, Krippendorff's Alpha, s* statistics).

Results : MARGOT performs differently on the two selected Cochrane reviews: the inter-rater indices show a fair-to-moderate agreement of the most relevant MARGOT metrics both with Cochrane and the skilled interval scores, with larger values for one of the two reviews.

Discussion and conclusions : The noted discrepancy could rely on a limitation of the MARGOT system that can be improved; yet, the level of agreement between human reviewers also suggests a different complexity between the two reviews in debating controversial arguments. These preliminary results encourage to expand and deepen the investigation to other topics and a larger number of highly specialized reviewers, to reduce uncertainty in the evaluation process, thus supporting the retraining of AM systems.

Brambilla Gianfranco, Rosi Antonella, Antici Francesco, Galassi Andrea, Giansanti Daniele, Magurano Fabio, Ruggeri Federico, Torroni Paolo, Cisbani Evaristo, Lippi Marco

2022

COVID-19, argument mining, artificial intelligence, inter-rater agreement, scientific literature quality assessment

Radiology Radiology

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.

In Radiology. Artificial intelligence

Purpose : To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs.

Materials and Methods : A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists.

Results : Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both).

Conclusion : AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.

Sun Ju, Peng Le, Li Taihui, Adila Dyah, Zaiman Zach, Melton-Meaux Genevieve B, Ingraham Nicholas E, Murray Eric, Boley Daniel, Switzer Sean, Burns John L, Huang Kun, Allen Tadashi, Steenburg Scott D, Gichoya Judy Wawira, Kummerfeld Erich, Tignanelli Christopher J

2022-Jul

Application Domain, Classification, Diagnosis, Infection, Lung

General General

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways.

In medRxiv : the preprint server for health sciences

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10 -14 ), Alzheimer's disease (FDR ≤ 5.46×10 -10 ) and coronary artery disease (FDR ≤ 4.64×10 -2 ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Wang Lihua, Western Dan, Timsina Jigyasha, Repaci Charlie, Song Won-Min, Norton Joanne, Kohlfeld Pat, Budde John, Climer Sharlee, Butt Omar H, Jacobson Daniel, Garvin Michael, Templeton Alan R, Campagna Shawn, O’Halloran Jane, Presti Rachel, Goss Charles W, Mudd Philip A, Ances Beau M, Zhang Bin, Sung Yun Ju, Cruchaga Carlos

2022-Jul-25

oncology Oncology

The Impact of COVID-19 on Physician-Scientist Trainees and Faculty in the United States: A National Survey.

In Academic medicine : journal of the Association of American Medical Colleges

PURPOSE : Physician-scientists have long been considered an endangered species, and their extended training pathway is vulnerable to disruptions. This study investigated the effects of COVID-19-related challenges on the personal lives, career activities, stress levels, and research productivity of physician-scientist trainees and faculty.

METHOD : The authors surveyed medical students (MS), graduate students (GS), residents/fellows (R/F), and faculty (F) using a tool distributed to 120 U.S. institutions with MD-PhD programs in April-June 2020. Chi-squared and Fisher's exact tests were used to compare differences between groups. Machine learning was employed to select variables for multivariate logistic regression analyses aimed at identifying factors associated with stress and impaired productivity.

RESULTS : The analyses included 1,929 respondents (MS: n = 679, 35%; GS: n = 676, 35%; R/F: n = 274, 14%; F: n = 300, 16%). All cohorts reported high levels of social isolation, stress from effects of the pandemic, and negative impacts on productivity. R/F and F respondents were more likely than MS and GS respondents to report financial difficulties due to COVID-19. R/F and F respondents with a dual degree expressed more impaired productivity compared to those without a dual degree. Multivariate regression analyses identified impacted research/scholarly activities, financial difficulties, and social isolation as predictors of stress and impaired productivity for both MS and GS cohorts. For both R/F and F cohorts, impacted personal life and research productivity were associated with stress, while dual-degree status, impacted research/scholarly activities, and impacted personal life were predictors of impaired productivity. More female than male respondents reported increased demands at home.

CONCLUSIONS : This national survey of physician-scientist trainees and faculty found a high incidence of stress and impaired productivity related to the COVID-19 pandemic. Understanding the challenges faced and their consequences may improve efforts to support the physician-scientist workforce in the post-pandemic period.

Kwan Jennifer M, Noch Evan, Qiu Yuqing, Toubat Omar, Christophers Briana, Azzopardi Stephanie, Gilmer Gabrielle, Wiedmeier Julia Erin, Daye Dania

2022-Aug-02

General General

Meta-analysis of the microbial biomarkers in the gut - lung crosstalk in COVID-19, community acquired pneumonia and Clostridium difficile infections.

In Letters in applied microbiology

Respiratory infections are the leading causes of mortality and the current pandemic COVID-19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unraveling the correlations and interplay of the human microbiota in the gut- lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID-19 infected gut, COVID-19 infected lungs, Clostridium difficile infected gut and community acquired pneumonia infected lungs. The metagenome datasets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID-19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community acquired pneumonia whereas abundance of Faecalibacterium and Clostridium are associated with the Clostridium difficile infections in gut. A machine learning random forest classifier applied to the datasets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut lung axis during dysbiosis of two anatomically different organs.

Aishwarya S, Gunasekaran K

2022-Aug-03

diversity, gut - lung axis, interplay, microbiota, random forest classifier

General General

A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.

In Applied soft computing

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

Attallah Omneya, Samir Ahmed

2022-Jul-29

COVID-19, Computed tomography (CT), Convolutional neural networks, Deep learning, Discrete wavelet transform (DWT), ResNet

General General

A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles

bioRxiv Preprint

Monkeypox has emerged as a fast-spreading disease around the world and an outbreak has been reported in 42 countries so far. Although the clinical attributes of Monkeypox are similar to that of Smallpox, skin lesions and rashes caused by Monkeypox often resemble that of other pox types, e.g., Chickenpox and Cowpox. This scenario makes an early diagnosis of Monkeypox challenging for the healthcare professional just by observing the visual appearance of lesions and rashes. The rarity of Monkeypox before the current outbreak further created a knowledge gap among healthcare professionals around the world. To tackle this challenging situation, scientists are taking motivation from the success of supervised machine learning in COVID-19 detection. However, the lack of Monkeypox skin image data is making the bottleneck of using machine learning in Monkeypox detection from skin images of patients. Therefore, in this project, we introduce the Monkeypox Skin Image Dataset (MSID), the largest of its kind so far. We used web-scrapping to collect Monkeypox, Chickenpox, Smallpox, Cowpox and Measles infected skin as well as healthy skin images to build a comprehensive image database and made it publicly available. We believe that our database will facilitate the development of baseline machine learning algorithms for early Monkeypox detection in clinical settings. Our dataset is available at the following Kaggle link: https://www.kaggle.com/datasets/arafathussain/monkeypox-skin-image-dataset-2022.

Islam, T.; Hussain, M. A.; Chowdhury, F. U. H.; Islam, B. M. R.

2022-08-04

General General

Better understanding the behavior of air pollutants at shutdown times - results of a short full lockdown.

In International journal of environmental health research

Numerous studies have evaluated the effects of lockdowns during the COVID-19 pandemic, but most of them have concerned large cities and regions. This study aimed to evaluate the dynamics of air pollutants during and after the implementation of a short lockdown in the medium-sized city of Pelotas, Brazil, using hourly measurements of pollutants. The evaluation period included in this study was between August 9th and 12th, 2020. A machine learning model was used to investigate the expected behavior against what was observed during the study period. All pollutants presented a gradual reduction until a dynamic plateau established 48 hours after the start of the lockdown: NO2 (↓4%), O3 (↓34%), SO2 (↓24%), CO (↓48%), PM10 (↓82%) and PM2.5 (↓82%). At the end of the restriction measures, the PM10 and PM2.5 levels continued to decline beyond expectations. Our findings show that these measures can positively affect the air quality in medium-sized cities.

Tavella Ronan Adler, El Koury Santos Jéssica, de Moura Fernando Rafael, da Silva Júnior Flávio Manoel Rodrigues

2022-Aug-02

Air pollution, Brazil, COVID-19, air quality

General General

Vec4Cred: a model for health misinformation detection in web pages.

In Multimedia tools and applications

Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.

Upadhyay Rishabh, Pasi Gabriella, Viviani Marco

2022-Jul-28

Consumer health, Deep learning, Health misinformation, Machine learning, Natural language processing

Pathology Pathology

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays.

In BioMed research international ; h5-index 102.0

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

Badr Malek, Al-Otaibi Shaha, Alturki Nazik, Abir Tanvir

2022

General General

Real-time assessment of the Ganga river during pandemic COVID-19 and predictive data modeling by machine learning.

In International journal of environmental science and technology : IJEST

** : In this study, four water quality parameters were reviewed at 14 stations of river Ganga in pre-, during and post-lockdown and these parameters were modeled by using different machine learning algorithms. Various mathematical models were used for the computation of water quality parameters in pre-, during and post- lockdown period by using Central Pollution Control Board real-time data. Lockdown resulted in the reduction of Biochemical Oxygen Demand ranging from 55 to 92% with increased concentration of dissolved oxygen at few stations. pH was in range of 6.5-8.5 of during lockdown. Total coliform count declined during lockdown period at some stations. The modeling of oxygen saturation deficit showed supremacy of Thomas Mueller model (R 2 = 0.75) during lockdown over Streeter Phelps (R 2 = 0.57). Polynomial regression and Newton's Divided Difference model predicted possible values of water quality parameters till 30th June, 2020 and 07th August, 2020, respectively. It was found that predicted and real values were close to each other. Genetic algorithm was used to optimize hyperparameters of algorithms like Support Vector Regression and Radical Basis Function Neural Network, which were then employed for prediction of all examined water quality metrics. Computed values from ANN model were found close to the experimental ones (R 2 = 1). Support Vector Regression-Genetic Algorithm Hybrid proved to be very effective for accurate prediction of pH, Biochemical Oxygen Demand, Dissolved Oxygen and Total coliform count during lockdown.

