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Internal Medicine Internal Medicine

Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.

In Frontiers in medicine

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76-89], diagnostic odds ratio (DOR) of 496 [198-1,245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94-98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96-0.98] and 0.92 [0.91-0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

Marateb Hamid Reza, Ziaie Nezhad Farzad, Mohebian Mohammad Reza, Sami Ramin, Haghjooy Javanmard Shaghayegh, Dehghan Niri Fatemeh, Akafzadeh-Savari Mahsa, Mansourian Marjan, Mañanas Miquel Angel, Wolkewitz Martin, Binder Harald

2021

COVID-19, computer-aided diagnosis, machine learning, screening, validation studies

General General

Classification of COVID-19 by using supervised optimized machine learning technique.

In Materials today. Proceedings

In recent two years, covid-19 diseases is the most harmful diseases in entire world. This disease increase the high mortality rate in several developed countries. Earlier identification of covid-19 symptoms can avoid the over illness or death. However, there are several researchers are introduced different methodology to identification of diseases symptoms. But, identification and classification of covid-19 diseases is the difficult task for every researchers and doctors. In this modern world, machine learning techniques is useful for several medical applications. This study is more focused in applying machine learning classifier model as SVM for classification of diseases. By improve the classification accuracy of the classifier by using hyper parameter optimization technique as modified cuckoo search algorithm. High dimensional data have unrelated, misleading features, which maximize the search space size subsequent in struggle to process data further thus not contributing to the learning practise, So we used a hybrid feature selection technique as mRMR (Minimum Redundancy Maximum Relevance) algorithm. The experiment is conducted by using UCI machine learning repository dataset. The classifier is conducted to classify the two set of classes such as COVID-19, and normal cases. The proposed model performance is analysed by using different parametric metrics, which are explained in result section.

Sharma Dilip Kumar, Subramanian Muthukumar, Malyadri Pacha, Reddy Bojja Suryanarayana, Sharma Mukta, Tahreem Madiha

2021-Nov-29

Classification, Covid-19, Feature selection, Machine learning, Modified cuckoo search algorithm and optimization

Public Health Public Health

Sentiments Evoked by WHO Public Health Posts During the COVID-19 Pandemic: A Neural Network-Based Machine Learning Analysis.

In Cureus

Introduction The World Health Organization (WHO) is a specialized agency of the United Nations responsible for international public health. Established on April 7, 1948, it has since played a pivotal role in several public health achievements and has had considerable success. But never since the establishment of the WHO has it faced a pandemic of such a huge scale. The spread of the coronavirus and the inability of the WHO to contain it has raised many questions about its efficiency and role. The present study explores the range of emotions and sentiments evoked by public health information posts of WHO over the course of the pandemic. Methods This study uses Bidirectional Encoder Representations from Transformers (BERT), which is a neural network-based technique for natural language processing. Three timeframes of five months each, starting from March 2020, were defined. A total of six posts, two posts from each timeframe, were then analysed. Comments were classified as positive, neutral and negative. The broader positive and negative classes were further subclassified into two classes each. Natural language processing was further applied to obtain results. Results The general trend of the sentiments over the period of pandemic showed a significant and dominant proportion of negative comments that overshadowed the neutral, positive and irrelevant comments over all timeframes. Specifically, the negative sentiments peaked during the second timeframe. The negativity was directed more towards the WHO, governments and people not complying with coronavirus disease 2019-appropriate norms. Positive comments were mostly expressed towards health workers. Conclusion An unusually high proportion of negative sentiment was observed in response to relatively innocuous public health posts. This may be a result of heightened anxiety, questionable credibility of the sources of information and geopolitical power play maligning the image of the WHO.

Pathak Tanmay S, Athavale Harsh, Pathak Amey S, Athavale Sunita

2021-Oct

bert, covid 19, neural networks, sentiment analysis, social media analytics, who- world health organization

Radiology Radiology

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

In Digital health

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

Cushnan Dominic, Berka Rosalind, Bertolli Ottavia, Williams Peter, Schofield Daniel, Joshi Indra, Favaro Alberto, Halling-Brown Mark, Imreh Gergely, Jefferson Emily, Sebire Neil J, Reilly Gerry, Rodrigues Jonathan C L, Robinson Graham, Copley Susan, Malik Rizwan, Bloomfield Claire, Gleeson Fergus, Crotty Moira, Denton Erika, Dickson Jeanette, Leeming Gary, Hardwick Hayley E, Baillie Kenneth, Openshaw Peter Jm, Semple Malcolm G, Rubin Caroline, Howlett Andy, Rockall Andrea G, Bhayat Ayub, Fascia Daniel, Sudlow Cathie, Jacob Joseph

Imaging, artificial intelligence, coronavirus SARS-CoV-2 disease, general, machine learning, medicine, radiology, respiratory

Public Health Public Health

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.

In Frontiers in psychiatry

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.

Badal Varsha D, Nebeker Camille, Shinkawa Kaoru, Yamada Yasunori, Rentscher Kelly E, Kim Ho-Cheol, Lee Ellen E

2021

NLP, Social support, artificial intelligence, gender, linguistic features, loneliness, social connectedness

General General

Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic.

In AI & society

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: (1) The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; (2) the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period.

de Sá Angela A R, Carvalho Jairo D, Naves Eduardo L M

2021-Nov-27

Artificial intelligence, COVID-19, Espistemology, Intelligent systems

General General

Contactless Body Temperature Monitoring of In-Patient Department (IPD) Using 2.4 GHz Microwave Frequency via the Internet of Things (IoT) Network.

In Wireless personal communications

Since the COVID-19 situation keeps going on started from 2019. Many solutions are to against the spreading of coronavirus disease. The nurses have died, and other medical workers are in critical condition from operating in the hospital. It is a deadly virus that kills many humans; Thailand's solutions have urged the public to be confident about the Government's handling of the 2019-novel Coronavirus. At the same time, everyone has to embrace the new normal lifestyle and social distancing while patiently waiting for scientists and doctors to discover vaccines and treatments to defeat COVID-19. This work proposes an innovation of wireless body temperature that instead of the used manual by medical workers in the hospital of "the contactless body temperature monitoring (CBTM) of the in-patient department (IPD)." The proposed CBTM implementation applied artificial intelligence and Internet of Things (IoT) technologies. The specified infrared body temperature on the MLX90614 DCI used for the medical field was selected to embed the IoT-CBTM for IPD using the IoT platform. The MLX90614 is an accurate sensor that matches to use for medical promotion. The detected information data from IPD will be sent to the host computer and stored in the cloud internet service at a microwave band frequency of 2.5/5.0 GHz. This paper presents the accuracy test of the IoT-CBTM prototype calibrated with the manual body temperature verifying device under Thai Industrial Institute to close with the accuracy standard requirement. The experiments were repeated many times until raise up over 70% to get more reliability accuracy. The findings indicated that the proposed prototype achieved a reliability calibration of 74.7%. The actual use of IoT-CBTM is convenient to the nurse, doctor, and medical workers to collect body temperature data into the host computer, and they can monitor this information at all times in the working room, which is far away from the COVID-19 patients. Therefore, this novel innovation was achieved because it took to try out at a local health-promoting hospital in Songkhla Province, Thailand, which the IoT-CBTM system was satisfied by the medical staff because it can safe their time, and genuinely reaching the new norm on medical distancing real-time monitoring.

Boonsong Wasana, Senajit Narongrit, Prasongchan Piya

2021-Nov-29

CBTM, COVID-19, IPD, TISI

General General

Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study.

In Computers in human behavior ; h5-index 125.0

This study is informed by two research gaps. One, Artificial Intelligence's (AI's) Machine Learning (ML) techniques have the potential to help separate information and misinformation, but this capability has yet to be empirically verified in the context of COVID-19. Two, while older adults can be particularly susceptible to the virus as well as its online infodemic, their information processing behaviour amid the pandemic has not been understood. Therefore, this study explores and understands how ML techniques (Study 1), and humans, particularly older adults (Study 2), process the online infodemic regarding COVID-19 prevention and cure. Study 1 employed ML techniques to classify information and misinformation. They achieved a classification accuracy of 86.7% with the Decision Tree classifier, and 86.67% with the Convolutional Neural Network model. Study 2 then investigated older adults' information processing behaviour during the COVID-19 infodemic period using some of the posts from Study 1. Twenty older adults were interviewed. They were found to be more willing to trust traditional media rather than new media. They were often left confused about the veracity of online content related to COVID-19 prevention and cure. Overall, the paper breaks new ground by highlighting how humans' information processing differs from how algorithms operate. It offers fresh insights into how during a pandemic, older adults-a vulnerable demographic segment-interact with online information and misinformation. On the methodological front, the paper represents an intersection of two very disparate paradigms-ML techniques and interview data analyzed using thematic analysis and concepts drawn from grounded theory to enrich the scholarly understanding of human interaction with cutting-edge technologies.

Choudrie Jyoti, Banerjee Snehasish, Kotecha Ketan, Walambe Rahee, Karende Hema, Ameta Juhi

2021-Jun

AI, COVID-19 pandemic, Information-misinformation, Interview, Machine learning techniques, Older adult

General General

Impacts of COVID-19 local spread and Google search trend on the US stock market.

In Physica A

We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. We use both conventional econometric and Machine Learning (ML) models that incorporate the local spread dynamics, COVID-19 cases and death, and Google search activities to assess if incorporating information about local spreads improves the predictive accuracy of models for the US stock market. The results suggest that COVID-19 cases and deaths, its local spread, and Google searches have impacts on abnormal stock prices between January 2020 to May 2020. Furthermore, incorporating information about local spread significantly improves the performance of forecasting models of the abnormal stock prices at longer forecasting horizons.

Dey Asim K, Hoque G M Toufiqul, Das Kumer P, Panovska Irina

2021-Sep-30

Abnormal price, Causality, Covid-19, Stock market, Temporal network, Volatility

Public Health Public Health

Digital Interventions to Reduce Distress Among Health Care Providers at the Frontline: Protocol for a Feasibility Trial.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Stress, anxiety, distress, and depression are high among healthcare workers during the COVID-19 pandemic, and they have reported acting in ways that are contrary to their moral values and professional commitments that degrade their integrity. This creates moral distress and injury due to constraints they have encountered, such as limited resources.

OBJECTIVE : The purpose of this study is to develop and show the feasibility of digital platforms (a virtual reality (VR) and a mobile platform) to understand the causes and ultimately reduce the moral distress of healthcare providers during the COVID-19 pandemic. This project is a proof-of-concept integration of concepts/applications to demonstrate viability over six months and guide future studies to develop these state-of-the-art technologies to help frontline healthcare workers work in complex moral contexts. In addition, the project will develop innovations that can be used for future pandemics and in other contexts prone to producing moral distress and injury.

METHODS : This will be a prospective, single cohort, pre- and post-test study examining the effect of a brief informative video describing moral distress on perceptual, psychological, and physiological indicators of stress and decision-making during the scenario known to potentially elicit moral distress. To accomplish this, we have developed a VR simulation scenario that will be used before and after the digital intervention for monitoring short-term impacts. The simulation involves an ICU setting during the COVID-19 pandemic, and participants will be placed in a morally challenging situation. The participants will be engaged in an educational intervention at the individual, team, and organizational levels. During each test, data will be collected for a) physiological measures of stress and after each test, data will be collected regarding b) thoughts, feelings and behaviours during a morally challenging situation, and c) perceptual estimates of psychological stress. We aim to create an effective compound intervention composed of the VR-based simulation educational intervention that is examined through the data collected from mental health questionnaires. In addition, participants will continue to be monitored for moral distress and other psychological stresses for eight weeks through our Digital intervention/intelligence Group mobile (DiiG) platform for a longer-term impact. Finally, a comparison will be conducted using machine learning and biostatistical techniques to analyze the short- and long-term impacts of the VR intervention.

RESULTS : Funded in (November, 2020), approved by REB in (March, 2021), the study is ongoing.

CONCLUSIONS : This project aims to demonstrate the feasibility of using digital platforms to understand the continuum of moral distress that can lead to moral injury. Demonstration of feasibility will lead to future studies to examine the efficacy of digital platforms to reduce moral distress.

CLINICALTRIAL : Trial registry name: ClinicalTrials.gov, registration/identifier number: NCT05001542, URL: https://clinicaltrials.gov/ct2/show/NCT05001542, Unity Health Toronto protocol record: 21-066.

Nguyen Binh, Torres Andrei, Sim Walter, Kenny Deborah, Campbell Douglas M, Beavers Lindsay, Lou Wendy, Kapralos Bill, Peter Elizabeth, Dubrowski Adam, Krishnan Sridhar, Bhat Venkat

2021-Nov-22

Public Health Public Health

Fire association with respiratory disease and COVID-19 complications in the State of Pará, Brazil.

In Lancet Regional Health. Americas

Background : Brazil has faced two simultaneous problems related to respiratory health: forest fires and the high mortality rate due to COVID-19 pandemics. The Amazon rain forest is one of the Brazilian biomes that suffers the most with fires caused by droughts and illegal deforestation. These fires can bring respiratory diseases associated with air pollution, and the State of Pará in Brazil is the most affected. COVID-19 pandemics associated with air pollution can potentially increase hospitalizations and deaths related to respiratory diseases. Here, we aimed to evaluate the association of fire occurrences with the COVID-19 mortality rates and general respiratory diseases hospitalizations in the State of Pará, Brazil.

Methods : We employed machine learning technique for clustering k-means accompanied with the elbow method used to identify the ideal quantity of clusters for the k-means algorithm, clustering 10 groups of cities in the State of Pará where we selected the clusters with the highest and lowest fires occurrence from the 2015 to 2019. Next, an Auto-regressive Integrated Moving Average Exogenous (ARIMAX) model was proposed to study the serial correlation of respiratory diseases hospitalizations and their associations with fire occurrences. Regarding the COVID-19 analysis, we computed the mortality risk and its confidence level considering the quarterly incidence rate ratio in clusters with high and low exposure to fires.

Findings : Using the k-means algorithm we identified two clusters with similar DHI (Development Human Index) and GDP (Gross Domestic Product) from a group of ten clusters that divided the State of Pará but with diverse behavior considering the hospitalizations and forest fires in the Amazon biome. From the auto-regressive and moving average model (ARIMAX), it was possible to show that besides the serial correlation, the fires occurrences contribute to the respiratory diseases increase, with an observed lag of six months after the fires for the case with high exposure to fires. A highlight that deserves attention concerns the relationship between fire occurrences and deaths. Historically, the risk of mortality by respiratory diseases is higher (about the double) in regions and periods with high exposure to fires than the ones with low exposure to fires. The same pattern remains in the period of the COVID-19 pandemic, where the risk of mortality for COVID-19 was 80% higher in the region and period with high exposure to fires. Regarding the SARS-COV-2 analysis, the risk of mortality related to COVID-19 is higher in the period with high exposure to fires than in the period with low exposure to fires. Another highlight concerns the relationship between fire occurrences and COVID-19 deaths. The results show that regions with high fire occurrences are associated with more cases of COVID deaths.

Interpretation : The decision-make process is a critical problem mainly when it involves environmental and health control policies. Environmental policies are often more cost-effective as health measures than the use of public health services. This highlight the importance of data analyses to support the decision making and to identify population in need of better infrastructure due to historical environmental factors and the knowledge of associated health risk. The results suggest that The fires occurrences contribute to the increase of the respiratory diseases hospitalization. The mortality rate related to COVID-19 was higher for the period with high exposure to fires than the period with low exposure to fires. The regions with high fire occurrences is associated with more COVID-19 deaths, mainly in the months with high number of fires.

Funding : No additional funding source was required for this study.