Supplementary Information : The online version contains supplementary material available at 10.1007/s13762-022-04423-1.

Singh J, Swaroop S, Sharma P, Mishra V

2022-Jul-27

Artificial neural network, Biochemical oxygen demand, Dissolved oxygen, Modeling, The Ganga, Total Coliform Count, pH

Surgery Surgery

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.

In Knowledge-based systems

Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centred on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.

Costantini Giovanni, Cesarini Valerio, Robotti Carlo, Benazzo Marco, Pietrantonio Filomena, Di Girolamo Stefano, Pisani Antonio, Canzi Pietro, Mauramati Simone, Bertino Giulia, Cassaniti Irene, Baldanti Fausto, Saggio Giovanni

2022-Jul-28

1E, Vowel /e/ vocal task, 2S, Sentence vocal task, 3C, Cough vocal task, Adaboost, CFS, Correlation-based Feature Selection, CNN, Convolutional Neural Network, COVID-19, Classification, DL, Deep Learning, Deep learning, H, Healthy control subjects, MFCC, Mel-frequency Cepstral Coefficients, ML, Machine Learning, NS, Nasal Swab, P, Positive subjects, PCR, Polymerase Chain Reaction-based molecular swabs, PvsH, Positive versus Healthy subjects comparison, R, Recovered subjects, RF, Random Forest, ROC, Receiver-Operating Curve, ReLu, Rectified Linear Unit, RvsH, Recovered versus Healthy subjects comparison, SVM, Support Vector Machine, Speech processing

General General

Improving performance of classifiers for diagnosis of critical diseases to prevent COVID risk.

In Computers & electrical engineering : an international journal

The risk of developing COVID-19 and its variants may be higher in those with pre-existing health conditions such as thyroid disease, Hepatitis C Virus (HCV), breast tissue disease, chronic dermatitis, and other severe infections. Early and precise identification of these disorders is critical. A huge number of patients in nations like India require early and rapid testing as a preventative measure. The problem of imbalance arises from the skewed nature of data in which the instances from majority class are classified correct, while the minority class is unfortunately misclassified by many classifiers. When it comes to human life, this kind of misclassification is unacceptable. To solve the misclassification issue and improve accuracy in such datasets, we applied a variety of data balancing techniques to several machine learning algorithms. The outcomes are encouraging, with a considerable increase in accuracy. As an outcome of these proper diagnoses, we can make plans and take the required actions to stop patients from acquiring serious health issues or viral infections.

Kumar Vinod, Lalotra Gotam Singh, Kumar Ravi Kant

2022-Sep

COVID-19, Class balancing techniques, Clinical dataset, Machine learning, Multi-class classification

General General

Prediction, scanning and designing of TNF-α inducing epitopes for human and mouse

bioRxiv Preprint

Tumor Necrosis Factor alpha (TNF-) is a pleiotropic pro-inflammatory cytokine that plays a crucial role in controlling signaling pathways within the immune cells. Recent studies reported that the higher expression levels of TNF- is associated with the progression of several diseases including cancers, cytokine release syndrome in COVID-19 and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF- progression in various disease conditions. In the pilot study, we have proposed a host-specific in-silico tool for the prediction, designing and scanning of TNF- inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF- inducing/non-inducing for human and mouse hosts. Firstly, we developed alignment free (machine learning based models using composition of peptides) methods for predicting TNF- inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, alignment based (using BLAST) method has been used for predicting TNF- inducing epitopes. Finally, a hybrid method (combination of alignment free and alignment-based method) has been developed for predicting epitopes. Our hybrid method achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified the potential TNF- inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2 and human insulin. Best models developed in this study has been incorporated in a webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

Dhall, A.; Patiyal, S.; Choudhury, S.; Jain, S.; Narang, K.; Raghava, G. P. S.

2022-08-03

General General

Recognition of Freezing of Gait in Parkinson's Disease Based on Machine Vision.

In Frontiers in aging neuroscience ; h5-index 64.0

Background : Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.

Objective : A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.

Methods : In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.

Results : We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.

Conclusion : A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.

Li Wendan, Chen Xiujun, Zhang Jintao, Lu Jianjun, Zhang Chencheng, Bai Hongmin, Liang Junchao, Wang Jiajia, Du Hanqiang, Xue Gaici, Ling Yun, Ren Kang, Zou Weishen, Chen Cheng, Li Mengyan, Chen Zhonglue, Zou Haiqiang

2022

Parkinson’s disease, XGBoost, freezing of gait, machine learning, machine vision

General General

Sentiment analysis of COVID-19 social media data through machine learning.

In Multimedia tools and applications

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries' economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

Dangi Dharmendra, Dixit Dheeraj K, Bhagat Amit

2022-Jul-25

COVID-19, Decision tree, Logistic regression, Multinomial Naïve Bayes, Random forest, Support vector machine

General General

Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review.

In SN computer science

COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

Siddiqui Shah, Arifeen Murshedul, Hopgood Adrian, Good Alice, Gegov Alexander, Hossain Elias, Rahman Wahidur, Hossain Shazzad, Al Jannat Sabila, Ferdous Rezowan, Masum Shamsul

2022

Computed tomography (CT) images, Coronavirus (COVID-19), Deep learning (DL), Machine learning (ML), RT-PCR, X-ray images

General General

Polarized Citizen Preferences for the Ethical Allocation of Scarce Medical Resources in 20 Countries.

In MDM policy & practice

** : Objective. When medical resources are scarce, clinicians must make difficult triage decisions. When these decisions affect public trust and morale, as was the case during the COVID-19 pandemic, experts will benefit from knowing which triage metrics have citizen support. Design. We conducted an online survey in 20 countries, comparing support for 5 common metrics (prognosis, age, quality of life, past and future contribution as a health care worker) to a benchmark consisting of support for 2 no-triage mechanisms (first-come-first-served and random allocation). Results. We surveyed nationally representative samples of 1000 citizens in each of Brazil, France, Japan, and the United States and also self-selected samples from 20 countries (total N = 7599) obtained through a citizen science website (the Moral Machine). We computed the support for each metric by comparing its usability to the usability of the 2 no-triage mechanisms. We further analyzed the polarizing nature of each metric by considering its usability among participants who had a preference for no triage. In all countries, preferences were polarized, with the 2 largest groups preferring either no triage or extensive triage using all metrics. Prognosis was the least controversial metric. There was little support for giving priority to healthcare workers. Conclusions. It will be difficult to define triage guidelines that elicit public trust and approval. Given the importance of prognosis in triage protocols, it is reassuring that it is the least controversial metric. Experts will need to prepare strong arguments for other metrics if they wish to preserve public trust and morale during health crises.

Highlights : We collected citizen preferences regarding triage decisions about scarce medical resources from 20 countries.We find that citizen preferences are universally polarized.Citizens either prefer no triage (random allocation or first-come-first served) or extensive triage using all common triage metrics, with "prognosis" being the least controversial.Experts will need to prepare strong arguments to preserve or elicit public trust in triage decisions.

Awad Edmond, Bago Bence, Bonnefon Jean-François, Christakis Nicholas A, Rahwan Iyad, Shariff Azim

cross-cultural study, medical ethics, triage preferences

General General

Product pricing solutions using hybrid machine learning algorithm.

In Innovations in systems and software engineering

E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes-where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms.

Namburu Anupama, Selvaraj Prabha, Varsha M

2022-Jul-25

CatBoost, Ensemble algorithms, Product pricing, X-NGBoost, XGBoost

General General

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

In Signal, image and video processing

** : Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters.

Supplementary Information : The online version contains supplementary material available at 10.1007/s11760-022-02302-3.

Rajamani Kumar T, Rani Priya, Siebert Hanna, ElagiriRamalingam Rajkumar, Heinrich Mattias P

2022-Jul-25

Attention mechanism, Attention-augmented convolution, COVID-19, Consolidation, Ground-glass opacities, Segmentation, U-Net

General General

Deep Content Information Retrieval for COVID-19 Detection from Chromatic CT Scans.

In Arabian journal for science and engineering

In this paper, we investigate the role of the chromatic information in CT scans in COVID-19 detection and we aim to confirm the inclusion of the artificial intelligence findings in assisting COVID-19 diagnosis. This paper proposes a freezing-based convolutional neural network learning using a morphological transformation of CT images to classify COVID-19 cohorts to help in prognostication pneumonia disease monitoring. The experiments made on the collected CT images from previous works have proven to be a powerful aid to recognize the lesions in CT images which works at comprehensively greater accuracy and speed. The proposed CNN architecture has reflected the viral proliferation in infected patients and archives an accuracy of 87.56% with an improvement by 3% compared to the baseline method on the available database of CT images.

Sassi Ameni, Ouarda Wael, Amar Chokri Ben

2022-Jul-22

COVID-19, CT scans, Chromatic information, Convolutional neural network, Dilation, Erosion, Image retrieval

General General

Geographic microtargeting of social assistance with high-resolution poverty maps.

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

Hundreds of millions of poor families receive some form of targeted social assistance. Many of these antipoverty programs involve some degree of geographic targeting, where aid is prioritized to the poorest regions of the country. However, policy makers in many low-resource settings lack the disaggregated poverty data required to make effective geographic targeting decisions. Using several independent datasets from Nigeria, this paper shows that high-resolution poverty maps, constructed by applying machine learning algorithms to satellite imagery and other nontraditional geospatial data, can improve the targeting of government cash transfers to poor families. Specifically, we find that geographic targeting relying on machine learning-based poverty maps can reduce errors of exclusion and inclusion relative to geographic targeting based on recent nationally representative survey data. This result holds for antipoverty programs that target both the poor and the extreme poor and for initiatives of varying sizes. We also find no evidence that machine learning-based maps increase targeting disparities by demographic groups, such as gender or religion. Based in part on these findings, the Government of Nigeria used this approach to geographically target emergency cash transfers in response to the COVID-19 pandemic.

Smythe Isabella S, Blumenstock Joshua E

2022-Aug-09

Nigeria, poverty, satellite imagery, targeting

Internal Medicine Internal Medicine

Gastrointestinal sequalae months after severe acute respiratory syndrome corona virus 2 infection: a prospective, observational study.

In European journal of gastroenterology & hepatology

INTRODUCTION : Post-coronavirus disease (post-COVID) symptoms arise mostly from impaired function of respiratory tract although in many patients, the dysfunction of gastrointestinal tract and liver among other organ systems may persist.

METHODS : Primary data collection was based on a short gastrointestinal symptom questionnaire at the initial screening. A brief telephone survey within the patient and control group was performed 5-8 months after the initial screening. R ver. 4.0.5 and imbalanced RandomForest (RF) machine-learning algorithm were used for data explorations and analyses.

RESULTS : A total of 590 patients were included in the study. The general presence of gastrointestinal symptoms 208.2 days (153-230 days) after the initial acute severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection was 19% in patients with moderate-to-serious course of the disease and 7.3% in patients with mild course compared with 3.0% in SARS-CoV-2 negative controls (P < 0.001). Diarrhea and abdominal pain are the most prevalent post-COVID gastrointestinal symptoms. RF machine-learning algorithm identified acute diarrhea and antibiotics administration as the strongest predictors for gastrointestinal sequelae with area under curve of 0.68. Variable importance for acute diarrhea is 0.066 and 0.058 for antibiotics administration.

CONCLUSION : The presence of gastrointestinal sequelae 7 months after the initial SARS-CoV-2 infection is significantly higher in patients with moderate-to-severe course of the acute COVID-19 compared with asymptomatic patients or those with mild course of the disease. The most prevalent post-COVID gastrointestinal symptoms are diarrhea and abdominal pain. The strongest predictors for persistence of these symptoms are antibiotics administration and acute diarrhea during the initial infection.

Liptak Peter, Duricek Martin, Rosolanka Robert, Ziacikova Ivana, Kocan Ivan, Uhrik Peter, Grendar Marian, Hrnciarova Martina, Bucova Patricia, Galo David, Banovcin Peter

2022-Sep-01

Internal Medicine Internal Medicine

Gastrointestinal sequalae months after severe acute respiratory syndrome corona virus 2 infection: a prospective, observational study.

In European journal of gastroenterology & hepatology

INTRODUCTION : Post-coronavirus disease (post-COVID) symptoms arise mostly from impaired function of respiratory tract although in many patients, the dysfunction of gastrointestinal tract and liver among other organ systems may persist.

METHODS : Primary data collection was based on a short gastrointestinal symptom questionnaire at the initial screening. A brief telephone survey within the patient and control group was performed 5-8 months after the initial screening. R ver. 4.0.5 and imbalanced RandomForest (RF) machine-learning algorithm were used for data explorations and analyses.

RESULTS : A total of 590 patients were included in the study. The general presence of gastrointestinal symptoms 208.2 days (153-230 days) after the initial acute severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection was 19% in patients with moderate-to-serious course of the disease and 7.3% in patients with mild course compared with 3.0% in SARS-CoV-2 negative controls (P < 0.001). Diarrhea and abdominal pain are the most prevalent post-COVID gastrointestinal symptoms. RF machine-learning algorithm identified acute diarrhea and antibiotics administration as the strongest predictors for gastrointestinal sequelae with area under curve of 0.68. Variable importance for acute diarrhea is 0.066 and 0.058 for antibiotics administration.

CONCLUSION : The presence of gastrointestinal sequelae 7 months after the initial SARS-CoV-2 infection is significantly higher in patients with moderate-to-severe course of the acute COVID-19 compared with asymptomatic patients or those with mild course of the disease. The most prevalent post-COVID gastrointestinal symptoms are diarrhea and abdominal pain. The strongest predictors for persistence of these symptoms are antibiotics administration and acute diarrhea during the initial infection.

Liptak Peter, Duricek Martin, Rosolanka Robert, Ziacikova Ivana, Kocan Ivan, Uhrik Peter, Grendar Marian, Hrnciarova Martina, Bucova Patricia, Galo David, Banovcin Peter

2022-Jul-28

Cardiology Cardiology

COVID-19 telehealth preparedness: a cross-sectional assessment of cardiology practices in the USA.

In Personalized medicine

Aim: The COVID-19 pandemic forced medical practices to augment healthcare delivery to remote and virtual services. We describe the results of a nationwide survey of cardiovascular professionals regarding telehealth perspectives. Materials & methods: A 31-question survey was sent early in the pandemic to assess the impact of COVID-19 on telehealth adoption & reimbursement. Results: A total of 342 clinicians across 42 states participated. 77% were using telehealth, with the majority initiating usage 2 months after the COVID-19 shutdown. A variety of video-based systems were used. Telehealth integration requirements differed, with electronic medical record integration being mandated in more urban than rural practices (70 vs 59%; p < 0.005). Many implementation barriers surfaced, with over 75% of respondents emphasizing reimbursement uncertainty and concerns for telehealth generalizability given the complexity of cardiovascular diseases. Conclusion: Substantial variation exists in telehealth practices. Further studies and legislation are needed to improve access, reimbursement and the quality of telehealth-based cardiovascular care.

Waldman Carly E, Min Jean H, Wassif Heba, Freeman Andrew M, Rzeszut Anne K, Reilly Jack, Theriot Paul, Soliman Ahmed M, Thamman Ritu, Bhatt Ami, Bhavnani Sanjeev P

2022-Aug-01

COVID-19, access to care, electronic medical record, healthcare access, pandemic, payment parity, reimbursement, telehealth, telemedicine, video-visitations

General General

Association of COVID-19 with New-Onset Alzheimer's Disease.

In Journal of Alzheimer's disease : JAD

An infectious etiology of Alzheimer's disease has been postulated for decades. It remains unknown whether SARS-CoV-2 viral infection is associated with increased risk for Alzheimer's disease. In this retrospective cohort study of 6,245,282 older adults (age ≥65 years) who had medical encounters between 2/2020-5/2021, we show that people with COVID-19 were at significantly increased risk for new diagnosis of Alzheimer's disease within 360 days after the initial COVID-19 diagnosis (hazard ratio or HR:1.69, 95% CI: 1.53-1.72), especially in people age ≥85 years and in women. Our findings call for research to understand the underlying mechanisms and for continuous surveillance of long-term impacts of COVID-19 on Alzheimer's disease.

Wang Lindsey, Davis Pamela B, Volkow Nora D, Berger Nathan A, Kaelber David C, Xu Rong

2022-Jul-29

Alzheimer’s disease, COVID-19, electronic health records, viral etiology

General General

Real-time internet of medical things framework for early detection of Covid-19.

In Neural computing & applications

The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

Yildirim Emre, Cicioğlu Murtaza, Çalhan Ali

2022-Jul-24

Apache spark, Covid-19 diagnosis, Ensemble learning, Machine learning, Real-time analytics

General General

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.

In BMC medical imaging

BACKGROUND : Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module.

METHODS : Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers.

RESULTS : The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%).

CONCLUSIONS : MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.

Wang Wei, Jiang Yongbin, Wang Xin, Zhang Peng, Li Ji

2022-Jul-30

COVID-19, Chest X-Ray images, Convolutional neural network (CNN), Deep learning, MLES-Net

Cardiology Cardiology

Unsupervised machine learning demonstrates the prognostic value of TAPSE/PASP ratio among hospitalized patients with COVID-19.

In Echocardiography (Mount Kisco, N.Y.)

BACKGROUND : The ratio of tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP) is a validated index of right ventricular-pulmonary arterial (RV-PA) coupling with prognostic value. We determined the predictive value of TAPSE/PASP ratio and adverse clinical outcomes in hospitalized patients with COVID-19.

METHODS : Two hundred and twenty-nine consecutive hospitalized racially/ethnically diverse adults (≥18 years of age) admitted with COVID-19 between March and June 2020 with clinically indicated transthoracic echocardiograms (TTE) that included adequate tricuspid regurgitation (TR) velocities for calculation of PASP were studied. The exposure of interest was impaired RV-PA coupling as assessed by TAPSE/PASP ratio. The primary outcome was in-hospital mortality. Secondary endpoints comprised of ICU admission, incident acute respiratory distress syndrome (ARDS), and systolic heart failure.

RESULTS : One hundred and seventy-six patients had both technically adequate TAPSE measurements and measurable TR velocities for analysis. After adjustment for age, sex, BMI, race/ethnicity, diabetes mellitus, and smoking status, log(TAPSE/PASP) had a significantly inverse association with ICU admission (p = 0.015) and death (p = 0.038). ROC analysis showed the optimal cutoff for TAPSE/PASP for death was 0.51 mm mmHg-1 (AUC = 0.68). Unsupervised machine learning identified two groups of echocardiographic function. Of all echocardiographic measures included, TAPSE/PASP ratio was the most significant in predicting in-hospital mortality, further supporting its significance in this cohort.