Schroeder Lucas, de Souza Eniuce Menezes, Rosset Clévia, Marques Junior Ademir, Boquett Juliano André, Francisco Rofatto Vinicius, Brum Diego, Gonzaga Luiz, Zagonel de Oliveira Marcelo, Veronez Mauricio Roberto

2022-Feb

ARIMAX, COVID-19, Fire, Hospitalizations, Incidence rate ratio, K-Means, Mortality, Respiratory diseases, Risk, SARS-Cov-2, Time series analysis

General General

I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece.

In Public health in practice (Oxford, England)

Objectives : In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health.

Study design : An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries.

Methods : Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion.

Results : Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown.

Conclusions : From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation.

Carollo Alessandro, Bizzego Andrea, Gabrieli Giulio, Wong Keri Ka-Yee, Raine Adrian, Esposito Gianluca

2021-Nov

COVID-19, Global study, Lockdown, Loneliness, Machine learning, Mental health

General General

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.

In Current research in pharmacology and drug discovery

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Prasad Kartikay, Kumar Vijay

2021

Artificial intelligence, COVID-19, Deep learning, Drug repurposing, SARS-CoV-2, Structure

Pathology Pathology

A Deep Learning Approach for Predicting Severity of COVID-19 Patients Using A Parsimonious Set of Laboratory Markers.

In iScience

The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy to deploy system to predict the severe manifestation of disease in COVID-19 patients with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0·78 95% CI: 0·77-0·82, and the negative predictive value NPV of 0·86 95% CI: 0·84-0·88 for the need to use a ventilator and has an accuracy with AUC of 0·85 95% CI: 0·84-0·86, and the NPV of 0·94 95% CI: 0·92-0·96 for predicting in-hospital 30-day mortality.

Singh Vivek, Kamaleswaran Rishi, Chalfin Donald, Buño-Soto Antonio, San Roman Janika, Rojas-Kenney Edith, Molinaro Ross, von Sengbusch Sabine, Hodjat Parsa, Comaniciu Dorin, Kamen Ali

2021-Nov-27

General General

Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm.

In Infectious Disease Modelling

This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.

Krivorotko Olga, Sosnovskaia Mariia, Vashchenko Ivan, Kerr Cliff, Lesnic Daniel

2022-Mar

Agent-based modeling, COVID-19, Coronavirus data analysis, Epidemiology, Forecasting scenarios, Interventions analysis, Optimization, Parameter identification, Reproduction number

General General

HLAncPred: A method for predicting promiscuous non-classical HLA binding sites

bioRxiv Preprint

In the last two decades, ample of methods have been developed to predict the classical HLA binders in an antigen. In contrast, limited attempts have been made to develop methods for predicting binders for non-classical HLA; due to the scarcity of sufficient experimental data and lack of community interest. Of Note, non-classical HLA plays a crucial immunomodulatory role and regulates various immune responses. Recent studies revealed that non-classical HLA (HLA-E & HLA-G) based immunotherapies have many advantages over classical HLA based-immunotherapy, particularly against COVID-19. In order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of non-classical HLA alleles (HLA-G and HLA-E). All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning based-models achieved more than 0.98 AUC for HLA-G alleles on validation or independent dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.88 on the validation dataset for HLA-E*01:01, HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA binders and compared with the models developed in this study. Moreover, we have also predicted the non-classical HLA binders in the spike protein of different variants of virus causing COVID-19 including omicron (B.1.1.529) to facilitate the community. One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. In order to predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred), and a standalone package.

Dhall, A.; Patiyal, S.; Raghava, G. P. S.

2021-12-06

General General

An investigation into the deep learning approach in sentimental analysis using graph-based theories.

In PloS one ; h5-index 176.0

Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a "black-box" and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features' extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on the input unit and backward path respectively. We experiment on the Twitter Health News dataset were the model has been extended to approach different approximations (tweet/aspect and tweets' source levels, frequency, polarity/subjectivity), it was also transparent and traceable. Moreover, results of comparing with four recent models on same data corpus for tweets analysis showed a rapid convergence with an overall accuracy of ≈83% and 94% of correctly identified true positive sentiments. Therefore, weights can be ideally assigned to specific active features by following the proposed method. As opposite to other compared works, the inferred features are conditioned through the users' preferences (i.e., frequency degree) and via the activation's derivatives (i.e., reject feature if not scored). Future direction will address the inductive aspect of graph embeddings to include dynamic graph structures and expand the model resiliency by considering other datasets like SemEval task7, covid-19 tweets, etc.

Kentour Mohamed, Lu Joan

2021

General General

Detection of COVID-19 with CT Images using Hybrid Complex Shearlet Scattering Networks.

In IEEE journal of biomedical and health informatics

With the ongoing worldwide coronavirus disease 2019 (COVID-19) pandemic, it is desirable to develop effective algorithms for the automatic detection of COVID-19 with chest computed tomography (CT) images. As deep learning has achieved breakthrough results in numerous computer vision and image understanding tasks, a good choice is to consider diagnosis models based on deep learning. Recently, a considerable number of methods have indeed been proposed. However, training an accurate deep learning model requires a large-scale chest CT dataset, which is hard to collect due to the high contagiousness of COVID-19. To achieve improved COVID-19 detection performance, this paper proposes a hybrid framework that fuses the complex shearlet scattering transform (CSST) and a suitable convolutional neural network into a single model. The introduced CSST cascades complex shearlet transforms with modulus nonlinearities and low-pass filter convolutions to compute a sparse and locally invariant image representation. The features computed from the input chest CT images are discriminative for the detection of COVID-19. Furthermore, a wide residual network with a redesigned residual block (WR2N) is developed to learn more granular multiscale representations by applying it to scattering features. The combination of the model-based CSST and data-driven WR2N leads to a more convenient neural network for image representation, where the idea is to learn only the image parts that the CSST cannot handle instead of all parts. The experimental results obtained on two public chest CT datasets for COVID-19 detection demonstrate the superiority of the proposed method. We can obtain more accurate results than several state-of-the-art COVID-19 classification methods in terms of measures such as accuracy, the F1-score, and the area under the receiver operating characteristic curve.

Ren Qingyun, Zhou Bingyin, Tian Liang, Guo Wei

2021-Dec-02

General General

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection.

In Journal of healthcare engineering

Methods : Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance.

Results : SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained.

Conclusion : The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

Tchagna Kouanou Aurelle, Mih Attia Thomas, Feudjio Cyrille, Djeumo Anges Fleurio, Ngo Mouelas Adèle, Nzogang Mendel Patrice, Tchito Tchapga Christian, Tchiotsop Daniel

2021

General General

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.

In Scientific reports ; h5-index 158.0

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

Gidde Prashant Sadashiv, Prasad Shyam Sunder, Singh Ajay Pratap, Bhatheja Nitin, Prakash Satyartha, Singh Prateek, Saboo Aakash, Takhar Rohit, Gupta Salil, Saurav Sumeet, M V Raghunandanan, Singh Amritpal, Sardana Viren, Mahajan Harsh, Kalyanpur Arjun, Mandal Atanendu Shekhar, Mahajan Vidur, Agrawal Anurag, Agrawal Anjali, Venugopal Vasantha Kumar, Singh Sanjay, Dash Debasis

2021-Dec-01

General General

Use of Medical Information and Digital Services for Self-Empowerment before, during, and after a Major Disaster.

In The Tohoku journal of experimental medicine

Disaster response procedures have been developed and improved following the Great East Japan Earthquake. Innovative services have also been created through digital transformation, including an acceleration and deepening of artificial intelligence technology. Things that were once technically impossible are now possible. These innovative technologies will spread across various fields, and disaster response will not be an exception. The Ministry of Health, Labour and Welfare is promoting the use of personal health records in a way that effectively supports the management of treatments by using data from wearable devices and specific applications. During the COVID-19 pandemic, the trade-off between protecting personal information and enabling social benefits, such as in the use of digital tracking, and infodemics, including misinformation, have become new social challenges. Reviewing past disaster preparedness and the services and value provided by digital transformation indicates what new disaster preparedness should be. Digital transformation does not require literacy (ability to collect, analyze, and use information) but competence (beneficial behavioral traits derived from experience). Understanding behavior through data and enabling rational behavior are crucial. By increasing human productivity, we can save time and improve self- and mutual-help in times of disaster. Medical information and digital services must be properly used in normal times. A society that uses such services will be more disaster resilient.

Fujii Susumu, Nonaka Sayuri, Nakayama Masaharu

2021-11

Great East Japan Earthquake, digital transformation, disaster medicine, medical informatics, personal health record

General General

Delta variant with P681R critical mutation revealed by ultra-large atomic-scale ab initio simulation: Implications for the fundamentals of biomolecular interactions

bioRxiv Preprint

SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing the virus to invade human cells much faster and with increased efficiency. Particularly, an especially dangerous mutation P681R close to the furin cleavage site has been identified as responsible for increasing the infection rate. Together with the earlier reported mutation D614G in the same domain, it offers an excellent instance to investigate the nature of mutations and how they affect the interatomic interactions in the spike protein. Here, using ultra large-scale ab initio computational modeling, we study the P681R and D614G mutations in the SD2-FP domain including the effect of double mutation and compare the results with the wild type. We have recently developed a method of calculating the amino acid-amino acid bond pairs (AABP) to quantitatively characterize the details of the interatomic interactions, enabling us to explain the nature of mutation at the atomic resolution. Our most significant find is that the mutations reduce the AABP value, implying a reduced bonding cohesion between interacting residues and increasing the flexibility of these amino acids to cause the damage. The possibility of using this unique mutation quantifiers in a machine learning protocol could lead to the prediction of emerging mutations.

Adhikari, P.; Jawad, B.; Rao, P.; Podgornik, R.; Ching, W.-Y.

2021-12-03

General General

COVID-19. Implications for Paediatric Anaesthesia, lessons learnt and how to prepare for the next pandemic.

In Paediatric anaesthesia

COVID-19 is mainly considered an "adult pandemic", but it also has strong implications for children and consequently for paediatric anaesthesia. Despite the lethality of SARS-CoV-2 infection being directly correlated with age, children have equally experienced the negative impacts of this pandemic. In fact, the spectrum of COVID-19 symptoms among children ranges from very mild to those resembling adults, but may also present as a multisystemic inflammatory syndrome. Moreover, the vast majority of children might be affected by asymptomatic or pauci-symptomatic infection making them the "perfect" carriers for spreading the disease in the community. Beyond the clinical manifestations of SARS-CoV-2 infection, the COVID-19 pandemic may ultimately have catastrophic health and socioeconomic consequences for children and adolescents which are yet to be defined. The aim of this narrative review is to highlight how COVID-19 pandemic has affected and changed the paediatric anaesthesia practice and which lessons are to be learned in case of a future "pandemic". In particular, the rapid evolution and dissemination of research and clinical findings have forced the scientific community to adapt and alter clinical practice on an unseen and pragmatic manner. Equally, implementation of new platforms, techniques and devices together with artificial intelligence and large-scale collaborative efforts may present a giant step for mankind. The valuable lessons of this pandemic will ultimately translate into new treatments modalities for various diseases but will also have the potential for safety improvement and better quality of care. However, this pandemic has revealed the vulnerability and deficiencies of our healthcare system. If not addressed properly, we may end up with a tsunami of burn-out and compassionate fatigue among health care professionals. Paediatric anaesthesia and critical care staff are no exceptions.

Afshari Arash, Disma Nicola, von Ungern-Sternberg Britta S, Matava Clyde

2021-Nov-30

children, coronavirus, covid-19, paediatric anaesthesia, simulation

General General

An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.

In GigaScience

BACKGROUND : The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19-affected UK population in terms of geographic, demographic, and temporal coverage.

FINDINGS : The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage.

CONCLUSION : The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Cushnan Dominic, Bennett Oscar, Berka Rosalind, Bertolli Ottavia, Chopra Ashwin, Dorgham Samie, Favaro Alberto, Ganepola Tara, Halling-Brown Mark, Imreh Gergely, Jacob Joseph, Jefferson Emily, Lemarchand François, Schofield Daniel, Wyatt Jeremy C

2021-Nov-25

COVID-19, SARS-CoV2, machine learning, medical imaging, thoracic imaging

Public Health Public Health

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens.

In Network modeling and analysis in health informatics and bioinformatics

COVID-19 is a pandemic disease caused by novel corona virus, SARS-CoV-2, initially originated from China. In response to this serious life-threatening disease, designing and developing more accurate and sensitive tests are crucial. The aim of this study is designing a multi-epitope of spike and nucleocapsid antigens of COVID-19 virus by bioinformatics methods. The sequences of nucleotides obtained from the NCBI Nucleotide Database. Transmembrane structures of proteins were predicted by TMHMM Server and the prediction of signal peptide of proteins was performed by Signal P Server. B-cell epitopes' prediction was performed by the online prediction server of IEDB server. Beta turn structure of linear epitopes was also performed using the IEDB server. Conformational epitope prediction was performed using the CBTOPE and eventually, eight antigenic epitopes with high physicochemical properties were selected, and then, all eight epitopes were blasted using the NCBI website. The analyses revealed that α-helices, extended strands, β-turns, and random coils were 28.59%, 23.25%, 3.38%, and 44.78% for S protein, 21.24%, 16.71%, 6.92%, and 55.13% for N Protein, respectively. The S and N protein three-dimensional structure was predicted using the prediction I-TASSER server. In the current study, bioinformatics tools were used to design a multi-epitope peptide based on the type of antigen and its physiochemical properties and SVM method (Machine Learning) to design multi-epitopes that have a high avidity against SARS-CoV-2 antibodies to detect infections by COVID-19.

Javadi Mamaghani Amirreza, Arab-Mazar Zahra, Heidarzadeh Siamak, Ranjbar Mohammad Mehdi, Molazadeh Shima, Rashidi Sama, Niazpour Farzad, Naghi Vishteh Mohadeseh, Bashiri Homayoon, Bozorgomid Arezoo, Behniafar Hamed, Ashrafi Mohammad

2021

Multi-epitopes, Nucleocapsid phosphoprotein, SARS-CoV2, Serological tests, Spike glycoprotein

General General

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

In Journal of ambient intelligence and humanized computing

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

Priya Bhanu, Malhotra Jyoteesh

2021-Nov-26

Artificial Intelligence, Double deep reinforcement learning, Edge computing, QoE, Resource utilisation factor, Software-defined wireless networking

General General

The Feasibility of Conducting Safe Objective Structured Clinical Exams (OSCEs) During the COVID-19 Era.

In Advances in medical education and practice

Purpose : Objective structured clinical examination (OSCE) is an effective tool for learners' assessment that require hands-on performance. During the COVID-era, many schools decided to minimize all forms of in-person communication between faculty members and students to mitigate the risk of COVID-19 transmission. We aimed to describe our experience in conducting physical OSCEs during the COVID-19 era. We also reported students' satisfaction during this time.

Materials and Methods : Descriptive cohort study by comparing the 2019-2020 cohort to the 2020-2021 cohort. Descriptive framework for the feasibility of conducting physical OSCEs in the college of pharmacy at King Saudi Bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia.

Results : There were no reported cases of COVID-19 transmission among students and faculty members during the OSCE assessments. Overall, the 2020-2021 cohort reported increased satisfaction compared to their peers in the 2019-2020 cohort; p < 0.05. We observed an increased need for coordination to ensure students' and staff safety while adopting machine learning applications as a public measure when possible.

Conclusion : Owing to the implementation of clear and strong measures, it was feasible to conduct OSCEs, and there were no reported cases of COVID-19 transmission. Other universities may adopt a similar approach so as to provide an optimal educational experience while ensuring the safety of their staff and faculty.

Alshaya Abdulrahman, Alowais Shuroug, Alharbi Shmeylan, Albekairy Abdulkareem

2021

COVID-19, OSCE, education, simulation, teaching

General General

Deep learning-based exchange rate prediction during the COVID-19 pandemic.

In Annals of operations research

This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.