CONCLUSION : Impaired RV-PA coupling, assessed noninvasively via the TAPSE/PASP ratio, was predictive of need for ICU level care and in-hospital mortality in hospitalized patients with COVID-19 suggesting utility of TAPSE/PASP in identification of poor clinical outcomes in this population both by traditional statistical and unsupervised machine learning based methods.

Jani Vivek, Kapoor Karan, Meyer Joseph, Lu Jim, Goerlich Erin, Metkus Thomas S, Madrazo Jose A, Michos Erin, Wu Katherine, Bavaro Nicole, Kutty Shelby, Hays Allison G, Mukherjee Monica

2022-Jul-30

COVID-19, echocardiography, right ventricular failure

Public Health Public Health

Within-host dynamics of SARS-CoV-2 infection: a systematic review and meta-analysis.

In Transboundary and emerging diseases ; h5-index 40.0

Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between December 1, 2019 and February 10, 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response, or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared, and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/mL), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/mL). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/mL]-1 day-1 ) and 0.92 (95% CI:-0.09, 1.93) day-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p<0.01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions. This article is protected by copyright. All rights reserved.

Du Zhanwei, Wang Shuqi, Bai Yuan, Gao Chao, Lau Eric H Y, Cowling Benjamin J

2022-Jul-30

COVID-19, Review, SARS-CoV-2, Viral dynamic parameters, Within-host model

Pathology Pathology

Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.

In PloS one ; h5-index 176.0

The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

Rashidi Hooman H, Pepper John, Howard Taylor, Klein Karina, May Larissa, Albahra Samer, Phinney Brett, Salemi Michelle R, Tran Nam K

2022

Surgery Surgery

Rapid prediction of in-hospital mortality among adults with COVID-19 disease.

In PloS one ; h5-index 176.0

BACKGROUND : We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission.

METHODS : This retrospective study included 13,190 racially and ethnically diverse adults admitted to one of the New York City Health + Hospitals (NYC H+H) system for COVID-19 illness between March 1 and June 30, 2020. Demographic characteristics, simple vital signs and routine clinical laboratory tests were collected from the electronic medical records. A clinical prediction model to estimate the risk of dying during the hospitalization were developed.

RESULTS : Mean age (interquartile range) was 58 (45-72) years; 5421 (41%) were women, 5258 were Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 were (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables-oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine-that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5-1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5-98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/.

CONCLUSIONS : In a diverse population of hospitalized patients with COVID-19 illness, a clinical prediction model using a few readily available vital signs reflecting the severity of disease may precisely predict in-hospital mortality in diverse populations and can rapidly assist decisions to prioritize admissions and intensive care.

Kim Kyoung Min, Evans Daniel S, Jacobson Jessica, Jiang Xiaqing, Browner Warren, Cummings Steven R

2022

oncology Oncology

A Stress and Pain Self-management mHealth App for Adult Outpatients With Sickle Cell Disease: Protocol for a Randomized Controlled Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : This paper describes the research protocol for a randomized controlled trial of a self-management intervention for adults diagnosed with sickle cell disease (SCD). People living with SCD experience lifelong recurrent episodes of acute and chronic pain, which are exacerbated by stress.

OBJECTIVE : This study aims to decrease stress and improve SCD pain control with reduced opioid use through an intervention with self-management relaxation exercises, named You Cope, We Support (YCWS). Building on our previous findings from formative studies, this study is designed to test the efficacy of YCWS on stress intensity, pain intensity, and opioid use in adults with SCD.

METHODS : A randomized controlled trial of the short-term (8 weeks) and long-term (6 months) effects of YCWS on stress, pain, and opioid use will be conducted with 170 adults with SCD. Patients will be randomized based on 1:1 ratio (stratified on pain intensity [≤5 or >5]) to be either in the experimental (self-monitoring of outcomes, alerts or reminders, and use of YCWS [relaxation and distraction exercises and support]) or control (self-monitoring of outcomes and alerts or reminders) group. Patients will be asked to report outcomes daily. During weeks 1 to 8, patients in both groups will receive system-generated alerts or reminders via phone call, text, or email to facilitate data entry (both groups) and intervention use support (experimental). If the participant does not enter data after 24 hours, the study support staff will contact them for data entry troubleshooting (both groups) and YCWS use (experimental). We will time stamp and track patients' web-based activities to understand the study context and conduct exit interviews on the acceptability of system-generated and staff support. This study was approved by our institutional review board.

RESULTS : This study was funded by the National Institute of Nursing Research of the National Institutes of Health in 2020. The study began in March 2021 and will be completed in June 2025. As of April 2022, we have enrolled 45.9% (78/170) of patients. We will analyze the data using mixed effects regression models (short term and long term) to account for the repeated measurements over time and use machine learning to construct and evaluate prediction models. Owing to the COVID-19 pandemic, the study was modified to allow for mail-in consent process, internet-based consent process via email or Zoom videoconference, devices delivered by FedEx, and training via Zoom videoconference.

CONCLUSIONS : We expect the intervention group to report reductions in pain intensity (primary outcome; 0-10 scale) and in stress intensity (0-10 scale) and opioid use (Wisepill event medication monitoring system), which are secondary outcomes. Our study will contribute to advancing the use of nonopioid therapy such as guided relaxation and distraction techniques for managing SCD pain.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04484272; https://clinicaltrials.gov/ct2/show/NCT04484272.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : PRR1-10.2196/33818.

Ezenwa Miriam O, Yao Yingwei, Mandernach Molly W, Fedele David A, Lucero Robert J, Corless Inge, Dyal Brenda W, Belkin Mary H, Rohatgi Abhinav, Wilkie Diana J

2022-Jul-29

analgesics, intervention, opioid use, pain, protocol, randomized controlled trial, self-management, sickle cell disease, stress, support

General General

Providing Care Beyond Therapy Sessions With a Natural Language Processing-Based Recommender System That Identifies Cancer Patients Who Experience Psychosocial Challenges and Provides Self-care Support: Pilot Study.

In JMIR cancer

BACKGROUND : The negative psychosocial impacts of cancer diagnoses and treatments are well documented. Virtual care has become an essential mode of care delivery during the COVID-19 pandemic, and online support groups (OSGs) have been shown to improve accessibility to psychosocial and supportive care. de Souza Institute offers CancerChatCanada, a therapist-led OSG service where sessions are monitored by an artificial intelligence-based co-facilitator (AICF). The AICF is equipped with a recommender system that uses natural language processing to tailor online resources to patients according to their psychosocial needs.

OBJECTIVE : We aimed to outline the development protocol and evaluate the AICF on its precision and recall in recommending resources to cancer OSG members.

METHODS : Human input informed the design and evaluation of the AICF on its ability to (1) appropriately identify keywords indicating a psychosocial concern and (2) recommend the most appropriate online resource to the OSG member expressing each concern. Three rounds of human evaluation and algorithm improvement were performed iteratively.

RESULTS : We evaluated 7190 outputs and achieved a precision of 0.797, a recall of 0.981, and an F1 score of 0.880 by the third round of evaluation. Resources were recommended to 48 patients, and 25 (52%) accessed at least one resource. Of those who accessed the resources, 19 (75%) found them useful.

CONCLUSIONS : The preliminary findings suggest that the AICF can help provide tailored support for cancer OSG members with high precision, recall, and satisfaction. The AICF has undergone rigorous human evaluation, and the results provide much-needed evidence, while outlining potential strengths and weaknesses for future applications in supportive care.

Leung Yvonne W, Park Bomi, Heo Rachel, Adikari Achini, Chackochan Suja, Wong Jiahui, Alie Elyse, Gancarz Mathew, Kacala Martyna, Hirst Graeme, de Silva Daswin, French Leon, Bender Jacqueline, Mishna Faye, Gratzer David, Alahakoon Damminda, Esplen Mary Jane

2022-Jul-29

artificial intelligence, natural language processing, online support groups, recommender system, supportive care in cancer

Public Health Public Health

Dynamics of Respiratory Infectious Diseases in Incarcerated and Free-Living Populations: A Simulation Modeling Study.

In Medical decision making : an international journal of the Society for Medical Decision Making

BACKGROUND : Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern.

METHODS : We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics.

RESULTS : We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite.

CONCLUSIONS : We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly.

HIGHLIGHTS : We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.

Weyant Christopher, Lee Serin, Andrews Jason R, Alarid-Escudero Fernando, Goldhaber-Fiebert Jeremy D

2022-Jul-29

COVID-19, correctional facility, infectious diseases, meta-model

General General

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

In Frontiers in genetics ; h5-index 62.0

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Hu Yue, Rehawi Ghalia, Moyon Lambert, Gerstner Nathalie, Ogris Christoph, Knauer-Arloth Janine, Bittner Florian, Marsico Annalisa, Mueller Nikola S

2022

COVID-19, machine learning, multi-omic integration, network embedding, network inference, polygenic risk score (PRS)

General General

A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients.

In PloS one ; h5-index 176.0

INTRODUCTION : Identifying COVID-19 patients that are most likely to progress to a severe infection is crucial for optimizing care management and increasing the likelihood of survival. This study presents a machine learning model that predicts severe cases of COVID-19, defined as the presence of Acute Respiratory Distress Syndrome (ARDS) and highlights the different risk factors that play a significant role in disease progression.

METHODS : A cohort composed of 289,351 patients diagnosed with COVID-19 in April 2020 was created using US administrative claims data from Oct 2015 to Jul 2020. For each patient, information about 817 diagnoses, were collected from the medical history ahead of COVID-19 infection. The primary outcome of the study was the presence of ARDS in the 4 months following COVID-19 infection. The study cohort was randomly split into training set used for model development, test set for model evaluation and validation set for real-world performance estimation.