Abedin Mohammad Zoynul, Moon Mahmudul Hasan, Hassan M Kabir, Hajek Petr

2021-Nov-26

Bagging ridge, Bi-LSTM, COVID-19, Deep learning, Exchange rate forecasting, Machine learning

oncology Oncology

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

In Scientific reports ; h5-index 158.0

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Hsieh Kanglin, Wang Yinyin, Chen Luyao, Zhao Zhongming, Savitz Sean, Jiang Xiaoqian, Tang Jing, Kim Yejin

2021-Nov-30

General General

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.

In Scientific reports ; h5-index 158.0

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.

Jia Lijing, Wei Zijian, Zhang Heng, Wang Jiaming, Jia Ruiqi, Zhou Manhong, Li Xueyan, Zhang Hankun, Chen Xuedong, Yu Zheyuan, Wang Zhaohong, Li Xiucheng, Li Tingting, Liu Xiangge, Liu Pei, Chen Wei, Li Jing, He Kunlun

2021-Nov-30

General General

A machine learning model for nowcasting epidemic incidence.

In Mathematical biosciences

Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID - 19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting.

Sahai Saumya Yashmohini, Gurukar Saket, KhudaBukhsh Wasiur R, Parthasarathy Srinivasan, Rempała Grzegorz A

2021-Nov-27

Backfilling, COVID-19 incidence, Nowcasting, Random forest

General General

T cell receptor repertoire signatures associated with COVID-19 severity

bioRxiv Preprint

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoires composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of viral infection such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we performed a large-scale analysis of over 4.7 billion sequences across 2,130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identified and characterized convergent COVID-19 associated CDR3 gene usages, specificity groups, and sequence patterns. T cell clonal expansion was found to be associated with upregulation of T cell effector function, TCR signaling, NF-kB signaling, and Interferon-gamma signaling pathways. Machine learning approaches accurately predicted disease severity for patients based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores across various predictor permutations. These analyses provided an integrative, systems immunology view of T cell adaptive immune responses to COVID-19.

Park, J.; Lee, K.; Lam, S.; Chen, S.

2021-12-02

Public Health Public Health

Horizon Scanning: Rise of Planetary Health Genomics and Digital Twins for Pandemic Preparedness.

In Omics : a journal of integrative biology

The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.

Geanta Marius, Tanwar Ankit Singh, Lehrach Hans, Satyamoorthy Kapaettu, Brand Angela

2021-Dec-01

SARS-CoV-2 sequencing, digital transformation, digital twins, genomic surveillance, pandemic preparedness, public health genomics

General General

Computational anti-COVID-19 drug design: progress and challenges.

In Briefings in bioinformatics

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Wang Jinxian, Zhang Ying, Nie Wenjuan, Luo Yi, Deng Lei

2021-Nov-30

COVID-19, SARS-CoV-2, artificial intelligence, computational drug design, structure-based

Radiology Radiology

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020.

In European journal of radiology ; h5-index 47.0

PURPOSE : To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020.

METHODS : Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors' affiliation was manually extracted from each article. The Bibliographic Item Co-Occurrence Matrix Builder and VOSviewer were used to identify the research topics of the included articles and illustrate the future trajectories of studies.

RESULTS : The study included 2685 original articles. The number of publications on DL and medical imaging has increased substantially since 2017, accounting for 97.2% of all included articles. We evaluated the rules of the application of 47 DL networks to eight radiological tasks on 11 human organ sites. Neuroradiology, thorax, and abdomen were frequent research subjects, while thyroid was under-represented. Segmentation and classification tasks were the primary purposes. U-Net, ResNet, and VGG were the most frequently used Convolutional neural network-derived networks. GAN-derived networks were widely developed and applied in 2020, and transfer learning was highlighted in the COVID-19 studies. Brain, prostate, and diabetic retinopathy-related studies were mature research topics in the field. Breast- and lung-related studies were in a stage of rapid development.

CONCLUSIONS : This study evaluates the general rules and future trajectories of DL network application in medical image analyses and provides guidance for future studies.

Wang Lu, Wang Hairui, Huang Yingna, Yan Baihui, Chang Zhihui, Liu Zhaoyu, Zhao Mingfang, Cui Lei, Song Jiangdian, Li Fan

2021-Nov-24

Bibliometrics, Deep learning, Diagnostic imaging, Radiology

General General

Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification.

In Computers in biology and medicine

Deep learning (DL) has shown great success in the field of medical image analysis. In the wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on DL have made significant progress in automated screening of COVID-19 disease from the chest X-ray (CXR) images. But these DL models have no inherent way of expressing uncertainty associated with the model's prediction, which is very important in medical image analysis. Therefore, in this paper, we develop an uncertainty-aware convolutional neural network model, named UA-ConvNet, for the automated detection of COVID-19 disease from CXR images, with an estimation of associated uncertainty in the model's predictions. The proposed approach utilizes the EfficientNet-B3 model and Monte Carlo (MC) dropout, where an EfficientNet-B3 model has been fine-tuned on the CXR images. During inference, MC dropout has been applied for M forward passes to obtain the posterior predictive distribution. After that mean and entropy have been calculated on the obtained predictive distribution to get the mean prediction and model uncertainty. The proposed method is evaluated on the three different datasets of chest X-ray images, namely the COVID19CXr, X-ray image, and Kaggle datasets. The proposed UA-ConvNet model achieves a G-mean of 98.02% (with a Confidence Interval (CI) of 97.99-98.07) and sensitivity of 98.15% for the multi-class classification task on the COVID19CXr dataset. For binary classification, the proposed model achieves a G-mean of 99.16% (with a CI of 98.81-99.19) and a sensitivity of 99.30% on the X-ray Image dataset. Our proposed approach shows its superiority over the existing methods for diagnosing the COVID-19 cases from the CXR images.

Gour Mahesh, Jain Sweta

2021-Nov-23

COVID-19 automatic screening uncertainty estimation Monte Carlo dropout pre-trained EfficientNet CNN Chest X-ray images

Public Health Public Health

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world.

In bioRxiv : the preprint server for biology

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.

Li Yawei, Liu Qingyun, Zeng Zexian, Luo Yuan

2021-Nov-24

General General

Severe COVID-19 infection is associated with aberrant cytokine production by infected lung epithelial cells rather than by systemic immune dysfunction.

In Research square

The mechanisms explaining progression to severe COVID-19 remain poorly understood. It has been proposed that immune system dysregulation/over-stimulation may be implicated, but it is not clear how such processes would lead to respiratory failure. We performed comprehensive multiparameter immune monitoring in a tightly controlled cohort of 128 COVID-19 patients, and used the ratio of oxygen saturation to fraction of inspired oxygen (SpO2 / FiO2) as a physiologic measure of disease severity. Machine learning algorithms integrating 139 parameters identified IL-6 and CCL2 as two factors predictive of severe disease, consistent with the therapeutic benefit observed with anti-IL6-R antibody treatment. However, transcripts encoding these cytokines were not detected among circulating immune cells. Rather, in situ analysis of lung specimens using RNAscope and immunofluorescent staining revealed that elevated IL-6 and CCL2 were dominantly produced by infected lung type II pneumocytes. Severe disease was not associated with higher viral load, deficient antibody responses, or dysfunctional T cell responses. These results refine our understanding of severe COVID-19 pathophysiology, indicating that aberrant cytokine production by infected lung epithelial cells is a major driver of immunopathology. We propose that these factors cause local immune regulation towards the benefit of the virus.

Gajewski Thomas, Rouhani Sherin, Trujillo Jonathan, Pyzer Athalia, Yu Jovian, Fessler Jessica, Cabanov Alexandra, Higgs Emily, Cron Kyle, Zha Yuanyuan, Lu Yihao, Bloodworth Jeffrey, Abasiyanik Mustafa, Okrah Susan, Flood Blake, Hatogai Ken, Leung Michael, Pezeshk Apameh, Kozloff Lara, Reschke Robin, Strohbehn Garth, Chervin Carolina Soto, Kumar Madan, Schrantz Stephen, Madariaga Maria Lucia, Beavis Kathleen, Yeo Kiang-Teck, Sweis Randy, Segal Jeremy, Tay SavaÅŸ, Izumchenko Evgeny, Mueller Jeffrey, Chen Lin

2021-Nov-24

General General

Machine Learning-Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study.

In JMIRx med

Background : The onset and development of the COVID-19 pandemic have placed pressure on hospital resources and staff worldwide. The integration of more streamlined predictive modeling in prognosis and triage-related decision-making can partly ease this pressure.

Objective : The objective of this study is to assess the performance impact of dimensionality reduction on COVID-19 mortality prediction models, demonstrating the high impact of a limited number of features to limit the need for complex variable gathering before reaching meaningful risk labelling in clinical settings.

Methods : Standard machine learning classifiers were employed to predict an outcome of either death or recovery using 25 patient-level variables, spanning symptoms, comorbidities, and demographic information, from a geographically diverse sample representing 17 countries. The effects of feature reduction on the data were tested by running classifiers on a high-quality data set of 212 patients with populated entries for all 25 available features. The full data set was compared to two reduced variations with 7 features and 1 feature, respectively, extracted using univariate mutual information and chi-square testing. Classifier performance on each data set was then assessed on the basis of accuracy, sensitivity, specificity, and received operating characteristic-derived area under the curve metrics to quantify benefit or loss from reduction.

Results : The performance of the classifiers on the 212-patient sample resulted in strong mortality detection, with the highest performing model achieving specificity of 90.7% (95% CI 89.1%-92.3%) and sensitivity of 92.0% (95% CI 91.0%-92.9%). Dimensionality reduction provided strong benefits for performance. The baseline accuracy of a random forest classifier increased from 89.2% (95% CI 88.0%-90.4%) to 92.5% (95% CI 91.9%-93.0%) when training on 7 chi-square-extracted features and to 90.8% (95% CI 89.8%-91.7%) when training on 7 mutual information-extracted features. Reduction impact on a separate logistic classifier was mixed; however, when present, losses were marginal compared to the extent of feature reduction, altogether showing that reduction either improves performance or can reduce the variable-sourcing burden at hospital admission with little performance loss. Extreme feature reduction to a single most salient feature, often age, demonstrated large standalone explanatory power, with the best-performing model achieving an accuracy of 81.6% (95% CI 81.1%-82.1%); this demonstrates the relatively marginal improvement that additional variables bring to the tested models.

Conclusions : Predictive statistical models have promising performance in early prediction of death among patients with COVID-19. Strong dimensionality reduction was shown to further improve baseline performance on selected classifiers and only marginally reduce it in others, highlighting the importance of feature reduction in future model construction and the feasibility of deprioritizing large, hard-to-source, and nonessential feature sets in real world settings.

Doyle Riccardo

COVID-19, artificial intelligence, automation, comorbidities, coronavirus, dimensionality reduction, epidemiology, hospital, machine learning, medical informatics, model development, mortality, pre-existing conditions, prediction, prognosis, public data, resource management, triage

General General

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia.

In Tomography (Ann Arbor, Mich.)

We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration. Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality. In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12-7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02-4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32-2.33, p < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 95% CI = 1.02-1.15, p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined. AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification.

Szabó István Viktor, Simon Judit, Nardocci Chiara, Kardos Anna Sára, Nagy Norbert, Abdelrahman Renad-Heyam, Zsarnóczay Emese, Fejér Bence, Futácsi Balázs, Müller Veronika, Merkely Béla, Maurovich-Horvat Pál

2021-Nov-01

COVID-19, artificial intelligence, computed tomography

General General

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models.

In SN computer science

As the number of COVID-19 cases increases day by day, the situation and livelihood of people throughout the world deteriorates. The goal of this study is to use machine learning models to identify disease and forecast whether or not a person is infected with the virus or another common illness. More articles about COVID-19 will be released starting in 2020, but we still do not have a reliable prediction mechanism to diagnose the disease with 100% accuracy. This comparison is done to see which model is the most effective in detecting and predicting disease. Despite the fact that we have immunizations, we require a best-prediction strategy to assist all humans in surviving. Researchers claimed that the supervised learning method predicts more accurately than the unsupervised learning method in the majority of studies. Supervised learning is the process of mapping inputs to derived outputs using a set of variables and created functions. This will also help us to optimize performance criteria using experience. It is further divided into two categories: classification and regression. According to recent studies, classification models are more accurate than other models.

Abirami R Sudha, Kumar G Suresh

2022

Classification, Coronavirus disease (COVID-19), Machine learning, Regression, Supervised, Unsupervised

General General

A method for the identification of COVID-19 biomarkers in human breath using Proton Transfer Reaction Time-of-Flight Mass Spectrometry.

In EClinicalMedicine

Background : COVID-19 has caused a worldwide pandemic, making the early detection of the virus crucial. We present an approach for the determination of COVID-19 infection based on breath analysis.

Methods : A high sensitivity mass spectrometer was combined with artificial intelligence and used to develop a method for the identification of COVID-19 in human breath within seconds. A set of 1137 positive and negative subjects from different age groups, collected in two periods from two hospitals in the USA, from 26 August, 2020 until 15 September, 2020 and from 11 September, 2020 until 11 November, 2020, was used for the method development. The subjects exhaled in a Tedlar bag, and the exhaled breath samples were subsequently analyzed using a Proton Transfer Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS). The produced mass spectra were introduced to a series of machine learning models. 70% of the data was used for these sub-models' training and 30% was used for testing.

Findings : A set of 340 samples, 95 positives and 245 negatives, was used for the testing. The combined models successfully predicted 77 out of the 95 samples as positives and 199 out of the 245 samples as negatives. The overall accuracy of the model was 81.2%. Since over 50% of the total positive samples belonged to the age group of over 55 years old, the performance of the model in this category was also separately evaluated on 339 subjects (170 negative and 169 positive). The model correctly identified 166 out of the 170 negatives and 164 out of the 169 positives. The model accuracy in this case was 97.3%.

Interpretation : The results showed that this method for the identification of COVID-19 infection is a promising tool, which can give fast and accurate results.

Liangou Aikaterini, Tasoglou Antonios, Huber Heinz J, Wistrom Christopher, Brody Kevin, Menon Prahlad G, Bebekoski Thomas, Menschel Kevin, Davidson-Fiedler Marlise, DeMarco Karl, Salphale Harshad, Wistrom Jonathan, Wistrom Skyler, Lee Richard J

2021-Dec

General General

COVID-19 Anomaly Detection and Classification Method based on Supervised Machine Learning of Chest X-ray Images.

In Results in physics

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

Hasoon Jamal N, Hussein Fadel Ali, Subhi Hameed Rasha, Mostafa Salama A, Ahmed Khalaf Bashar, Abed Mohammed Mazin, Nedoma Jan

2021-Nov-22

COVID-19 diagnosis, Haralick, k-nearest neighbor, local binary pattern, machine learning, support vector machine, x-ray image

General General

Office buildings occupancy analysis and prediction associated with the impact of the COVID-19 pandemic.

In Sustainable cities and society

Buildings' occupancy is one of the important factors causing the energy performance and sustainability gap in buildings. Better occupancy prediction decreases this gap both in the design stage and in the use phase of the building. Machine learning-based models proved to be very accurate and fast for occupancy prediction when buildings are exploited under normal conditions. Meanwhile, during the Covid-19 pandemic occupancy of the offices has dramatically changed. The study presents 2 office buildings' long-term monitoring results for different periods of the pandemic. It aims to analyse actual occupancies during the pandemic and its influence on the ELM (Extreme Learning Machine) based occupancy-forecasting models' reliability. The results show much lower actual occupancies in the offices than given in standards and methodologies; it is still low even when quarantines are cancelled. Average peak occupancy within the whole measured period is: for Building A - 12-20 % and for Building B - 2-23 %. The daily occupancy schedules differ for both offices as they belong to different industries. ELM-SA model has shown low accuracies during pandemic periods as a result of lower occupancies - R2 = 0.27-0.56.

Motuzienė Violeta, Bielskus Jonas, Lapinskienė Vilūnė, Rynkun Genrika, Bernatavičienė Jolita

2021-Nov-20

Covid-19, ELM, long-term monitoring, occupancy, pandemic, prediction

Radiology Radiology

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

In Applied soft computing

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

Huang Zhongwei, Lei Haijun, Chen Guoliang, Li Haimei, Li Chuandong, Gao Wenwen, Chen Yue, Wang Yaofa, Xu Haibo, Ma Guolin, Lei Baiying

2021-Nov-24

3D-CNN, COVID-19 diagnosis, Decision fusion, Histogram of oriented gradient, Multi-center sparse learning

General General

Towards sound based testing of COVID-19-Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge.

In Computer speech & language

The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the "Coswara" project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled "Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge". The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs.

Sharma Neeraj Kumar, Muguli Ananya, Krishnan Prashant, Kumar Rohit, Chetupalli Srikanth Raj, Ganapathy Sriram

2021-Nov-24

Acoustics, COVID-19, Healthcare, Machine learning, Respiratory diagnosis

Radiology Radiology

Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.

In Iranian journal of medical sciences

Background : Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.

Methods : In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest.

Results : The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%).

Conclusion : In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.

Tabatabaei Mohsen, Tasorian Baharak, Goyal Manu, Moini Abdollatif, Sotoudeh Houman

2021-Nov

** Artificial intelligence, COVID-19, Influenza, Human, Tomography**

General General

A large-scale systematic survey of SARS-CoV-2 antibodies reveals recurring molecular features

bioRxiv Preprint

In the past two years, the global research in combating COVID-19 pandemic has led to isolation and characterization of numerous human antibodies to the SARS-CoV-2 spike. This enormous collection of antibodies provides an unprecedented opportunity to study the antibody response to a single antigen. Using information derived from 88 research publications and 13 patents, we have assembled a dataset of ~8,000 human antibodies to the SARS-CoV-2 spike from >200 donors. Analysis of antibodies that target different domains of the spike protein reveals a number of common (public) responses to SARS-CoV-2, exemplified via recurring IGHV/IGK(L)V pairs, CDR H3 sequences, IGHD usage, and somatic hypermutation. We further present a proof-of-concept for predicting antigen specificity by using deep learning to differentiate sequences of antibodies to SARS-CoV-2 spike and to influenza hemagglutinin. Overall, this study not only provides an informative resource for antibody research, but fundamentally advances our molecular understanding of public antibody responses.

Wang, Y.; Yuan, M.; Peng, J.; Wilson, I. A.; Wu, N. C.

2021-11-30

Public Health Public Health

Impacts of emergency health protection measures upon air quality, traffic and public health: Evidence from Oxford, UK.

In Environmental pollution (Barking, Essex : 1987)

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM2.5, PM10 and O3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM2.5 and O3 concentration changes were not significant, but PM10 increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO2 emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO2 emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting.

Singh Ajit, Bartington Suzanne E, Song Congbo, Ghaffarpasand Omid, Kraftl Martin, Shi Zongbo, Pope Francis D, Stacey Brian, Hall James, Thomas G Neil, Bloss William J, Leach Felix C P

2021-Nov-26

Air quality, COVID-19, Deweathered, Meteorology, Oxford city, Vehicle emissions

General General

Prediction of COVID-19 epidemic situation via fine-tuned IndRNN.

In PeerJ. Computer science

The COVID-19 pandemic is the most serious catastrophe since the Second World War. To predict the epidemic more accurately under the influence of policies, a framework based on Independently Recurrent Neural Network (IndRNN) with fine-tuning are proposed for predict the epidemic development trend of confirmed cases and deaths in the United Stated, India, Brazil, France, Russia, China, and the world to late May, 2021. The proposed framework consists of four main steps: data pre-processing, model pre-training and weight saving, the weight fine-tuning, trend predicting and validating. It is concluded that the proposed framework based on IndRNN and fine-tuning with high speed and low complexity, has great fitting and prediction performance. The applied fine-tuning strategy can effectively reduce the error by up to 20.94% and time cost. For most of the countries, the MAPEs of fine-tuned IndRNN model were less than 1.2%, the minimum MAPE and RMSE were 0.05%, and 1.17, respectively, by using Chinese deaths, during the testing phase. According to the prediction and validation results, the MAPEs of the proposed framework were less than 6.2% in most cases, and it generated lowest MAPE and RMSE values of 0.05% and 2.14, respectively, for deaths in China. Moreover, Policies that play an important role in the development of COVID-19 have been summarized. Timely and appropriate measures can greatly reduce the spread of COVID-19; untimely and inappropriate government policies, lax regulations, and insufficient public cooperation are the reasons for the aggravation of the epidemic situations. The code is available at https://github.com/zhhongsh/COVID19-Precdiction. And the prediction by IndRNN model with fine-tuning are now available online (http://47.117.160.245:8088/IndRNNPredict).

Hong Zhonghua, Fan Ziyang, Tong Xiaohua, Zhou Ruyan, Pan Haiyan, Zhang Yun, Han Yanling, Wang Jing, Yang Shuhu, Wu Hong, Li Jiahao

2021

COVID-19, Deep Learning, Fine-tuning, Gated-Recurrent-Unit, Independently Recurrent Neural Network, Long-Short-Term-Memory, Prediction Model

General General

Household visitation during the COVID-19 pandemic.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic has posed novel risks related to the indoor mixing of individuals from different households and challenged policymakers to adequately regulate this behaviour. While in many cases household visits are necessary for the purpose of social care, they have been linked to broadening community transmission of the virus. In this study we propose a novel, privacy-preserving framework for the measurement of household visitation at national and regional scales, making use of passively collected mobility data. We implement this approach in England from January 2020 to May 2021. The measures expose significant spatial and temporal variation in household visitation patterns, impacted by both national and regional lockdown policies, and the rollout of the vaccination programme. The findings point to complex social processes unfolding differently over space and time, likely informed by variations in policy adherence, vaccine relaxation, and regional interventions.

Ross Stuart, Breckenridge George, Zhuang Mengdie, Manley Ed

2021-Nov-25

Public Health Public Health

Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.

In Computers in biology and medicine

The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust experimental design combining deep learning with molecular docking experiments to identify the most promising candidates from the list of FDA-approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning-based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2440 FDA-approved and 8168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA-approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran around 50,000 docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. A list of 49 most promising FDA-approved drugs with the best consensus KIBA scores and binding affinity values against selected SARS-CoV-2 viral proteins was generated. Most importantly, 16 drugs including anidulafungin, velpatasvir, glecaprevir, rifapentine, flavin adenine dinucleotide (FAD), terlipressin, and selinexor demonstrated the highest predicted inhibitory potential against key SARS-CoV-2 viral proteins. We further measured the inhibitory activity of 5 compounds (rifapentine, velpatasvir, glecaprevir, anidulafungin, and FAD disodium) on SARS-CoV-2 PLpro using Ubiquitin-Rhodamine 110 Gly fluorescent intensity assay. The highest inhibition of PLpro activity was seen with rifapentine (IC50: 15.18 μM) and FAD disodium (IC50: 12.39 μM), the drugs with high predicted KIBA scores and binding affinities.

Anwaar Muhammad U, Adnan Farjad, Abro Asma, Khan Rayyan A, Rehman Asad U, Osama Muhammad, Rainville Christopher, Kumar Suresh, Sterner David, Javed Saad, Jamal Syed B, Baig Ahmadullah, Shabbir Muhammad R, Ahsan Waseh, Butt Tauseef R, Assir Muhammad Z

2021-Nov-20

Binding affinity, Docking, Drug repurposing, Machine learning, SARS-CoV-2

General General

Deep-AVPpred: Artificial intelligence driven discovery of peptide drugs for viral infections.

In IEEE journal of biomedical and health informatics

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

Sharma Ritesh, Shrivastava Sameer, Singh Sanjay Kumar, Kumar Abhinav, Singh Amit Kumar, Saxena Sonal

2021-Nov-25

General General

Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations.

In Bioengineering (Basel, Switzerland)

The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing resources exists in regional and developing areas of the world. In this paper, we evaluate and report the computational and predictive performance design trade-offs for four candidate deep learning models that can be deployed for rapid malaria case finding. The goal is to maximise malaria detection accuracy while reducing computing resource and energy consumption. Based on our experimental results using a blood smear malaria test data set, the quantised versions of Basic Convolutional Neural Network (B-CNN) and MobileNetV2 have better malaria detection performance (up to 99% recall), lower memory usage (2MB 8-bit quantised model) and shorter inference time (33-95 microseconds on mobile phones) than VGG-19 fine-tuned and quantised models. Hence, we have implemented MobileNetV2 in our mobile application as it has even a lower memory requirement than B-CNN. This work will help to counter the negative effects of COVID-19 on the previous successes towards global malaria elimination.

Eze Peter U, Asogwa Clement O

2021-Oct-21

deep learning, digital health, edge devices, malaria, model quantisation, resource optimisation

General General

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

In Computational and mathematical methods in medicine

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

Bangyal Waqas Haider, Qasim Rukhma, Rehman Najeeb Ur, Ahmad Zeeshan, Dar Hafsa, Rukhsar Laiqa, Aman Zahra, Ahmad Jamil

2021

General General

Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net.

In Scientific reports ; h5-index 158.0

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.

Zhang Qin, Ren Xiaoqiang, Wei Benzheng

2021-Nov-24

Surgery Surgery

The amputation and mortality of inpatients with diabetic foot ulceration in the COVID-19 pandemic and postpandemic era: A machine learning study.

In International wound journal

This study aimed to explore the clinical characteristic and outcomes of inpatients with diabetic foot ulceration (DFU) in 2019 (prelockdown) and 2020 (postlockdown) due to the COVID-19 pandemic, at an emergency medical service unit. Prediction models for mortality and amputation were developed to describe the risk factors using a machine learning-based approach. Hospitalized DFU patients (N = 23) were recruited after the lockdown in 2020 and matched with corresponding inpatients (N = 23) before lockdown in 2019. Six widely used machine learning models were built and internally validated using 3-fold cross-validation to predict the risk of amputation and death in DFU inpatients under the COVID-19 pandemic. Previous DF ulcers, prehospital delay, and mortality were significantly higher in 2020 compared to 2019. Diabetic foot patients in 2020 had higher hs-CRP levels (P = .037) but lower hemoglobin levels (P = .017). The extreme gradient boosting (XGBoost) performed best in all models for predicting amputation and mortality with the highest area under the curve (0.86 and 0.94), accuracy (0.80 and 0.90), sensitivity (0.67 and 1.00), and negative predictive value (0.86 and 1.00). A long delay in admission and a higher risk of mortality was observed in patients with DFU who attended the emergency center during the COVID-19 post lockdown. The XGBoost model can provide evidence-based risk information for patients with DFU regarding their amputation and mortality. The prediction models would benefit DFU patients during the COVID-19 pandemic.

Du Chenzhen, Li Yuyao, Xie Puguang, Zhang Xi, Deng Bo, Wang Guixue, Hu Youqiang, Wang Min, Deng Wu, Armstrong David G, Ma Yu, Deng Wuquan

2021-Nov-24

COVID-19 pandemic, amputation, diabetic foot ulceration, machine learning, mortality

General General

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19.

In Research square

BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

Xu Yixi, Trivedi Anusua, Becker Nicholas, Blazes Marian, Ferres Juan, Lee Aaron, Liles W, Bhatraju Pavan

2021-Nov-16

General General

Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods.

In Procedia computer science

The large amount of data accumulated so far on the dynamics of the COVID-19 outbreak has allowed assessing the accuracy of forecasting methods in retrospect. This work compares several basic time series analysis methods, including machine learning methods, for forecasting the number of confirmed cases for some days ahead. Year-long data for all regions of Russia has been used from the Yandex DataLens platform. As a result, accuracy estimates for these basic methods have been obtained for Russian regions and Russia as a whole, in dependence on the forecasting horizon. The best basic models for forecasting for 14 days are exponential smoothing and ARIMA, with an error of 11-19% by the MAPE metric for the latest part of the course of the epidemic. The accuracies obtained can be considered as baselines for more complex prospective models.

Naumov A V, Moloshnikov I A, Serenko A V, Sboev A G, Rybka R B

2021

COVID forecasting, Machine learning, Time series analysis

General General

India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability.

In Soft computing

The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed.

Ketu Shwet, Mishra Pramod Kumar

2021-Nov-19

CNN-LSTM, COVID-19, Deep learning, Medical resource, Time series prediction

General General

A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods.

In Expert systems with applications

The restrictions have been preferred by governments to reduce the spread of Covid-19 and to protect people's health according to regional risk levels. The risk levels of locations are determined due to threshold values ​​based on the number of cases per 100,000 people without environmental variables. The purpose of our study is to apply unsupervised machine learning techniques to determine the cities with similar risk levels by using the number of cases and environmental parameters. Hierarchical, partitional, soft, and gray relational clustering algorithms were applied to different datasets created with weekly the number of cases, population densities, average ages, and air pollution levels. Comparisons of the clustering algorithms were performed by using internal validation indexes, and the most successful method was identified. In the study, it was revealed that the most successful method in clustering based on the number of cases is Gray Relational Clustering. The results show that using the environmental variables for restrictions requires more clusters than 4 for healthier decisions and Gray Relational Clustering gives stable results, unlike other algorithms.

Fidan Huseyin, Erkan Yuksel Mehmet

2021-Nov-19

Clustering, Covid-19, Gray relational clustering, Restrictions, Risk levels, Unsupervised machine learning

Radiology Radiology

Factors determining generalization in deep learning models for scoring COVID-CT images.

In Mathematical biosciences and engineering : MBE

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.

Horry Michael James, Chakraborty Subrata, Pradhan Biswajeet, Fallahpoor Maryam, Chegeni Hossein, Paul Manoranjan

2021-Oct-27

** COVID-19 scoring , computed tomography , deep learning , external validation , image pre-processing , model generalization **

General General

A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences.

In Mathematical biosciences and engineering : MBE

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.

Deif Mohanad A, Solyman Ahmed A A, Kamarposhti Mehrdad Ahmadi, Band Shahab S, Hammam Rania E

2021-Oct-15

** COVID-19 , GRU , LSTM Multi-class classification , SARS-CoV-2 , coronavirus , deep learning , recurrent neural networks **

Public Health Public Health

Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study.

In Mathematical biosciences and engineering : MBE

Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that there is a significant benefit in adhering to the COVID-19 NPIs.

Iyaniwura Sarafa A, Rabiu Musa, David Jummy F, Kong Jude D

2021-Oct-15

** COVID-19 , SARS-CoV-2 , adherent and non-adherent , direct and indirect transmission , epidemics , non-pharmaceutical intervention , population dynamics , seir models **

General General

Tackling pandemics in smart cities using machine learning architecture.

In Mathematical biosciences and engineering : MBE

With the recent advancement in analytic techniques and the increasing generation of healthcare data, artificial intelligence (AI) is reinventing the healthcare system for tackling pandemics securely in smart cities. AI tools continue register numerous successes in major disease areas such as cancer, neurology and now in new coronavirus SARS-CoV-2 (COVID-19) detection. COVID-19 patients often experience several symptoms which include breathlessness, fever, cough, nausea, sore throat, blocked nose, runny nose, headache, muscle aches, and joint pains. This paper proposes an artificial intelligence (AI) algorithm that predicts the rate of likely survivals of COVID-19 suspected patients based on good immune system, exercises and age quantiles securely. Four algorithms (Naïve Bayes, Logistic Regression, Decision Tree and k-Nearest Neighbours (kNN)) were compared. We performed True Positive (TP) rate and False Positive (FP) rate analysis on both positive and negative covid patients data. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Naïve Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive covid patients, Naïve Bayes outperformed other models with a score of 10.90%. On the other hand, Naïve Bayes obtained a score of 89.10% for FP rate for negative patients while Logistic Regression, kNN, and Decision Tree obtained scores of 93.90%, 93.90%, and 94.50%, respectively.