RESULTS : We analyzed three machine learning classifiers to predict the presence of ARDS. Among the algorithms considered, a Gradient Boosting Decision Tree had the highest performance with an AUC of 0.695 (95% CI, 0.679-0.709) and an AUPRC of 0.0730 (95% CI, 0.0676 - 0.0823), showing a 40% performance increase in performance against a baseline classifier. A panel of five clinicians was also used to compare the predictive ability of the model to that of clinical experts. The comparison indicated that our model is on par or outperforms predictions made by the clinicians, both in terms of precision and recall.

CONCLUSION : This study presents a machine learning model that uses patient claims history to predict ARDS. The risk factors used by the model to perform its predictions have been extensively linked to the severity of the COVID-19 in the specialized literature. The most contributing diagnosis can be easily retrieved in the patient clinical history and can be used for an early screening of infected patients. Overall, the proposed model could be a promising tool to deploy in a healthcare setting to facilitate and optimize the care of COVID-19 patients.

Lazzarini Nicola, Filippoupolitis Avgoustinos, Manzione Pedro, Eleftherohorinou Hariklia

2022

Radiology Radiology

A comparison of Covid-19 early detection between convolutional neural networks and radiologists.

In Insights into imaging

BACKGROUND : The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.

METHODS : The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx.

RESULTS : Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx.

CONCLUSION : The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.

Albiol Alberto, Albiol Francisco, Paredes Roberto, Plasencia-Martínez Juana María, Blanco Barrio Ana, Santos José M García, Tortajada Salvador, González Montaño Victoria M, Rodríguez Godoy Clara E, Fernández Gómez Saray, Oliver-Garcia Elena, de la Iglesia Vayá María, Márquez Pérez Francisca L, Rayo Madrid Juan I

2022-Jul-28

Covid-19, Deep learning, Radiology

General General

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis.

In medRxiv : the preprint server for health sciences

Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.

Sambarey Awanti, Smith Kirk, Chung Carolina, Arora Harkirat Singh, Yang Zhenhua, Agarwal Prachi, Chandrasekaran Sriram

2022-Jul-21

General General

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier.

In Digital health

Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including 'not survived', 'recovered', and 'not recovered' based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.

Saad Eysha, Sadiq Saima, Jamil Ramish, Rustam Furqan, Mehmood Arif, Choi Gyu Sang, Ashraf Imran

COVID-19, adverse reactions, machine learning, post-vaccination symptoms

General General

Medical students' intention to integrate digital health into their medical practice: A pre-peri COVID-19 survey study in Canada.

In Digital health

Objective : We aimed to explore the factors that influence medical students' intention to integrate dHealth technologies in their practice and analyze the influence of the COVID-19 pandemic on their perceptions and intention.

Methods : We conducted a two-phased survey study at the University of Montreal's medical school in Canada. The study population consisted of 1367 medical students. The survey questionnaire was administered in two phases, that is, an initial survey (t0) in February 2020, before the Covid-19 pandemic, and a replication survey (t1) in January 2021, during the pandemic. Component-based structural equation modeling (SEM) was used to test seven research hypotheses.

Results : A total of 184 students responded to the survey at t0 (13%), whereas 138 responded to the survey at t1 (10%). Findings reveal that students, especially those who are in their preclinical years, had little occasion to experiment with dHealth technologies during their degree. This lack of exposure may explain why a vast majority felt that dHealth should be integrated into medical education. Most respondents declared an intention to integrate dHealth, including AI-based tools, into their future medical practice. One of the most salient differences observed between t0 and t1 brings telemedicine to the forefront of medical education. SEM results confirm the explanatory power of the proposed research model.

Conclusions : The present study unveils the specific dHealth technologies that could be integrated into existing medical curricula. Formal training would increase students' competencies with these technologies which, in turn, could ease their adoption and effective use in their practice.

Paré Guy, Raymond Louis, Pomey Marie-Pascale, Grégoire Geneviève, Castonguay Alexandre, Ouimet Antoine Grenier

COVID-19, Digital health, artificial intelligence, eHealth, medical education, medical practice, survey

Radiology Radiology

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.

In International journal of molecular sciences ; h5-index 102.0

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted &gt;50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

Flora James, Khan Wasiq, Jin Jennifer, Jin Daniel, Hussain Abir, Dajani Khalil, Khan Bilal

2022-Jul-26

COVID-19, VAERS, adverse events, association rule mining, bipartite graphs, hierarchical clustering, self-organizing maps, vaccine analysis workflow, vaccine development

General General

Bibliometric Analysis of Health Technology Research: 1990~2020.

In International journal of environmental research and public health ; h5-index 73.0

This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016-2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.

Luo Xiaomei, Wu Yuduo, Niu Lina, Huang Lucheng

2022-Jul-25

Citespace, VOSviewer, bibliometrics, emerging research topic, healthy technology, research frontier

General General

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.

In International journal of environmental research and public health ; h5-index 73.0

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Gianquintieri Lorenzo, Brovelli Maria Antonia, Pagliosa Andrea, Dassi Gabriele, Brambilla Piero Maria, Bonora Rodolfo, Sechi Giuseppe Maria, Caiani Enrico Gianluca

2022-Jul-25

COVID-19, emergency medical services, geo-AI, geographic information system, health geomatics, machine learning, resources management, spatial filtering

Radiology Radiology

Deep learning for understanding multilabel imbalanced Chest X-ray datasets

ArXiv Preprint

Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as "multilabel classification problems". A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make the decision.

Helena Liz, Javier Huertas-Tato, Manuel Sánchez-Montañés, Javier Del Ser, David Camacho

2022-07-28

Public Health Public Health

DTLMV2-A real-time deep transfer learning mask classifier for overcrowded spaces.

In Applied soft computing

Through the commencement of the COVID-19 pandemic, the whole globe is in disarray and debating on unique approaches to stop this viral transmission. Masks are being worn by people all around the world as one of the preventative measures to avoid contracting this sickness. Although some people are following and adopting this precaution, others are not, despite official recommendations from the administration and public health organisations has been announced. In this paper DTLMV2 (Deep Transfer Learning MobileNetV2 for the objective of classification) is proposed - A face mask identification model that can reliably determine whether an individual is wearing a mask or not is suggested and implemented in this work. The model architecture employs the peruse of MobileNetV2, a lightweight Convolutional Neural Network (CNN) that requires less computing power and can be readily integrated into computer vision and mobile systems. The computer vision with MobileNet is required to formulate a low-cost mask detection system for a group of people in open spaces that can assist in determining whether a person is wearing a mask or not, as well as function as a surveillance system since it is effective on both real-time pictures and videos. The face recognition model obtained 97.01% accuracy on validation data, 98% accuracy on training data and 97.45% accuracy on testing data.

Gupta Meenu, Chaudhary Gopal, Bansal Dhruvi, Pandey Shashwat

2022-Jul-21

CNN, Computer vision, Covid19, Deep learning, Mask classifier, MobileNetV2, Object detection

General General

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey.

In Sustainable cities and society

Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors' best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.

Himeur Yassine, Al-Maadeed Somaya, Almadeed Noor, Abualsaud Khalid, Mohamed Amr, Khattab Tamer, Elharrouss Omar

2022-Jul-21

Bird’s eye view, Convolutional neural networks, Euclidean distance, Pedestrian detection, Transfer learning, Visual social distancing monitoring

General General

A Hybrid Random Forest Deep learning Classifier Empowered Edge Cloud Architecture for COVID-19 and Pneumonia Detection.

In Expert systems with applications

COVID-19 is a global pandemic that mostly affects patients' respiratory systems, and the only way to protect oneself against the virus at present moment is to diagnose the illness, isolate the patient, and provide immunization. In the present situation, the testing used to predict COVID-19 is inefficient and results in more false positives. This difficulty can be solved by developing a remote medical decision support system that detects illness using CT scans or X-ray images with less manual interaction and is less prone to errors. The state-of-art techniques mainly used complex deep learning architectures which are not quite effective when deployed in resource-constrained edge devices. To overcome this problem, a multi-objective Modified Heat Transfer Search (MOMHTS) optimized hybrid Random Forest Deep learning (HRFDL) classifier is proposed in this paper. The MOMHTS algorithm mainly optimizes the deep learning model in the HRFDL architecture by optimizing the hyperparameters associated with it to support the resource-constrained edge devices. To evaluate the efficiency of this technique, extensive experimentation is conducted on two real-time datasets namely the COVID19 lung CT scan dataset and the Chest X-Ray images (Pneumonia) datasets. The proposed methodology mainly offers increased speed for communication between the IoT devices and COVID-19 detection via the MOMHTS optimized HRFDL classifier is modified to support the resources which can only support minimal computation and handle minimum storage. The proposed methodology offers an accuracy of 99% for both the COVID19 lung CT scan dataset and the Chest X-Ray images (Pneumonia) datasets with minimal computational time, cost, and storage. Based on the simulation outcomes, we can conclude that the proposed methodology is an appropriate fit for edge computing detection to identify the COVID19 and pneumonia with higher detection accuracy.

Hemalatha Murugan

2022-Jul-21

Healthcare industry, Heat Transfer Search Algorithm, Web Services, and Random Forest, and cloud computing, deep learning

General General

Prediction of viral-host interactions of COVID-19 by computational methods.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.

Alakus Talha Burak, Turkoglu Ibrahim

2022-Jul-21

Covid-19, Deep learning, Protein mapping, SARS-CoV-2 virus

General General

REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data

bioRxiv Preprint

In the recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aim is to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.