Ngabo Desire, Dong Wang, Ibeke Ebuka, Iwendi Celestine, Masabo Emmanuel

2021-Sep-27

** artificial intelligence , pandemics , smart cities **

General General

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification.

In IEEE access : practical innovations, open solutions

The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.

El-Kenawy El-Sayed M, Mirjalili Seyedali, Ibrahim Abdelhameed, Alrahmawy Mohammed, El-Said M, Zaki Rokaia M, Eid Marwa Metwally

2021

Chest X-ray, convolutional neural network, multilayer perceptron, optimization algorithm, squirrel search optimization, transfer learning

General General

Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs.

In IEEE access : practical innovations, open solutions

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid.

Zhang Pengyi, Zhong Yunxin, Deng Yulin, Tang Xiaoying, Li Xiaoqiong

2020

COVID-19~diagnosis, DRRs, X-ray imaging, deep learning, infection segmentation

Radiology Radiology

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.

In IEEE access : practical innovations, open solutions

Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.

Casiraghi Elena, Malchiodi Dario, Trucco Gabriella, Frasca Marco, Cappelletti Luca, Fontana Tommaso, Esposito Alessandro Andrea, Avola Emanuele, Jachetti Alessandro, Reese Justin, Rizzi Alessandro, Robinson Peter N, Valentini Giorgio

2020

Associative tree, Boruta feature selection, COVID-19, clinical data analysis, generalized linear models, missing data imputation, random forest classifier, risk prediction

General General

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

In IEEE access : practical innovations, open solutions

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19's endemic and pandemic.

Lai Yaoming, Li Guangming, Wu Dongmei, Lian Wanmin, Li Cheng, Tian Junzhang, Ma Xiaofen, Chen Hui, Xu Wen, Wei Jun, Zhang Yaqin, Jiang Guihua

2020

2019 novel coronavirus-infected pneumonia, COVID-19, chest CT, few-shot learning

General General

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life.

In IEEE access : practical innovations, open solutions

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

Vedaei Seyed Shahim, Fotovvat Amir, Mohebbian Mohammad Reza, Rahman Gazi M E, Wahid Khan A, Babyn Paul, Marateb Hamid Reza, Mansourian Marjan, Sami Ramin

2020

COVID-19, IoT, health monitoring, pandemic, smart healthcare

General General

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

In Soft computing

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.

Laxmi Lydia E, Anupama C S S, Beno A, Elhoseny Mohamed, Alshehri Mohammad Dahman, Selim Mahmoud M

2021-Nov-18

COVID-19, Chest X-ray images, Cognitive computing, Deep learning, Edge computing, Federated learning, Internet of things, Pattern recognition

General General

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

In Molecular informatics

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

John Lijo, Soujanya Yarasi, Mahanta Hridoy Jyoti, Narahari Sastry G

2021-Nov-23

Antivirals, Chemoinformatics, Feature Selection, MCC, Machine Learning, Molecular Descriptors, SARS-COVID-19

Public Health Public Health

Data revolution, health status transformation and the role of artificial intelligence for health and pandemic preparedness in the African context.

In BMC proceedings

BACKGROUND : Artificial Intelligence (AI) platforms, increasingly deployed in public health, utilize robust data systems as a critical component for health emergency preparedness. Yet, Africa faces numerous challenges in the availability, analyses, and use of data to inform health decision-making. Countries have limited access to their population data. Those with access, struggle to utilize these data for program improvements. Owing to the rapid growth of mobile phone ownership and use in the region, Africa is poised to leverage AI technologies to increase the adoption, access and use of data for health. To discuss and propose solutions for responsible development and adoption of innovations like AI in Africa, a virtual workshop was organized from the 21st to 24th June, 2021. This report highlights critical policy dimensions of strengthening digital health ecosystems by high-level policymakers, technical experts, academia, public and private sector partners.

METHOD : The four days' workshop focused on nine sessions, with each session focusing on three themes. Discussions during the sessions concentrated on public and private sectors, the academia and multilateral organizations' deployment of AI. These discussions expanded participants' understanding of AI, the opportunities and challenges that exist during adoption, including the future of AI for health in the African region. Approximately 250 participants attended the workshop, including countries representatives from ministries of Health, Information and Technology, Developmental Organizations, Private Sector, Academia and Research Institutions among others.

RESULTS : The workshop resolved that governments and relevant stakeholders should collaborate to ensure that AI and digital health receive critical attention. Government ownership and leadership were identified as critical for sustainable financing and effective scale-up of AI-enabled applications in Africa. Thus, government is to ensure that key recommendations from the workshop are implemented to improve health sector development in Africa.

CONCLUSIONS : The AI workshop was a good forum to deliberate important issues regarding AI for health in the African context. It was concluded that there is a need to focus on vital priorities in deploying AI in Africa: Data protection, privacy and sharing protocols; training and creating platforms for researchers; funding and business models; developing frameworks for assessing and implementing AI; organizing forums and conferences on AI; and instituting regulations, governance and ethical guidelines for AI. There is a need to adopt a health systems approach in planning for AI to reduce inefficiencies, redundancies while increasing effectiveness in the use of AI. Thus, robust collaborations and partnerships among governments and various stakeholders were identified as key.

Ibeneme Sunny, Okeibunor Joseph, Muneene Derrick, Husain Ishrat, Bento Pascoal, Gaju Carol, Housseynou Ba, Chibi Moredreck, Karamagi Humphrey, Makubalo Lindiwe

2021-Nov-22

Africa, Artificial intelligence, COVID-19, Digital health, Healthcare innovation

General General

Mental health symptoms among American veterans during the COVID-19 Pandemic.

In Psychiatry research ; h5-index 64.0

We examined the symptom trajectories of posttraumatic stress disorder (PTSD), depression, and anxiety among 1,230 American veterans assessed online one month prior to the COVID-19 outbreak in the United States (February 2020) through the next year (August 2020, November 2020, February 2021). Veterans slightly increased mental health symptoms over time and those with pre-pandemic alcohol and cannabis use disorders reported greater symptoms compared to those without. Women and racial/ethnic minority veterans reported greater symptoms pre-pandemic but less steep increases over time compared to men and white veterans. Findings point to the continued need for mental health care efforts with veterans.

Pedersen Eric R, Davis Jordan P, Prindle John, Fitzke Reagan E, Tran Denise D, Saba Shaddy

2021-Nov-17

Mental health, Substance use, Veterans

General General

Methods to Engage Patients in the Modern Clinic.

In Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology

OBJECTIVE : To identify current patient and provider engagement methods that utilize technology in allergy and immunology clinics, hospitals and at home.

DATA SOURCES : Apple App Store and Google searches for allergy and immunology technology apps, PubMed search of literature involving keywords of: website, technology, EMR, medical devices, disparity in technology, coding for remote patient monitoring and artificial intelligence.

STUDY SELECTIONS : Studies that addressed the keywords were included and narrowed down based upon their applicability in the allergy and immunology clinic.

RESULTS : There has been rapid innovation in the digital healthcare space with expansion of EMR services and the patient portal, creation of allergy and immunology specific medical devices and apps with remote patient monitoring capabilities, and website and artificial intelligence development to interact with patients.

CONCLUSION : These technological advances provide distinct advantages to the provider and patient, but also have a burden of time for evaluation of the data for the provider and disparate access to certain technologies for patients. The development of these technologies has been fast-tracked since the start of the Covid-19 pandemic. With the explosion in telehealth and medical device development, advancement of medical technology is not showing any signs of slowing down. It is paving a new way to interact with patients in the future.

Maurer Laura E, Bansal Chandani, Bansal Priya

2021-Nov-20

App, Artificial Intelligence, Covid-19, Digital, EMR, Healthcare Disparities, Mobile Device, Patient Portal, Technology, Website

General General

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

In PloS one ; h5-index 176.0

BACKGROUND : Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis.

METHODS AND PERFORMANCE : Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days).

CONCLUSION : We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.

Bishop Jennifer A, Javed Hamza A, El-Bouri Rasheed, Zhu Tingting, Taylor Thomas, Peto Tim, Watkinson Peter, Eyre David W, Clifton David A

2021

General General

Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning.

In Patterns (New York, N.Y.)

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome including previously unappreciated features in spike, RdRp and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural and immunological features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.

Park Jonathan J, Chen Sidi

2021-Nov-18

General General

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic.

In IEEE access : practical innovations, open solutions

Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

Poongodi M, Malviya Mohit, Hamdi Mounir, Rauf Hafiz Tayyab, Kadry Seifedine, Thinnukool Orawit

2021

5G, CT-scan, Epidemic, X-Ray, artificial intelligence, cloud, coronavirus, drone, telemedicine

General General

UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.

In BMC medical imaging

BACKGROUND : With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning.

METHODS : This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality.

RESULTS : The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models.

CONCLUSION : In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

Miao Rui, Dong Xin, Xie Sheng-Li, Liang Yong, Lo Sio-Long

2021-Nov-22

CNN, COVID-19, UMLF-COVID, X-ray

General General

Unsupervised clustering analysis of SARS-Cov-2population structure reveals six major subtypes atearly stage across the world

bioRxiv Preprint

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.

Li, Y.; Liu, Q.; Zeng, Z.; Luo, Y.

2021-11-24

General General

From Hume to Wuhan: An Epistemological Journey on the Problem of Induction in COVID-19 Machine Learning Models and its Impact Upon Medical Research.

In IEEE access : practical innovations, open solutions

Advances in computer science have transformed the way artificial intelligence is employed in academia, with Machine Learning (ML) methods easily available to researchers from diverse areas thanks to intuitive frameworks that yield extraordinary results. Notwithstanding, current trends in the mainstream ML community tend to emphasise wins over knowledge, putting the scientific method aside, and focusing on maximising metrics of interest. Methodological flaws lead to poor justification of method choice, which in turn leads to disregard the limitations of the methods employed, ultimately putting at risk the translation of solutions into real-world clinical settings. This work exemplifies the impact of the problem of induction in medical research, studying the methodological issues of recent solutions for computer-aided diagnosis of COVID-19 from chest X-Ray images.

Vega Carlos

2021

Biomedical imaging, X-rays, computational systems biology, machine learning, philosophical considerations

Public Health Public Health

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.

In IEEE access : practical innovations, open solutions

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Nguyen Dinh C, Ding Ming, Pathirana Pubudu N, Seneviratne Aruna

2021

Artificial Intelligence (AI), Blockchain, SARS-CoV-2, coronavirus (COVID-19), deep learning, epidemic, machine learning, privacy, security

General General

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images.

In IEEE access : practical innovations, open solutions

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.

Calderon-Ramirez Saul, Yang Shengxiang, Moemeni Armaghan, Colreavy-Donnelly Simon, Elizondo David A, Oala Luis, Rodriguez-Capitan Jorge, Jimenez-Navarro Manuel, Lopez-Rubio Ezequiel, Molina-Cabello Miguel A

2021

Coronavirus, Covid-19, MixMatch, Uncertainty estimation, chest x-ray, computer aided diagnosis, semi-supervised deep learning

General General

COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images.

In IEEE access : practical innovations, open solutions

As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.

Zhou Changjian, Song Jia, Zhou Sihan, Zhang Zhiyao, Xing Jinge

2021

COVID-19, Resnet-SVM, deep learning, medical image processing

General General

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.

In IEEE access : practical innovations, open solutions

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

Navaz Alramzana Nujum, Serhani Mohamed Adel, El Kassabi Hadeel T, Al-Qirim Nabeel, Ismail Heba

2021

Artificial intelligence, COVID-19, IoT, big data, deep learning, healthcare, robotics, smart and connected healthcare

General General

Deep Learning for SARS COV-2 Genome Sequences.

In IEEE access : practical innovations, open solutions

The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses. Besides, we classify whether a genome sequence contains candidate regulatory motifs or otherwise. Regulatory motifs bind to transcription factors. Transcription factors are responsible for the expression of genes. The experimental results show that at peak performance, the proposed convolutional neural network bi-directional long short-term memory (CNN-Bi-LSTM) model achieves a classification accuracy of 99.95%, area under curve receiver operating characteristic (AUC ROC) of 100.00%, a specificity of 99.97%, the sensitivity of 99.97%, Cohen's Kappa equal to 0.9978, Mathews Correlation Coefficient (MCC) equal to 0.9978 for the classification of SARS CoV-2 amongst Coronaviruses. Also, the CNN-Bi-LSTM correctly detects whether a sequence has candidate regulatory motifs or binding-sites with a classification accuracy of 99.76%, AUC ROC of 100.00%, a specificity of 99.76%, a sensitivity of 99.76%, MCC equal to 0.9980, and Cohen's Kappa of 0.9970 at peak performance. These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regulatory motifs in the SARS CoV-2 genes.

Whata Albert, Chimedza Charles

2021

Bi-directional long-short memory, SARS-CoV-2, convolutional neural network, coronavirus deep learning, deoxyribonucleic acid

Public Health Public Health

IoT Platform for COVID-19 Prevention and Control: A Survey.

In IEEE access : practical innovations, open solutions

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and low vaccination rates, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.

Dong Yudi, Yao Yu-Dong

2021

COVID-19, Internet of Things, SARS-CoV-2, artificial intelligence, big data, fog computing, smart healthcare

General General

MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

In IEEE access : practical innovations, open solutions

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

Pei Hong-Yang, Yang Dan, Liu Guo-Ru, Lu Tian

2021

COVID-19, CT, MPS-Net, U-Nnet

General General

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients.

In IEEE access : practical innovations, open solutions

Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.

Catala Omar Del Tejo, Igual Ismael Salvador, Perez-Benito Francisco Javier, Escriva David Millan, Castello Vicent Ortiz, Llobet Rafael, Perez-Cortes Juan-Carlos

2021

COVID-19, Deep learning, bias, chest X-ray, convolutional neural networks, saliency map, segmentation

General General

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal.

In IEEE access : practical innovations, open solutions

The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%~19.98% better precision, getting closer to achievable bounds for estimation.

Su Zhuoran, Pahlavan Kaveh, Agu Emmanuel

2021

BLE, COVID-19, PACT, RSSI features, classical estimation theory, machine learning, proximity detection

General General

Efficient masked face recognition method during the COVID-19 pandemic.

In Signal, image and video processing

The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN), namely VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods.

Hariri Walid

2021-Nov-15

COVID-19, Deep learning, Face recognition, Masked face

General General

Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network.

In Biomedical signal processing and control

Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.

Soltanian Mohammad, Borna Keivan

2022-Feb

Computational complexity, Convolutional neural network, Kernel separation, MFCC, Quadratic convolution

General General

Novel multi-site graph convolutional network with supervision mechanism for COVID-19 diagnosis from X-ray radiographs.

In Applied soft computing

The novel Coronavirus disease 2019 (COVID-2019) has become a global pandemic and affected almost all aspects of our daily life. The total number of positive COVID-2019 cases has exponentially increased in the last few months due to the easy transmissibility of the virus. It can be detected using the nucleic acid test or the antibodies blood test which are not always available and take several hours to get the results. Therefore, researchers proposed computer-aided diagnosis systems using the state-of-the-art artificial intelligence techniques to learn imaging biomarkers from chest computed tomography and X-ray radiographs to effectively diagnose COVID-19. However, previous methods either adopted transfer learning from a pre-trained model on natural images or trained on limited datasets. Either cases may lead to accuracy deficiency or overfitting. In addition, feature space suffers from noise and outliers when collecting X-ray images from multiple datasets. In this paper, we overcome the previous limitation by firstly collecting a large-scale X-ray dataset from multiple resources. Our dataset includes 11,312 images collected from 10 different data repositories. To alleviate the effect of the noise, we suppress it in the feature space of our new dataset. Secondly, we introduce a supervision mechanism and combine it with the VGG-16 network to consider the differences between the COVID-19 and healthy cases in the feature space. Thirdly, we propose a multi-site (center) COVID-19 graph convolutional network (GCN) that exploits dataset information, the status of training samples, and initial scores to effectively classify the disease status. Extensive experiments using different convolutional neural network-based methods with and without the supervision mechanism and different classifiers are performed. Results demonstrate the effectiveness of the proposed supervision mechanism in all models and superior performance with the proposed GCN.

Elazab Ahmed, Elfattah Mohamed Abd, Zhang Yuexin

2021-Nov-16

COVID-19, Chest X-ray radiographs, Multi-site graph convolutional network, Supervision mechanism, VGG-16 network

General General

Novel approaches for COVID-19 diagnosis and treatment: a nonsystematic review.

In Turkish journal of biology = Turk biyoloji dergisi

Since COVID-19 pandemic has been continuously rising and spreading, several original contributions and review articles on COVID-19 started to appear in the literature. The review articles are mainly focus on the current status of the pandemic along with current status of the corona diagnosis and treatment process. Due to some disadvantages of the currently used methods, the improvement on the novel promising diagnosis and treatment methods of corona virus is very important issue. In this review, after briefly discussing the status of current diagnosis and treatment methods, we present to the scientific community, novel promising methods in the diagnosis and treatment of COVID-19. As with other novel approaches, first, the diagnosis potential of mass spectroscopy and optical spectroscopic methods such as UV/visible, infrared, and Raman spectroscopy coupled with chemometrics will be discussed for the corona virus infected samples based on the relevant literature. In vibrational spectroscopy studies, due to complexity of the data, multivariate analysis methods are also applied to data. The application of multivariate analysis tools that can be used to extract useful information from the data for diagnostic and characterisation purposes is also included in this review. The reviewed methods include hierarchical cluster analysis, principal component analysis, linear and quadratic discriminant analysis, support vector machine algorithm, and one form of neural networks namely deep learning method. Second, novel treatment methods such as photodynamic therapy and the use of nanoparticles in the in-corona virus therapy will be discussed. Finally, the advantages of novel promising diagnosis and treatment methods in COVID-19, over standard methods will be discussed. One of the main aims of this paper is to encourage the scientific community to explore the potential of this novel tools for their use in corona virus characterization, diagnosis, and treatment.

Garip Ustaoğlu Şebnem, Kaygusuz Hakan, Bilgin Mehmet Dinçer, Severcan Feride

2021

COVID-19, Raman spectroscopy, diagnosis, infrared spectroscopy, nanotechnology, novel methods

General General

E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.

In Neural computing & applications

Recently, the COVID-19 pandemic has triggered different behaviors in education, especially during the lockdown, to contain the virus outbreak in the world. As a result, educational institutions worldwide are currently using online learning platforms to maintain their education presence. This research paper introduces and examines a dataset, E-LearningDJUST, that represents a sample of the student's study progress during the pandemic at Jordan University of Science and Technology (JUST). The dataset depicts a sample of the university's students as it includes 9,246 students from 11 faculties taking four courses in spring 2020, summer 2020, and fall 2021 semesters. To the best of our knowledge, it is the first collected dataset that reflects the students' study progress within a Jordanian institute using e-learning system records. One of this work's key findings is observing a high correlation between e-learning events and the final grades out of 100. Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. However, the proportion of students with high marks remained similar to that of pre-pandemic courses.

Abdullah Malak, Al-Ayyoub Mahmoud, AlRawashdeh Saif, Shatnawi Farah

2021-Nov-13

COVID-19, Correlation, E-learning, Machine learning

General General

Modular composite building in urgent emergency engineering projects: A case study of accelerated design and construction of Wuhan Thunder God Mountain/Leishenshan hospital to COVID-19 pandemic.

In Automation in construction

Wuhan Leishenshan/Leishenshan ("Leishenshan" for short) hospital is a makeshift emergency hospital for treating patients diagnosed with the novel coronavirus-infected pneumonia (NCIP). Engineering construction uses modular composite building finished products to the greatest extent, which reduces the workload of field operations and saves a lot of time. The building information model (BIM) technology assists in design and construction work to meet rapid construction requirements. Besides, based on the unmanned aerial vehicles (UAVs) data analysis and application platform, digitization and intelligence in engineering construction are improved. Simultaneously, on-site construction and overall hoisting were carried out to achieve maximum efficiency. This article aims to take the construction of Leishenshan Hospital as an example to illustrate how to adopt BIM technology and other high-tech technology such as big data, artificial intelligence, drones, and 5G for the fast construction of the fabricated steel structure systems in emergency engineering projects.

Chen Ling-Kun, Yuan Rui-Peng, Ji Xing-Jun, Lu Xing-Yu, Xiao Jiang, Tao Jun-Bo, Kang Xin, Li Xin, He Zhen-Hua, Quan Shu, Jiang Li-Zhong

2021-Apr

Accelerated design and construction, Building information model (BIM) technology, Light steel structure, Modular composite building, Off-site building modular units, Unmanned aerial vehicles (UAVs), Wuhan Leishenshan hospital

General General

Global Sensitivity Analysis in Epidemiological Modeling.

In European journal of operational research

Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.

Lu Xuefei, Borgonovo Emanuele

2021-Nov-16

Analytics, COVID-19 pandemic, Global sensitivity analysis, OR in pandemics, SIR models

Public Health Public Health

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

In Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.

Jiang Jyun-Yu, Zhou Yichao, Chen Xiusi, Jhou Yan-Ru, Zhao Liqi, Liu Sabrina, Yang Po-Chun, Ahmar Jule, Wang Wei

2022-Jan-10

knowledge graph, natural language processing, pandemic surveillance, social media mining

Public Health Public Health

Data science approaches to confronting the COVID-19 pandemic: a narrative review.

In Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Zhang Qingpeng, Gao Jianxi, Wu Joseph T, Cao Zhidong, Dajun Zeng Daniel

2022-Jan-10

COVID-19, big data, data science, infectious disease, mathematical modelling

Cardiology Cardiology

Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic.

In PloS one ; h5-index 176.0

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.

Melms Leander, Falk Evelyn, Schieffer Bernhard, Jerrentrup Andreas, Wagner Uwe, Matrood Sami, Schaefer Jürgen R, Müller Tobias, Hirsch Martin

2021

General General

Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic.

In PloS one ; h5-index 176.0

The COVID-19 pandemic continues to impact people worldwide-steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people's intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people's trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people's medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures-without eroding trust in human physicians.

Frank Darius-Aurel, Elbæk Christian T, Børsting Caroline Kjær, Mitkidis Panagiotis, Otterbring Tobias, Borau Sylvie

2021

General General

A Markerless 2D Video, Facial Feature Recognition-Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible.

OBJECTIVE : The study aimed to develop a markerless 2D video, facial feature recognition-based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis.

METHODS : We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists.

RESULTS : The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD.

CONCLUSIONS : PD patients commonly exhibit masked facial features. Videos of a facial feature recognition-based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient's condition, especially during the COVID-19 pandemic.

Hou Xinyao, Zhang Yu, Wang Yanping, Wang Xinyi, Zhao Jiahao, Zhu Xiaobo, Su Jianbo

2021-Nov-19

Parkinson disease, artificial intelligence, diagnosis, facial features

General General

Intelligent financial fraud detection practices in post-pandemic era.

In Innovation (New York, N.Y.)

The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.

Zhu Xiaoqian, Ao Xiang, Qin Zidi, Chang Yanpeng, Liu Yang, He Qing, Li Jianping

2021-Nov-28

COVID-19 pandemic, artificial intelligence, financial fraud detection

oncology Oncology

Impaired Dendritic Cell Homing in COVID-19.

In Frontiers in medicine

The high mortality of COVID-19 is mostly attributed to acute respiratory distress syndrome (ARDS), whose histopathological correlate is diffuse alveolar damage (DAD). Furthermore, severe COVID-19 is often accompanied by a cytokine storm and a disrupted response of the adaptive immune system. Studies aiming to depict this dysregulation have mostly investigated the peripheral cell count as well as the functionality of immune cells. We investigated the impact of SARS-CoV-2 on antigen-presenting cells using multiplexed immunofluorescence. Similar to MERS-CoV and SARS-CoV, SARS-CoV-2 appears to be impairing the maturation of dendritic cells (DCs). DC maturation involves a switch in surface antigen expression, which enables the cells' homing to lymph nodes and the subsequent activation of T-cells. As quantitative descriptions of the local inflammatory infiltrate are still scarce, we compared the cell population of professional antigen-presenting cells (APC) in the lungs of COVID-19 autopsy cases in different stages of DAD. We found an increased count of myeloid dendritic cells (mDCs) in later stages. Interestingly, mDCs also showed no significant upregulation of maturation markers in DAD-specimens with high viral load. Accumulation of immature mDCs, which are unable to home to lymph nodes, ultimately results in an inadequate T-cell response.

Borcherding Lukas, Teksen Alime Sema, Grosser Bianca, Schaller Tina, Hirschbühl Klaus, Claus Rainer, Spring Oliver, Wittmann Michael, Römmele Christoph, Sipos Éva, Märkl Bruno

2021

COVID-19, SARS-CoV-2, artificial intelligence, dendritic cells, diffuse alveolar damage (DAD), homing, maturation, multiplexed immunofluorescence

General General

Effects of Social Mobility and Stringency Measures on the COVID-19 Outcomes: Evidence From the United States.

In Frontiers in public health

This paper examines the effects of stringency measures (provided by the Oxford Coronavirus Government Response Tracker) and total time spent away from home (provided by the Google COVID-19 Community Mobility Reports) on the COVID-19 outcomes (measured by total COVID-19 cases and total deaths related to the COVID-19) in the United States. The paper focuses on the daily data from March 11, 2020 to August 13, 2021. The ordinary least squares and the machine learning estimators show that stringency measures are negatively related to the COVID-19 outcomes. A higher time spent away from home is positively associated with the COVID-19 outcomes. The paper also discusses the potential economic implications for the United States.

Sun Jianmin, Kwek Keh, Li Min, Shen Hongzhou

2021

COVID-19 outcomes, machine learning estimator, ordinary least squares, social mobility, stringency measures, the United States economy

Radiology Radiology

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission.

In Cureus

Aim This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission. Methods The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. Results A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. Conclusion Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.

Tabatabaie Mohsen, Sarrami Amir Hossein, Didehdar Mojtaba, Tasorian Baharak, Shafaat Omid, Sotoudeh Houman

2021-Oct

artificial intelligence, covid-19, machine learning, mortality, pandemics, prognosis

General General

Machine Learning Approach Effectively Predicts Binding Between SARS-CoV-2 Spike and ACE2 Across Mammalian Species - Worldwide, 2021.

In China CDC weekly

Introduction : Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recently emergent coronavirus of natural origin and caused the coronavirus disease (COVID-19) pandemic. The study of its natural origin and host range is of particular importance for source tracing, monitoring of this virus, and prevention of recurrent infections. One major approach is to test the binding ability of the viral receptor gene ACE2 from various hosts to SARS-CoV-2 spike protein, but it is time-consuming and labor-intensive to cover a large collection of species.

Methods : In this paper, we applied state-of-the-art machine learning approaches and created a pipeline reaching >87% accuracy in predicting binding between different ACE2 and SARS-CoV-2 spike.

Results : We further validated our prediction pipeline using 2 independent test sets involving >50 bat species and achieved >78% accuracy. A large-scale screening of 204 mammal species revealed 144 species (or 61%) were susceptible to SARS-CoV-2 infections, highlighting the importance of intensive monitoring and studies in mammalian species.

Discussion : In short, our study employed machine learning models to create an important tool for predicting potential hosts of SARS-CoV-2 and achieved the highest precision to our knowledge in experimental validation. This study also predicted that a wide range of mammals were capable of being infected by SARS-CoV-2.

Ma Yue, Hu Yu, Xia Binbin, Du Pei, Wu Lili, Liang Mifang, Chen Qian, Yan Huan, Gao George F, Wang Qihui, Wang Jun

2021-Nov-12

ACE2, SARS-CoV-2, machine learning

General General

The impact of weather condition and social activity on COVID-19 transmission in the United States.

In Journal of environmental management

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

Zhang Xinxuan, Maggioni Viviana, Houser Paul, Xue Yuan, Mei Yiwen

2021-Nov-11

COVID-19 transmission, Machine learning, Random forest regression model, Social activity factor, Weather condition

General General

Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction.

In Journal of biomedical informatics ; h5-index 55.0

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.

Weinzierl Maxwell A, Harabagiu Sanda M

2021-Nov-17

COVID-19, Machine learning, Natural Language Processing, Social Media, knowledge graph embedding, vaccine misinformation

General General

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.

In General hospital psychiatry

OBJECTIVE : To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHOD : Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission.

RESULTS : Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave.

CONCLUSION : This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Castro Victor M, Hart Kamber L, Sacks Chana A, Murphy Shawn N, Perlis Roy H, McCoy Thomas H

2021-Nov-02

COVID-19, Delirium, Electronic health records, Machine learning, Predictive modeling, Replication

General General

Exploration of SARS-CoV-2 3CLpro Inhibitors by Virtual Screening Methods, FRET Detection, and CPE Assay.

In Journal of chemical information and modeling

COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3',4'-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3',4'-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3',4'-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.

Zhao Jun, Ma Qinhai, Zhang Baoyue, Guo Pengfei, Wang Zhe, Liu Yi, Meng Minsi, Liu Ailin, Yang Zifeng, Du Guanhua

2021-Nov-19

General General

When online learning becomes compulsory: Student nurses' adoption of information communication technology in a private nursing education institution.

In Curationis

BACKGROUND : Integrating the use of information communication technology (ICT) in nursing curricula when preparing student nurses for the digital health future such as the sudden online learning as a result of the coronavirus disease 2019 (COVID-19) pandemic is vital. However, when student nurses in a South African private nursing education institution, struggled to complete obligatory online learning courses, nurse educators had to search for solutions.

OBJECTIVES : To explore the barriers and enablers for ICT adoption by a diverse group of student nurses in a private nursing education institution in the Free State Province.

METHOD : Following a qualitative, explorative, interpretive-descriptive design, student nurses were invited to participate. Based on all-inclusive, purposive sampling with inclusion criteria enabled selecting, a total of 17 participants who took part in three focus groups and written narratives. Transcribed interviews underwent thematic analysis with co-coder consensus. The study adhered to strategies to enhance trustworthiness.

RESULTS : Students shared their views related to ICT and online learning within their theory and practice training. Student nurses held positive, negative and contrasting views of ICT adoption and online learning. Actions to master ICT adoption and online learning are highlighted. Information communication technology brings a challenging interdependence between nurses and technology.

CONCLUSION : Integration of ICT into nursing programmes is important. The enablers and barriers to ICT are described. Expose students to different technologies, especially using smart phones to search for (academic/non-academic) information. The adoption of ICT should enhance the learning process and facilitate deep learning. Students preferred online learning for self-assessment and described how they tried to master ICT and online learning. Information communication technologies in the clinical setting highlight the challenged interdependence between nurses and technology. Context-specific recommendations are proposed.

Bester Petra, Smit Karlien, De Beer Maryke, Myburgh Pieter H

2021-Oct-28

ICT, barriers and enablers, information communication technology, online learning, student nurses

Radiology Radiology

A review of explainable and interpretable AI with applications in COVID-19 imaging.

In Medical physics ; h5-index 59.0

The development of medical imaging AI systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision-making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to Covid-19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID-19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID-19 to quickly understand the basics of several explainable AI techniques and assist in selection of an approach that is both appropriate and effective for a given scenario. This article is protected by copyright. All rights reserved.

Fuhrman Jordan D, Gorre Naveena, Hu Qiyuan, Li Hui, El Naqa Issam, Giger Maryellen L

2021-Nov-18

AI, COVID-19, deep learning, explainability, interpretability

Radiology Radiology

Vaccine Development in the Time of COVID-19: The Relevance of the Risklick AI to Assist in Risk Assessment and Optimize Performance.