Ayuso Munoz, A.; Ugarte Carro, E.; Prieto Santamaria, L.; Otero Carrasco, B.; Menasalvas Ruiz, E.; Perez Gallardo, Y.; Rodriguez-Gonzalez, A.

2022-07-27

General General

MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction

ArXiv Preprint

The COVID-19 pandemic has caused devastating economic and social disruption, straining the resources of healthcare institutions worldwide. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform distribution of limited healthcare resources. We respond to one of these calls specific to the pediatric population. To address this challenge, we study two prediction tasks for the pediatric population using electronic health records: 1) predicting which children are more likely to be hospitalized, and 2) among hospitalized children, which individuals are more likely to develop severe symptoms. We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts and incorporates the inter-feature relationships between heterogeneous medical features via graph neural networks (GNN). We evaluate MedML across 143,605 patients for the hospitalization prediction task and 11,465 patients for the severity prediction task using data from the National Cohort Collaborative (N3C) dataset. We also report detailed group-level and individual-level feature importance analyses to evaluate the model interpretability. MedML achieves up to a 7% higher AUROC score and up to a 14% higher AUPRC score compared to the best baseline machine learning models and performs well across all nine national geographic regions and over all three-month spans since the start of the pandemic. Our cross-disciplinary research team has developed a method of incorporating clinical domain knowledge as the framework for a new type of machine learning model that is more predictive and explainable than current state-of-the-art data-driven feature selection methods.

Junyi Gao, Chaoqi Yang, George Heintz, Scott Barrows, Elise Albers, Mary Stapel, Sara Warfield, Adam Cross, Jimeng Sun, the N3C consortium

2022-07-25

General General

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit

ArXiv Preprint

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

Nicky Nirlipta Sahoo, Balamurali Murugesan, Ayantika Das, Srinivasa Karthik, Keerthi Ram, Steffen Leonhardt, Jayaraj Joseph, Mohanasankar Sivaprakasam

2022-07-25

General General

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

In The Lancet. Digital health

BACKGROUND : Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

METHODS : We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.

FINDINGS : The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).

INTERPRETATION : Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.

FUNDING : Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Myall Ashleigh, Price James R, Peach Robert L, Abbas Mohamed, Mookerjee Sid, Zhu Nina, Ahmad Isa, Ming Damien, Ramzan Farzan, Teixeira Daniel, Graf Christophe, Weiße Andrea Y, Harbarth Stephan, Holmes Alison, Barahona Mauricio

2022-Aug

General General

Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019.

In Resuscitation ; h5-index 66.0

BACKGROUND : Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

METHODS : We conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted machine models developed in patients without COVID-19 to the COVID-19 cohort. We also updated the neural network model using transfer learning. Performance was compared between models and the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score.

RESULTS : Among the 4,125 patients with COVID-19 included in the analysis, 484 (12%) patients survived with favorable neurological outcomes. The gradient boosted machine, trained on non-COVID-19 patients was the best performing model for predicting neurological outcomes in COVID-19 patients, significantly better than the CASPRI score (c-statistic: 0.75 vs. 0.67, P < 0.001). While calibration improved for the neural network with transfer learning, it did not surpass the gradient boosted machine in terms of discrimination.

CONCLUSION : Our gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.

Mayampurath Anoop, Bashiri Fereshteh, Hagopian Raffi, Venable Laura, Carey Kyle, Edelson Dana, Churpek Matthew

2022-Jul-19

cardiac arrest, machine learning, neurological outcomes, prediction

General General

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation.

In Computers in biology and medicine

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.

Qi Ailiang, Zhao Dong, Yu Fanhua, Heidari Ali Asghar, Wu Zongda, Cai Zhennao, Alenezi Fayadh, Mansour Romany F, Chen Huiling, Chen Mayun

2022-Jul-13

ACO, Ant colony optimization, COVID-19 X-ray, Image segmentation, Optimization, Swarm intelligence

General General

Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study.

In Computers in biology and medicine

The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.

Nadimi-Shahraki Mohammad H, Zamani Hoda, Mirjalili Seyedali

2022-Jul-16

Binary whale optimization algorithm, COVID-19, Classification, Feature selection, Medical data mining, Transfer functions

Radiology Radiology

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

In Medicine

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Li Matthew D, Arun Nishanth T, Aggarwal Mehak, Gupta Sharut, Singh Praveer, Little Brent P, Mendoza Dexter P, Corradi Gustavo C A, Takahashi Marcelo S, Ferraciolli Suely F, Succi Marc D, Lang Min, Bizzo Bernardo C, Dayan Ittai, Kitamura Felipe C, Kalpathy-Cramer Jayashree

2022-Jul-22

General General

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics.

In Frontiers in bioscience (Landmark edition)

BACKGROUND : The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome play a key role in controlling the pandemic. Recent studies have highlighted the role of point-of-care ultrasound imaging for COVID-19 screening and prognosis, particularly given that it is non-invasive, globally available, and easy-to-sanitize. COVIDx-US Dataset: Motivated by these attributes and the promise of artificial intelligence tools to aid clinicians, we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related ultrasound imaging data. The COVIDx-US dataset was curated from multiple data sources and its current version, i.e., v1.5., consists of 173 ultrasound videos and 21,570 processed images across 147 patients with COVID-19 infection, non-COVID-19 infection, other lung diseases/conditions, as well as normal control cases.

CONCLUSIONS : The COVIDx-US dataset was released as part of a large open-source initiative, the COVID-Net initiative, and will be continuously growing, as more data sources become available. To the best of the authors' knowledge, COVIDx-US is the first and largest open-access fully-curated benchmark lung ultrasound imaging dataset that contains a standardized and unified lung ultrasound score per video file, providing better interpretation while enabling other research avenues such as severity assessment. In addition, the dataset is reproducible, easy-to-use, and easy-to-scale thanks to the well-documented modular design.

Ebadi Ashkan, Xi Pengcheng, MacLean Alexander, Florea Adrian, Tremblay Stéphane, Kohli Sonny, Wong Alexander

2022-Jun-24

COVID-19, artificial intelligence, curated dataset, open-access, ultrasound imaging

General General

The mechanical ventilator of the future: a breath of hope for the viral pandemics to come.

In The Pan African medical journal

Respiratory care for the critically ill is a complex and difficult duty to accomplish. By replicating human knowledge with automated algorithms, artificial intelligence could provide solutions to facilitate this multidisciplinary task in developing countries, especially during humanitarian crisis, as the COVID-19 pandemic. This article provides an overview on the subject, from the emergent nations perspective.

Filho Luiz Alberto Cerqueira Batista

2022

Artificial intelligence, COVID-19, critical care, machine learning, mechanical ventilation

General General

Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

In Computational intelligence and neuroscience

COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID-19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning-coronavirus 19 (Ftl-CoV19) for COVID-19 detection through chest X-rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl-CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.

Singh Tarishi, Saurabh Praneet, Bisen Dhananjay, Kane Lalit, Pathak Mayank, Sinha G R

2022

Radiology Radiology

Human vs Artificial Intelligence-Based Echocardiography Analysis as Predictor of Outcomes: An analysis from the World Alliance Societies of Echocardiography COVID study.

In Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

BACKGROUND : Transthoracic echocardiography (TTE) is the leading cardiac imaging modality for patients admitted with COVID-19 infection, a condition of high short-term mortality. We aimed to test the hypothesis that artificial intelligence (AI) based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert.

METHODS : Patients admitted to 13 hospitals for acute COVID-19 disease who had a TTE were included. Left ventricular (LV) ejection fraction (EF) and LV longitudinal strain (LS) were obtained manually by multiple expert readers and by an automated, AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared.

RESULTS : 870 patients were enrolled, mortality was 27.4% at a follow-up of 230±115 days. AI analysis had lower variability than manual for both LV EF (p=0.003) and LS (p=0.005). AI-derived LV EF and LS were predictors of mortality in univariable and multivariable regression analysis (OR=0.974, 95% CI= 0.956-0.991, p=0.003 for EF; OR=1.060, 95% CI 1.019-1.105, p=0.004 for LS), but LV EF and LS obtained by manual analysis were not. Direct comparison of predictive value of AI vs manual measurements of LV EF and LS was significantly better for AI (p=0.005 and 0.003 respectively). In addition, AI-derived LV EF and LS had more significant and stronger correlations to other objective biomarkers for acute disease than manual reads.

CONCLUSIONS : AI-based analysis of LVEF and LVLS had a similar feasibility to manual analysis, minimized variability and consequently increased the statistical power to predict mortality. AI-based analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.

Asch Federico M, Descamps Tine, Sarwar Rizwan, Karagodin Ilya, Singulane Cristiane Carvalho, Xie Mingxing, Tucay Edwin S, Tude Rodrigues Ana C, Vasquez-Ortiz Zuilma Y, Monaghan Mark J, Ordonez Salazar Bayardo A, Soulat-Dufour Laurie, Alizadehasl Azin, Mostafavi Atoosa, Moreo Antonella, Citro Rodolfo, Narang Akhil, Wu Chun, Addetia Karima, Upton Ross, Woodward Gary M, Lang Roberto M

2022-Jul-18

Artificial Intelligence, COVID-19, Echocardiography, Left Ventricular Function, Machine Learning, Outcomes Prediction, WASE

General General

VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies. Therefore, there is a need to develop a predictive model that can incorporate mutational information to make reasonable predictions about the current pandemic situation.

METHODS : This paper proposes a deep learning prediction framework, VOC-DL, based on Variants Of Concern (VOC). The framework uses slope feature method to process the time series dataset containing VOC variant information, and uses VOC-LSTM, VOC-GRU and VOC-BILSTM prediction models included in the framework to predict the daily newly confirmed cases.

RESULTS : We analyzed daily newly confirmed cases in Italy, South Korea, Russia, Japan and India from April 14th, 2021 to July 3rd, 2021. The experimental results show that all VOC-DL models proposed in this paper can accurately predict the pandemic trend in the medium and long term, and VOC-LSTM model has the best prediction performance, with the highest average determination coefficient R2 of 96.83% in five nations' datasets. The overall prediction has robustness.

CONCLUSIONS : The experimental results show that VOC-LSTM is the best predictor for such a series of data and has higher prediction accuracy in the long run. At the same time, our VOC-DL framework combining VOC variants has reference significance for predicting other variants in the future.

Liao Zhifang, Song Yucheng, Ren Shengbing, Song Xiaomeng, Fan Xiaoping, Liao Zhining

2022-Jun-30

COVID-19, LSTM, Prediction, Time series, VOC-DL model, Variant

General General

Resting-state functional connectome predicts individual differences in depression during COVID-19 pandemic.

In The American psychologist

Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM's ability for capturing the depression-related patterns with individuals' resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mao Yu, Chen Qunlin, Wei Dongtao, Yang Wenjing, Sun Jiangzhou, Yu Yaxu, Zhuang Kaixiang, Wang Xiaoqin, He Li, Feng Tingyong, Lei Xu, He Qinghua, Chen Hong, Duan Shukai, Qiu Jiang

2022-Jul-21

Public Health Public Health

Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals.

In The Journal of international medical research

OBJECTIVES : Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19.

METHODS : This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities.

RESULTS : Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%.

CONCLUSION : Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting.

Ahmed Marwa M, Sayed Amal M, El Abd Dina, El Sayed Inas T, Elkholy Yasmine S, Fares Ahmed H, Fares Samar

2022-Jul

Primary care, coronavirus disease 2019, deep learning, early detection, neural network, severe acute respiratory syndrome coronavirus 2

Public Health Public Health

Predicting a Positive Antibody Response After 2 SARS-CoV-2 mRNA Vaccines in Transplant Recipients: A Machine Learning Approach With External Validation.

In Transplantation ; h5-index 56.0

BACKGROUND : Solid organ transplant recipients (SOTRs) are less likely to mount an antibody response to SARS-CoV-2 mRNA vaccines. Understanding risk factors for impaired vaccine response can guide strategies for antibody testing and additional vaccine dose recommendations.

METHODS : Using a nationwide observational cohort of 1031 SOTRs, we created a machine learning model to explore, identify, rank, and quantify the association of 19 clinical factors with antibody responses to 2 doses of SARS-CoV-2 mRNA vaccines. External validation of the model was performed using a cohort of 512 SOTRs at Houston Methodist Hospital.

RESULTS : Mycophenolate mofetil use, a shorter time since transplant, and older age were the strongest predictors of a negative antibody response, collectively contributing to 76% of the model's prediction performance. Other clinical factors, including transplanted organ, vaccine type (mRNA-1273 versus BNT162b2), sex, race, and other immunosuppressants, showed comparatively weaker associations with an antibody response. This model showed moderate prediction performance, with an area under the receiver operating characteristic curve of 0.79 in our cohort and 0.67 in the external validation cohort. An online calculator based on our prediction model is available at http://transplantmodels.com/covidvaccine/.

CONCLUSIONS : Our machine learning model helps understand which transplant patients need closer follow-up and additional doses of vaccine to achieve protective immunity. The online calculator based on this model can be incorporated into transplant providers' practice to facilitate patient-centric, precision risk stratification and inform vaccination strategies among SOTRs.

Alejo Jennifer L, Mitchell Jonathan, Chiang Teresa P-Y, Chang Amy, Abedon Aura T, Werbel William A, Boyarsky Brian J, Zeiser Laura B, Avery Robin K, Tobian Aaron A R, Levan Macey L, Warren Daniel S, Massie Allan B, Moore Linda W, Guha Ashrith, Huang Howard J, Knight Richard J, Gaber Ahmed Osama, Ghobrial Rafik Mark, Garonzik-Wang Jacqueline M, Segev Dorry L, Bae Sunjae

2022-Jul-21

General General

Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses.

In ACS omega

The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, a machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing a machine learning-based novel coronavirus prediction technique, called COVID-Predictor, where 1000 sequences of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and other viruses are used to train a Naive Bayes classifier so that it can predict any unknown sequences of these viruses. The model has been validated using 10-fold cross-validation in comparison with other machine learning techniques. The results show the superiority of our predictor by achieving an average 99.7% accuracy on an unseen validation set of viruses. The same pre-trained model has been used to design a web-based application where sequences of unknown viruses can be uploaded to predict the novel coronavirus.

Sarkar Jnanendra Prasad, Saha Indrajit, Ghosh Nimisha, Maity Debasree, Plewczynski Dariusz

2022-Jul-12

General General

Review on the COVID-19 pandemic prevention and control system based on AI.

In Engineering applications of artificial intelligence

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article's primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies.

Yi Junfei, Zhang Hui, Mao Jianxu, Chen Yurong, Zhong Hang, Wang Yaonan

2022-Sep

Artificial intelligence, COVID-19 big data, Intelligent equipment and systems, Intelligent robots

General General

Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM.

In Health information science and systems

With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the "Tree Hole". The purpose of this article is to support the "Tree Hole" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of "Tree Hole" named "Zou Fan", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of "Tree Hole" messages in multiple time dimensions is positively correlated to emotion. The longer the "Tree Hole" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of "Tree Hole" rescue, volunteers should focus on the long-formed "Tree Hole" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.

Guo Chaohui, Lin Shaofu, Huang Zhisheng, Yao Yahong

2022-Dec

Adversarial training, BERT+BiLSTM, Depression, Sentiment analysis, Time feature

General General

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine.

In BioMed research international ; h5-index 102.0

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Sharma Ashwani, Virmani Tarun, Pathak Vipluv, Sharma Anjali, Pathak Kamla, Kumar Girish, Pathak Devender

2022

General General

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.

In Computational intelligence and neuroscience

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

Khan Muhammad Attique, Azhar Marium, Ibrar Kainat, Alqahtani Abdullah, Alsubai Shtwai, Binbusayyis Adel, Kim Ye Jin, Chang Byoungchol

2022

General General

An AI-enabled pre-trained model-based Covid detection model using chest X-ray images.

In Multimedia tools and applications

The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.

Gupta Rajeev Kumar, Kunhare Nilesh, Pathik Nikhlesh, Pathik Babita

2022-Jul-12

Convolution neural network, Covid-19, InceptionResNetV2, MobileNetV2, Pre-trained model, Resnet50, VGG19

Cardiology Cardiology

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.

In Frontiers in public health

The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.

Ramírez-Del Real Tania, Martínez-García Mireya, Márquez Manlio F, López-Trejo Laura, Gutiérrez-Esparza Guadalupe, Hernández-Lemus Enrique

2022

COVID-19, feature selection, imbalanced data, machine learning, predictive model

General General

A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.

In Frontiers in immunology ; h5-index 100.0

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

Lu Shuai, Li Yuguang, Ma Qiang, Nan Xiaofei, Zhang Shoutao

2022

B-cell epitopes prediction, Bi-LSTM, GCN, SARS-CoV-2, attention, structure-based

General General

Data-Centric Epidemic Forecasting: A Survey

ArXiv Preprint

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.

Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash

2022-07-19

Public Health Public Health

Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study.

In Frontiers in immunology ; h5-index 100.0

Background : Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited.

Objectives : To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively.

Findings : 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests.

Conclusions : Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

Sánchez-Montalvá Adrián, Álvarez-Sierra Daniel, Martínez-Gallo Mónica, Perurena-Prieto Janire, Arrese-Muñoz Iria, Ruiz-Rodríguez Juan Carlos, Espinosa-Pereiro Juan, Bosch-Nicolau Pau, Martínez-Gómez Xavier, Antón Andrés, Martínez-Valle Ferran, Riveiro-Barciela Mar, Blanco-Grau Albert, Rodríguez-Frias Francisco, Castellano-Escuder Pol, Poyatos-Canton Elisabet, Bas-Minguet Jordi, Martínez-Cáceres Eva, Sánchez-Pla Alex, Zurera-Egea Coral, Teniente-Serra Aina, Hernández-González Manuel, Pujol-Borrell Ricardo

2022

CXCL10, SARS-CoV-2 infection, acute phase reactants, chemokines, clinical laboratory tests, cytokines, flow cytometry, predictive risk-profile

General General

Discovering novel systemic biomarkers in photos of the external eye

ArXiv Preprint

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared against baseline models incorporating available clinicodemographic variables (e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on validation set A (a patient population similar to the development sets), where the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7.3-13.2%. Our findings provide further evidence that external eye photos contain important biomarkers of systemic health spanning multiple organ systems. Further work is needed to investigate whether and how these biomarkers can be translated into clinical impact.

Boris Babenko, Ilana Traynis, Christina Chen, Preeti Singh, Akib Uddin, Jorge Cuadros, Lauren P. Daskivich, April Y. Maa, Ramasamy Kim, Eugene Yu-Chuan Kang, Yossi Matias, Greg S. Corrado, Lily Peng, Dale R. Webster, Christopher Semturs, Jonathan Krause, Avinash V. Varadarajan, Naama Hammel, Yun Liu

2022-07-19

Public Health Public Health

Synergy between Public and Private Healthcare Organizations during COVID-19 on Twitter.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Social media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and NGOs for communicating health concerns, new advancements, and potential outbreaks. While the benefits of using them as a tool have been extensively discussed, the online activity of various healthcare organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated.