In Frontiers in digital health

The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration of vaccine development worldwide. Rapid vaccine development poses numerous risks for each category of vaccine technology. By using the Risklick artificial intelligence (AI), we estimated the risks associated with all types of COVID-19 vaccine during the early phase of vaccine development. We then performed a postmortem analysis of the probability and the impact matrix calculations by comparing the 2020 prognosis to the contemporary situation. We used the Risklick AI to evaluate the risks and their incidence associated with vaccine development in the early stage of the COVID-19 pandemic. Our analysis revealed the diversity of risks among vaccine technologies currently used by pharmaceutical companies providing vaccines. This analysis highlighted the current and future potential pitfalls connected to vaccine production during the COVID-19 pandemic. Hence, the Risklick AI appears as an essential tool in vaccine development for the treatment of COVID-19 in order to formally anticipate the risks, and increases the overall performance from the production to the distribution of the vaccines. The Risklick AI could, therefore, be extended to other fields of research and development and represent a novel opportunity in the calculation of production-associated risks.

Haas Quentin, Borisov Nikolay, Alvarez David Vicente, Ferdowsi Sohrab, von Mayenn Leonhard, Teodoro Douglas, Amini Poorya

2021

COVID-19, artificial intelligence, pharmacology, risk analysis, vaccine

General General

Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images.

In Computational and mathematical methods in medicine

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.

V J Sharmila, D Jemi Florinabel

2021

oncology Oncology

Blood Hemoglobin Substantially Modulates the Impact of Gender, Morbid Obesity, and Hyperglycemia on COVID-19 Death Risk: A Multicenter Study in Italy and Spain.

In Frontiers in endocrinology ; h5-index 55.0

Background : Hyperglycemia and obesity are associated with a worse prognosis in subjects with COVID-19 independently. Their interaction as well as the potential modulating effects of additional confounding factors is poorly known. Therefore, we aimed to identify and evaluate confounding factors affecting the prognostic value of obesity and hyperglycemia in relation to mortality and admission to the intensive care unit (ICU) due to COVID-19.

Methods : Consecutive patients admitted in two Hospitals from Italy (Bologna and Rome) and three from Spain (Barcelona and Girona) as well as subjects from Primary Health Care centers. Mortality from COVID-19 and risk for ICU admission were evaluated using logistic regression analyses and machine learning (ML) algorithms.

Results : As expected, among 3,065 consecutive patients, both obesity and hyperglycemia were independent predictors of ICU admission. A ML variable selection strategy confirmed these results and identified hyperglycemia, blood hemoglobin and serum bilirubin associated with increased mortality risk. In subjects with blood hemoglobin levels above the median, hyperglycemic and morbidly obese subjects had increased mortality risk than normoglycemic individuals or non-obese subjects. However, no differences were observed among individuals with hemoglobin levels below the median. This was particularly evident in men: those with severe hyperglycemia and hemoglobin concentrations above the median had 30 times increased mortality risk compared with men without hyperglycemia. Importantly, the protective effect of female sex was lost in subjects with increased hemoglobin levels.

Conclusions : Blood hemoglobin substantially modulates the influence of hyperglycemia on increased mortality risk in patients with COVID-19. Monitoring hemoglobin concentrations seem of utmost importance in the clinical settings to help clinicians in the identification of patients at increased death risk.

Mayneris-Perxachs Jordi, Russo Maria Francesca, Ramos Rafel, de Hollanda Ana, Arxé Arola Armengou, Rottoli Matteo, Arnoriaga-Rodríguez María, Comas-Cufí Marc, Bartoletti Michele, Verrastro Ornella, Gudiol Carlota, Fages Ester, Giménez Marga, Gil Ariadna de Genover, Bernante Paolo, Tinahones Francisco, Carratalà Jordi, Pagotto Uberto, Hernández-Aguado Ildefonso, Fernández-Aranda Fernando, Meira Fernanda, Castro Guardiola Antoni, Mingrone Geltrude, Fernández-Real José Manuel

2021

COVID-19, epidemiology, hemoglobin, hyperglycemia, machine learning, mortality, obesity

General General

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor.

In Journal of chemical information and modeling

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Clyde Austin, Galanie Stephanie, Kneller Daniel W, Ma Heng, Babuji Yadu, Blaiszik Ben, Brace Alexander, Brettin Thomas, Chard Kyle, Chard Ryan, Coates Leighton, Foster Ian, Hauner Darin, Kertesz Vlimos, Kumar Neeraj, Lee Hyungro, Li Zhuozhao, Merzky Andre, Schmidt Jurgen G, Tan Li, Titov Mikhail, Trifan Anda, Turilli Matteo, Van Dam Hubertus, Chennubhotla Srinivas C, Jha Shantenu, Kovalevsky Andrey, Ramanathan Arvind, Head Martha S, Stevens Rick

2021-Nov-18

Internal Medicine Internal Medicine

Digital health and artificial intelligence in kidney research: a report from the 2020 Kidney Disease Clinical Trialists (KDCT) meeting.

In Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association

The exponential growth in digital technology coupled with the global COVID-19 pandemic is driving a profound change in the delivery of medical care and research conduct. The growing availability of electronic monitoring, electronic health records, smartphones and other devices, and access to ever greater computational power, provides new opportunities, but also new challenges. Artificial intelligence (AI) exemplifies the potential of this digital revolution, which also includes other tools such as mobile health (mHealth) services and wearables. Despite digital technology becoming commonplace, its use in medicine and medical research is still in its infancy, with many clinicians and researchers having limited experience with such tools in their usual practice. This paper, derived from the 'Digital Health and Artificial Intelligence' session of the Kidney Disease Clinical Trialists virtual workshop held in September 2020, aims to illustrate the breadth of applications to which digital tools and AI can be applied in clinical medicine and research. It highlights several innovative projects incorporating digital technology that range from streamlining medical care of those with acute kidney injury to the use of AI to navigate the vast genomic and proteomic data gathered in kidney disease. Important considerations relating to any new digital health project are presented, with a view to encouraging the further evolution and refinement of these new tools in a manner that fosters collaboration and the generation of robust evidence.

Yi Tae Won, Laing Chris, Kretzler Matthias, Nkulikiyinka Richard, Legrand Matthieu, Jardine Meg, Rossignol Patrick, Smyth Brendan

2021-Nov-13

artificial intelligence, clinical trial, digital health, machine learning, prediction model

General General

Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X.

In AI and ethics

Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

Radanliev Petar, De Roure David, Maple Carsten, Ani Uchenna

2021-Oct-19

Artificial intelligence, Covid-19, Disease X, Edge devices, Healthcare systems, Internet-of-things (IoT)

General General

Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021.

In AI and ethics

In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers' workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd's research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

Chaudhry Muhammad Ali, Kazim Emre

2021-Jul-07

Artificial Intelligence, Artificial Intelligence in Education (AIEd), Education, Ethical AI, Fairness, Intelligent Tutoring Systems (ITS), Learning science, Machine learning

General General

The ethical use of high-performance computing and artificial intelligence: fighting COVID-19 at Barcelona Supercomputing Center.

In AI and ethics

The COVID-19 pandemic has created an extraordinary medical, economic and humanitarian emergency. Artificial intelligence, in combination with other digital technologies, is being used as a tool to support the fight against the viral pandemic that has affected the entire world since the beginning of 2020. Barcelona Supercomputing Center collaborates in the battle against the coronavirus in different areas: the application of bioinformatics for the research on the virus and its possible treatments, the use of artificial intelligence, natural language processing and big data techniques to analyse the spread and impact of the pandemic, and the use of the MareNostrum 4 supercomputer to enable massive analysis on COVID-19 data. Many of these activities have included the use of personal and sensitive data of citizens, which, even during a pandemic, should be treated and handled with care. In this work we discuss our approach based on an ethical, transparent and fair use of this information, an approach aligned with the guidelines proposed by the European Union.

Cortés Ulises, Cortés Atia, Garcia-Gasulla Dario, Pérez-Arnal Raquel, Álvarez-Napagao Sergio, Àlvarez Enric

2021-May-06

Artificial intelligence, COVID-19, Data protection, Ethics, High-performance computing, Machine learning

General General

Mapping value sensitive design onto AI for social good principles.

In AI and ethics

Value sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that 'disembody' the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

Umbrello Steven, van de Poel Ibo

2021-Feb-01

AI4SG, Artificial intelligence, COVID-19, Sustainable development goals, VSD, Value sensitive design

General General

An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information.

In Computational intelligence and neuroscience

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.

Alouffi Bader, Alharbi Abdullah, Sahal Radhya, Saleh Hager

2021

General General

COVID-19 pandemic as a trigger for the acceleration of the cybernetic revolution, transition from e-government to e-state, and change in social relations.

In Technological forecasting and social change

Among many influences that the pandemic has and will have on society and the World System as a whole, one of the most important is the acceleration of the start of a new technological wave and a new technological paradigm in the near future. This impact is determined by the growing need for the development of a number of areas in medicine, bio- and nanotechnology, artificial intelligence and others, which we denote as "MANBRIC convergence". It is shown that the experience of dealing with the COVID-19 pandemic has confirmed that the final phase of the Cybernetic Revolution will begin in the 2030s at the intersection of a number of medical, bio, digital and several other technologies, with medical needs as an integrating link. Among the multitude of self-regulating systems in the economy and life (which, in our opinion, will flourish during the Cybernetic Revolution) socio-technical self-regulating systems (SSSs) will play a special role. Thus, COVID-19 becomes a powerful impetus not only in terms of accelerating technological development and approaching the final phase of the Cybernetic Revolution, but also in changing sociopolitical (and socio-administrative) relations in the forthcoming decades.

Grinin Leonid, Grinin Anton, Korotayev Andrey

2021-Nov-12

AI, Biotechnology, COVID-19, Cybernetic revolution, E-government, E-state, Final phase, Self-regulating socio-technical systems, Vaccines

Public Health Public Health

The role of good governance in the race for global vaccination during the COVID-19 pandemic.

In Scientific reports ; h5-index 158.0

Governments have developed and implemented various policies and interventions to fight the COVID-19 pandemic. COVID-19 vaccines are now being produced and distributed globally. This study investigated the role of good governance and government effectiveness indicators in the acquisition and administration of COVID-19 vaccines at the population level. Data on six World Bank good governance indicators for 172 countries for 2019 and machine-learning methods (K-Means Method and Principal Component Analysis) were used to cluster countries based on these indicators and COVID-19 vaccination rates. XGBoost was used to classify countries based on their vaccination status and identify the relative contribution of each governance indicator to the vaccination rollout in each country. Countries with the highest COVID-19 vaccination rates (e.g., Israel, United Arab Emirates, United States) also have higher effective governance indicators. Regulatory Quality is the most important indicator in predicting COVID-19 vaccination status in a country, followed by Voice and Accountability, and Government Effectiveness. Our findings suggest that coordinated global efforts led by the World Health Organization and wealthier nations may be necessary to assist in the supply and distribution of vaccines to those countries that have less effective governance.

Tatar Moosa, Faraji Mohammad Reza, Montazeri Shoorekchali Jalal, Pagán José A, Wilson Fernando A

2021-Nov-17

General General

Esophageal virtual disease landscape using mechanics-informed machine learning

ArXiv Preprint

The pathogenesis of esophageal disorders is related to the esophageal wall mechanics. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map the esophageal wall mechanics-based parameters onto physiological and pathophysiological conditions corresponding to altered bolus transit and supraphysiologic IBP. In this work, we present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of the various esophageal disorders and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called endoscopic functional lumen imaging probe (EndoFLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal walls. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder (VAE) that generates a latent space and a side network that predicts mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and form clusters corresponding to the various esophageal disorders. The VDL not only distinguishes different disorders but can also be used to predict disease progression in time. Finally, we also demonstrate the clinical applicability of this framework for estimating the effectiveness of a treatment and track patient condition after a treatment.

Sourav Halder, Jun Yamasaki, Shashank Acharya, Wenjun Kou, Guy Elisha, Dustin A. Carlson, Peter J. Kahrilas, John E. Pandolfino, Neelesh A. Patankar

2021-11-19

General General

The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan.

In PloS one ; h5-index 176.0

The province of Sindh reported the first COVID-19 case in Pakistan on 26th February 2020. The Government of Sindh has employed numerous control measures to limit its spread. However, for low-and middle-income countries such as Pakistan, the management protocols for controlling a pandemic are not always as definitive as they would be in other developed nations. Given the dire socio-economic conditions of Sindh, continuation of province-wise lockdowns may inadvertently cause a potential economic breakdown. By using a data driven SEIR modelling framework, this paper describes the evolution of the epidemic projections because of government control measures. The data from reported COVID-19 prevalence and google mobility is used to parameterize the model at different time points. These time points correspond to the government's call for advice on the prerequisite actions required to curtail the spread of COVID-19 in Sindh. Our model predicted the epidemic peak to occur by 18th June 2020 with approximately 3500 reported cases at that peak, this projection correlated with the actual recorded peak during the first wave of the disease in Sindh. The impact of the governmental control actions and religious ceremonies on the epidemic profile during this first wave of COVID-19 are clearly reflected in the model outcomes through variations in the epidemic peaks. We also report these variations by displaying the trajectory of the epidemics had the control measures been guided differently; the epidemic peak may have occurred as early as the end of May 2020 with approximately 5000 reported cases per day had there been no control measures and as late as August 2020 with only around 2000 cases at the peak had the lockdown continued, nearly flattening the epidemic curve.

Usmani Bilal Ahmed, Ali Mustafain, Hasan Muhammad Abul, Siddiqui Amna Rehana, Siddiqi Sameen, Lim Aaron Guanliang, Qazi Saad Ahmed

2021

Public Health Public Health

Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

In PloS one ; h5-index 176.0

Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.

Rusu Andrei C, Emonet Rémi, Farrahi Katayoun

2021

General General

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution.

In IEEE access : practical innovations, open solutions

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.

Ndiaye Musa, Oyewobi Stephen S, Abu-Mahfouz Adnan M, Hancke Gerhard P, Kurien Anish M, Djouani Karim

2020

Artificial intelligence, COVID-19, big data, data sharing, internet of things, pandemic management

Public Health Public Health

Efficient Social Distancing during the COVID-19 Pandemic: Integrating Economic and Public Health Considerations.

In European journal of operational research

Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.

Chen Kexin, Pun Chi Seng, Wong Hoi Ying

2021-Nov-11

Deep Learning, Economic Modeling, Google Mobility Indices, OR in Health Services, Stochastic Controls, Stochastic SIRD Model

General General

ArchABM: An agent-based simulator of human interaction with the built environment. C O 2 and viral load analysis for indoor air quality.

In Building and environment

Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building's indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost- and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide ( C O 2 ) and virus quanta concentrations in each room and inhaled C O 2 and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.

Martinez Iñigo, Bruse Jan L, Florez-Tapia Ane M, Viles Elisabeth, Olaizola Igor G

2021-Nov-10

Aerosol model, Agent-based modeling, Building design, Building ventilation, Indoor air quality, Simulation

General General

COVID-19 and hospitality 5.0: Redefining hospitality operations.

In International journal of hospitality management

The sudden outbreak of COVID-19 has severely affected the global hospitality industry. The hygiene and cleanliness of hotels has become the focal point in the recovery plan during COVID-19. This study investigates the effects of past disasters on the global hospitality industry, and how the industry responded to them. Since past pandemics and epidemics identified hygiene and cleanliness as an important factor, this study further explores the role of technology in ensuring hygiene and cleanliness. Hence, this study further examines the scalability of Industry 5.0 design principles into the hospitality context, leading to Hospitality 5.0 to improve operational efficiency. The study further delineates how Hospitality 5.0 technologies can ensure hygiene and cleanliness in various touchpoints in customer's journey. This study serves as a foundation to understand how synergy between humans and machines can be achieved through Hospitality 5.0. The theoretical and practical implications are discussed.

Pillai Souji Gopalakrishna, Haldorai Kavitha, Seo Won Seok, Kim Woo Gon

2021-Apr

Artificial intelligence, Automation, COVID-19, Customer journey, Hospitality 5.0, Hygiene and cleanliness, Industry 5.0, Mobile technology, Robots, Virtual/augmented reality

General General

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic.

In International journal of hospitality management

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers' needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers' opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.

Luo Yi, Xu Xiaowei

2021-Apr

COVID-19, Deep learning, Online reviews, Restaurants, Sentiment analysis

General General

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments.

In Annals of operations research

Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.

Chen Jacky, Lim Chee Peng, Tan Kim Hua, Govindan Kannan, Kumar Ajay

2021-Nov-11

Artificial Intelligence, Asset management, Decision support, Pandemic preparedness, Predictive maintenance, Small and medium enterprises

General General

Local inequalities of the COVID-19 crisis.

In Regional science and urban economics

This paper assesses the pandemic's impact on Italian local economies with the newly developed machine learning control method for counterfactual building. Our results document that the economic effects of the COVID-19 shock vary dramatically across the Italian territory and are spatially uncorrelated with the epidemiological pattern of the first wave. The largest employment losses occurred in areas characterized by high exposure to social aggregation risks and pre-existing labor market fragilities. Lastly, we show that the hotspots of the COVID-19 crisis do not overlap with those of the Great Recession. These findings call for a place-based policy response to address the uneven economic geography of the pandemic.

Cerqua Augusto, Letta Marco

2021-Nov-12

COVID-19, Counterfactual approach, Impact evaluation, Italy, Local labor markets, Machine learning

General General

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example.

In Translational psychiatry ; h5-index 60.0

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Imami Ali S, McCullumsmith Robert E, O’Donovan Sinead M

2021-Nov-16

General General

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.

In Briefings in bioinformatics

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.

Napolitano Francesco, Xu Xiaopeng, Gao Xin

2021-Nov-11

SARS-CoV-2, epidemiology, genomics, imaging, machine learning, pharmacology

General General

DeepKG: An End-to-End Deep Learning-Based Workflow for Biomedical Knowledge Graph Extraction, Optimization and Applications.

In Bioinformatics (Oxford, England)

SUMMARY : DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research, etc. To improve the performance of DeepKG, a cascaded hybrid information extraction framework (CHIEF) is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144,900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7,980 entities and 43,760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform.

AVAILABILITY : Free to all users: http://covidkg.ai/.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Li Zongren, Zhong Qin, Yang Jing, Duan Yongjie, Wang Wenjun, Wu Chengkun, He Kunlun

2021-Nov-11

General General

Detection and Spatial Correlation Analysis of Infectious Diseases Using Wireless Body Area Network Under Imperfect Wireless Channel.

In Big data

The biosensors on a human body form a wireless body area network (WBAN) that can examine various physiological parameters, such as body temperature, electrooculography, electromyography, electroencephalography, and electrocardiography. Deep learning can use health information from the embedded sensors on the human body that can help monitoring diseases and medical disorders, including breathing issues and fever. In the context of communication, the links between the sensors are influenced by fading due to diffraction, reflection, shadowing by the body, clothes, body movement, and the surrounding environment. Hence, the channel between sensors and the central unit (CU), which collects data from sensors, is practically imperfect. Therefore, in this article, we propose a deep learning-based COVID-19 detection scheme using a WBAN setup in the presence of an imperfect channel between the sensors and the CU. Moreover, we also analyze the impact of correlation on WBAN by considering the imperfect channel. Our proposed algorithm shows promising results for real-time monitoring of COVID-19 patients.

Bhatti Dost Muhammad Saqib, Khalil Ruhul Amin, Saeed Nasir, Nam Haewoon

2021-Nov-16

IoT, health care, imperfect channels and correlation, sleeping disorder

Public Health Public Health

A Particle-Based COVID-19 Simulator With Contact Tracing and Testing.

In IEEE open journal of engineering in medicine and biology

Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.

Kuzdeuov Askat, Karabay Aknur, Baimukashev Daulet, Ibragimov Bauyrzhan, Varol Huseyin Atakan

2021

COVID-19, contact tracing, epidemic simulator, particle-based simulation, random testing

General General

Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity.

In IEEE access : practical innovations, open solutions

Due to the increase in the number of patients who died as a result of the SARS-CoV-2 virus around the world, researchers are working tirelessly to find technological solutions to help doctors in their daily work. Fast and accurate Artificial Intelligence (AI) techniques are needed to assist doctors in their decisions to predict the severity and mortality risk of a patient. Early prediction of patient severity would help in saving hospital resources and decrease the continual death of patients by providing early medication actions. Currently, X-ray images are used as early symptoms in detecting COVID-19 patients. Therefore, in this research, a prediction model has been built to predict different levels of severity risks for the COVID-19 patient based on X-ray images by applying machine learning techniques. To build the proposed model, CheXNet deep pre-trained model and hybrid handcrafted techniques were applied to extract features, two different methods: Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were integrated to select the most important features, and then, six machine learning techniques were applied. For handcrafted features, the experiments proved that merging the features that have been selected by PCA and RFE together (PCA + RFE) achieved the best results with all classifiers compared with using all features or using the features selected by PCA or RFE individually. The XGBoost classifier achieved the best performance with the merged (PCA + RFE) features, where it accomplished 97% accuracy, 98% precision, 95% recall, 96% f1-score and 100% roc-auc. Also, SVM carried out the same results with some minor differences, but overall it was a good performance where it accomplished 97% accuracy, 96% precision, 95% recall, 95% f1-score and 99% roc-auc. On the other hand, for pre-trained CheXNet features, Extra Tree and SVM classifiers with RFE achieved 99.6% for all measures.

Sayed Safynaz Abdel-Fattah, Elkorany Abeer Mohamed, Sayed Mohammad Sabah

2021

COVID-19, Chest X-rays, deep learning, handcrafted techniques, machine learning, mortality prediction, severity prediction

General General

Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters.

In IEEE access : practical innovations, open solutions

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.

Rahman Tawsifur, Khandakar Amith, Hoque Md Enamul, Ibtehaz Nabil, Kashem Saad Bin, Masud Reehum, Shampa Lutfunnahar, Hasan Mohammad Mehedi, Islam Mohammad Tariqul, Al-Maadeed Somaya, Zughaier Susu M, Badran Saif, Doi Suhail A R, Chowdhury Muhammad E H

2021

COVID-19, Complete blood count, early prediction of mortality risk, machine learning, prognostic model

General General

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.

In IEEE access : practical innovations, open solutions

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.

Alqudaihi Kawther S, Aslam Nida, Khan Irfan Ullah, Almuhaideb Abdullah M, Alsunaidi Shikah J, Ibrahim Nehad M Abdel Rahman, Alhaidari Fahd A, Shaikh Fatema S, Alsenbel Yasmine M, Alalharith Dima M, Alharthi Hajar M, Alghamdi Wejdan M, Alshahrani Mohammed S

2021

2019 novel coronavirus disease (Covid-19), Artificial intelligence (AI), cough detection, cough-based diagnosis, respiratory illness diagnosis

Public Health Public Health

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.

In IEEE access : practical innovations, open solutions

The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Rahman Md Mokhlesur, Paul Kamal Chandra, Hossain Md Amjad, Ali G G Md Nawaz, Rahman Md Shahinoor, Thill Jean-Claude

2021

COVID-19, air quality, coronavirus, human mobility, machine learning, pandemic, public health, review, spatio-temporal analysis

General General

An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study.

In IEEE access : practical innovations, open solutions

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.

Wu Peiliang, Ye Hua, Cai Xueding, Li Chengye, Li Shimin, Chen Mengxiang, Wang Mingjing, Heidari Ali Asghar, Chen Mayun, Li Jifa, Chen Huiling, Huang Xiaoying, Wang Liangxing

2021

COVID-19, coronavirus, disease diagnosis, feature selection, slime mould algorithm, support vector machine

General General

ANN Assisted-IoT Enabled COVID-19 Patient Monitoring.

In IEEE access : practical innovations, open solutions

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

Rathee Geetanjali, Garg Sahil, Kaddoum Georges, Wu Yulei, K Jayakody Dushantha Nalin, Alamri Atif

2021

Artificial neural network, COVID 19 patients’ identification, back propagation network, multi-perceptron layer, security in healthcare

General General

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.

In Proceedings. Biological sciences

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.

Fischhoff Ilya R, Castellanos Adrian A, Rodrigues João P G L M, Varsani Arvind, Han Barbara A

2021-Nov-24

COVID-19, ecological traits, machine learning, spillback, structural modelling, zoonotic

General General

Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach.

In IEEE access : practical innovations, open solutions

Newspapers are very important for a society as they inform citizens about the events around them and how they can impact their life. Their importance becomes more crucial and indispensable in the times of health crisis such as the current COVID-19 pandemic. Since the starting of this pandemic newspapers are providing rich information to the public about various issues such as the discovery of a new strain of coronavirus, lockdown and other restrictions, government policies, and information related to the vaccine development for the same. In this scenario, analysis of emergent and widely reported topics/themes/issues and associated sentiments from various countries can help us better understand the COVID-19 pandemic. In our research, the database of more than 100,000 COVID-19 news headlines and articles were analyzed using top2vec (for topic modeling) and RoBERTa (for sentiment classification and analysis). Our topic modeling results highlighted that education, economy, US, and sports are some of the most common and widely reported themes across UK, India, Japan, South Korea. Further, our sentiment classification model achieved 90% validation accuracy and the analysis showed that the worst affected country, i.e. the UK (in our dataset) also has the highest percentage of negative sentiment.

Ghasiya Piyush, Okamura Koji

2021

COVID-19, RoBERTa, Top2Vec, machine learning, natural language processing, newspaper, sentiment analysis, topic modeling

General General

The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement.

In IEEE access : practical innovations, open solutions

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.

Cotfas Liviu-Adrian, Delcea Camelia, Roxin Ioan, Ioanas Corina, Gherai Dana Simona, Tajariol Federico

2021

COVID-19, Opinion mining, SARS-CoV-2, social media, stance classification, vaccine

General General

CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter.

In IEEE access : practical innovations, open solutions

COVID-19 has affected all peoples' lives. Though COVID-19 is on the rising, the existence of misinformation about the virus also grows in parallel. Additionally, the spread of misinformation has created confusion among people, caused disturbances in society, and even led to deaths. Social media is central to our daily lives. The Internet has become a significant source of knowledge. Owing to the widespread damage caused by fake news, it is important to build computerized systems to detect fake news. The paper proposes an updated deep neural network for identification of false news. The deep learning techniques are The Modified-LSTM (one to three layers) and The Modified GRU (one to three layers). In particular, we carry out investigations of a large dataset of tweets passing on data with respect to COVID-19. In our study, we separate the dubious claims into two categories: true and false. We compare the performance of the various algorithms in terms of prediction accuracy. The six machine learning techniques are decision trees, logistic regression, k nearest neighbors, random forests, support vector machines, and naïve Bayes (NB). The parameters of deep learning techniques are optimized using Keras-tuner. Four Benchmark datasets were used. Two feature extraction methods were used (TF-ID with N-gram) to extract essential features from the four benchmark datasets for the baseline machine learning model and word embedding feature extraction method for the proposed deep neural network methods. The results obtained with the proposed framework reveal high accuracy in detecting Fake and non-Fake tweets containing COVID-19 information. These results demonstrate significant improvement as compared to the existing state of art results of baseline machine learning models. In our approach, we classify the data into two categories: fake or nonfake. We compare the execution of the proposed approaches with Six machine learning procedures. The six machine learning procedures are Decision Tree (DT), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). The parameters of deep learning techniques are optimized using Keras-tuner. Four Benchmark datasets were used. Two feature extraction methods were used (TF-ID with N-gram) to extract essential features from the four benchmark datasets for the baseline machine learning model and word embedding feature extraction method for the proposed deep neural network methods. The results obtained with the proposed framework reveal high accuracy in detecting Fake and non-Fake tweets containing COVID-19 information. These results demonstrate significant improvement as compared to the existing state of art results of baseline machine learning models.

Abdelminaam Diaa Salama, Ismail Fatma Helmy, Taha Mohamed, Taha Ahmed, Houssein Essam H, Nabil Ayman

2021

COVID-19, Fake news, deep learning, misleading information, pandemic, social media

General General

Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey.

In IEEE access : practical innovations, open solutions

Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.

Alghamdi Hanan S, Amoudi Ghada, Elhag Salma, Saeedi Kawther, Nasser Jomanah

2021

COVID-19, Chest x-ray, coronavirus, deep learning, radiological imaging

Radiology Radiology

A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.

In IEEE access : practical innovations, open solutions

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation. To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing. The data set was labelled following the guidelines of the i2b2 2014 de-identification track. As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities. Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach.

Catelli Rosario, Gargiulo Francesco, Casola Valentina, De Pietro Giuseppe, Fujita Hamido, Esposito Massimo

2021

Clinical de-identification, Italian language, contextualized embedding, deep learning, named entity recognition

General General

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods.

In IEEE access : practical innovations, open solutions

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

Ye Hua, Wu Peiliang, Zhu Tianru, Xiao Zhongxiang, Zhang Xie, Zheng Long, Zheng Rongwei, Sun Yangjie, Zhou Weilong, Fu Qinlei, Ye Xinxin, Chen Ali, Zheng Shuang, Heidari Ali Asghar, Wang Mingjing, Zhu Jiandong, Chen Huiling, Li Jifa

2021

COVID-19, Harris hawk optimization, coronavirus, disease diagnosis, feature selection, fuzzy K-nearest neighbor

General General

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.

In IEEE access : practical innovations, open solutions

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

Awal Md Abdul, Masud Mehedi, Hossain Md Shahadat, Bulbul Abdullah Al-Mamun, Mahmud S M Hasan, Bairagi Anupam Kumar

2021

ADASYN, Bayesian optimization, COVID-19, classification, “inpatients facility data”

General General

A Multivocal Literature Review on Growing Social Engineering Based Cyber-Attacks/Threats During the COVID-19 Pandemic: Challenges and Prospective Solutions.

In IEEE access : practical innovations, open solutions

The novel coronavirus (COVID-19) pandemic has caused a considerable and long-lasting social and economic impact on the world. Along with other potential challenges across different domains, it has brought numerous cybersecurity challenges that must be tackled timely to protect victims and critical infrastructure. Social engineering-based cyber-attacks/threats are one of the major methods for creating turmoil, especially by targeting critical infrastructure, such as hospitals and healthcare services. Social engineering-based cyber-attacks are based on the use of psychological and systematic techniques to manipulate the target. The objective of this research study is to explore the state-of-the-art and state-of-the-practice social engineering-based techniques, attack methods, and platforms used for conducting such cybersecurity attacks and threats. We undertake a systematically directed Multivocal Literature Review (MLR) related to the recent upsurge in social engineering-based cyber-attacks/threats since the emergence of the COVID-19 pandemic. A total of 52 primary studies were selected from both formal and grey literature based on the established quality assessment criteria. As an outcome of this research study; we discovered that the major social engineering-based techniques used during the COVID-19 pandemic are phishing, scamming, spamming, smishing, and vishing, in combination with the most used socio-technical method: fake emails, websites, and mobile apps used as weapon platforms for conducting successful cyber-attacks. Three types of malicious software were frequently used for system and resource exploitation are; ransomware, trojans, and bots. We also emphasized the economic impact of cyber-attacks performed on different organizations and critical infrastructure in which hospitals and healthcare were on the top targeted infrastructures during the COVID-19 pandemic. Lastly, we identified the open challenges, general recommendations, and prospective solutions for future work