OBJECTIVE : The purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations.

METHODS : Data was collected from the Twitter handles of five pharmaceutical companies, ten U.S. and Canadian public health agencies, and World Health Organization (WHO) between January 01, 2017 - December 31, 2021. A total of 181,469 tweets were divided into two phases for the analysis: before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using Natural Language Processing (NLP) based topic modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents.

RESULTS : We utilized the topics modeled from the tweets authored by the health organizations chosen for our analysis using Non-Negative Matrix Factorization (NMF) ('c_umass' scores: -3.6530 and -3.7944, before COVID-19 and during COVID-19 respectively). The topics are - 'Chronic Diseases', 'Health Research', 'Community Healthcare', 'Medical Trials', 'COVID-19', 'Vaccination', 'Nutrition and Well-being', and 'Mental Health'. In terms of user impact, WHO (user impact: 4171.24) had the highest impact overall, followed by the public health agencies, CDC (user impact: 2895.87), and NIH (user impact: 891.06). Among pharmaceutical companies, Pfizer's user impact was the highest at 97.79. Furthermore, for sentiment forecasting, ARIMA and SARIMAX models performed best on the majority of the subsets of data (divided as per the health organization and time-period), with Mean Absolute Error (MAE) between 0.027 - 0.084, Mean Squared Error (MSE) between 0.001 - 0.011, and Root Mean Squared Error (RMSE) between 0.031 - 0.105.

CONCLUSIONS : Our findings indicate that people engage more on topics like 'COVID-19' than 'Medical Trials', 'Customer Experience'. Also, there are notable differences in the user engagement levels across organizations. Global organizations, like WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement.

CLINICALTRIAL :

Singhal Aditya, Baxi Manmeet Kaur, Mago Vijay

2022-Jul-15

Pathology Pathology

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

In Journal of proteome research

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

Vanderboom Patrick M, Renuse Santosh, Maus Anthony D, Madugundu Anil K, Kemp Jennifer V, Gurtner Kari M, Singh Ravinder J, Grebe Stefan K, Pandey Akhilesh, Dasari Surendra

2022-Jul-18

COVID-19, antigen detection, limit of detection (LOD), machine learning (ML), parallel reaction monitoring (PRM), sensitivity

General General

Predicting Multiple Sclerosis Outcomes during the COVID-19 Stay-at-Home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping.

In JMIR mental health

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has broad negative impact on physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS).

OBJECTIVE : We present a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated "stay-at-home" period due to a global pandemic.

METHODS : First, we extract features that capture behavioral changes due to the "stay-at-home" order. Then, we adapt and apply an existing algorithm to these behavioral change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the "stay-at-home" period.

RESULTS : Using data collected between November 2019 and May 2020, algorithm detects depression with an accuracy of 82.5% (65% improvement over baseline; f1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; f1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; f1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; f1-score: 0.84).

CONCLUSIONS : Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics that would cause drastic behavioral changes.

CLINICALTRIAL : Not Applicable.

Chikersal Prerna, Venkatesh Shruthi, Masown Karmen, Walker Elizabeth, Quraishi Danyal, Dey Anind, Goel Mayank, Xia Zongqi

2022-Jul-16

Surgery Surgery

What, Where, When and How of COVID-19 Patents Landscape: A Bibliometrics Review.

In Frontiers in medicine

Two years after COVID-19 came into being, many technologies have been developed to bring highly promising bedside methods to help fight this epidemic disease. However, owing to viral mutation, how far the promise can be realized remains unclear. Patents might act as an additional source of information for informing research and policy and anticipating important future technology developments. A comprehensive study of 3741 COVID-19-related patents (3,543 patent families) worldwide was conducted using the Derwent Innovation database. Descriptive statistics and social network analysis were used in the patent landscape. The number of COVID-19 applications, especially those related to treatment and prevention, continued to rise, accompanied by increases in governmental and academic patent assignees. Although China dominated COVID-19 technologies, this position is worth discussing, especially in terms of the outstanding role of India and the US in the assignee collaboration network as well as the outstanding invention portfolio in Italy. Intellectual property barriers and racist treatment were reduced, as reflected by individual partnerships, transparent commercial licensing and diversified portfolios. Critical technological issues are personalized immunity, traditional Chinese medicine, epidemic prediction, artificial intelligence tools, and nucleic acid detection. Notable challenges include balancing commercial competition and humanitarian interests. The results provide a significant reference for decision-making by researchers, clinicians, policymakers, and investors with an interest in COVID-19 control.

Liu Kunmeng, Zhang Xiaoming, Hu Yuanjia, Chen Weijie, Kong Xiangjun, Yao Peifen, Cong Jinyu, Zuo Huali, Wang Jian, Li Xiang, Wei Benzheng

2022

COVID-19, bibliometric patent analysis, citation network, coronavirus, patent landscape, patent mining, social network analysis

oncology Oncology

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

In Methods in molecular biology (Clifton, N.J.)

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

Sahebkar Amirhossein, Abbasifard Mitra, Chaibakhsh Samira, Guest Paul C, Pourhoseingholi Mohamad Amin, Vahedian-Azimi Amir, Kesharwani Prashant, Jamialahmadi Tannaz

2022

COVID-19, Chest CT, Computed tomography, Deep learning, Diffuse opacities, Lesion distribution, SARS-CoV-2

General General

Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles.

In Methods in molecular biology (Clifton, N.J.)

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

Lazari Lucas C, Rosa-Fernandes Livia, Palmisano Giuseppe

2022

Biomarkers, COVID-19, Classification, MALDI-TOF, Machine learning, Prognostic

General General

Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective.

In Methods in molecular biology (Clifton, N.J.)

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

Guest Paul C, Popovic David, Steiner Johann

2022

Bias, Biomarker discovery, COVID-19, Confounding factor, Machine learning, Multiplex assay, SARS-CoV-2

General General

Users' Feedback on COVID-19 Lockdown Documentary: An Emotion Analysis and Topic Modeling Analysis.

In Frontiers in psychology ; h5-index 92.0

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose "The lockdown: One month in Wuhan" YouTube documentary, and applied emotion analysis as well as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation (LDA) topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust (22.8%), joy (15.4%), and anticipation (17.6%) are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing.

Shi Xiaochuan, Jia Miaoyutian, Li Jia, Chen Quiyi, Liu Guan, Liu Qian

2022

COVID-19, LDA topic modeling, emotion analysis, health measures, lockdown

General General

A Smart Device for a Preliminary Dental Examination Based on the Internet of Things.

In Computational intelligence and neuroscience

The COVID-19 pandemic has threatened the lives of many people, especially the elderly and those with chronic illnesses, as well as threatening the global economy. In response to the pandemic, many medical centers, including dental facilities, have significantly reduced the treatment of patients by limiting clinical practice to exclusively urgent, nondeferred care. Dentists are more vulnerable to contracting COVID-19, due to the necessity of the dentist being close to the patient. One of the precautions that dentists take to avoid transmitting infections is to wear a mask and gloves. However, the basic condition for nontransmission of infection is to leave a safe distance between the patient and the dentist. This system can be implemented by using an Arduino microcontroller, which is designed as a preliminary device by a dentist to examine a patient's teeth so that a safe distance of three meters between the dentist and the patient can be maintained. The project is based on hardware and has been programmed through Arduino. The proposed system uses a small wired camera with a length of five meters that is connected to the dentist's mobile or laptop and is installed on a robotic arm. The dentist can control the movement of the arm in all directions using a joystick at a distance of three meters. The results showed the effectiveness of this system for leaving a safe distance between the patient and the dentist. In our future work, we will control the movement of the arm via Bluetooth, and we will use a wi-fi-based camera.

Wedyan Mohammad, Alturki Ryan, Gazzawe Foziah, Ramadan Enas

2022

General General

The Role of Emerging Technologies to Fight Against COVID-19 Pandemic: An Exploratory Review.

In Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology

Since the end of the year 2019, the whole world is experiencing a global emergency due to the COVID-19 pandemic. The major sectors including industry, economics, education have been affected. Ongoing pandemics confined us to avoid mass gathering and rigorously maintain social distancing to mitigate the spreading of this infectious disease. In this situation emerging technologies including the internet of things (IoT), Artificial Intelligence (AI) is playing a very important role in various fields such as healthcare, economics, educational system, and others to monitoring or tackle the impact of COVID-19 pandemic. Several papers discussed the impact of IoT on the COVID-19 pandemic in various aspects. However, the challenges and designing issues towards the implementation of IoT-based monitoring systems are not deeply investigated. Alongside, the adaptation of IoT and other technologies in the post-covid situation is not addressed properly. Our review article provides an up to date extensive survey on how IoT-enabled technologies are helping to combat the pandemic and to manage industry, education, economic, and medical system. As result, the realization is that IoT and other associated technologies have a great impact on virus detection, tracking, and mitigate the spread. In the face of an expeditiously spreading pandemic, the associated designing issues of the IoT-based framework have been looked into as a part of this review. Alongside, this review highlights the major challenges like privacy, security scalability, etc. facing in using such technologies. Finally, we explore 'The New Normal' and the use of technologies to help in the post-pandemic era.

Mondal Sanjoy, Mitra Priyanjana

2022

AI, COVID-19, Drone, IoMT, IoT, Pandemic

General General

Topological Analysis on Multi-scenario Graphs: Applications Toward Discerning Variability in SARS-CoV-2 and Topic Similarity in Research.

In Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology

A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysi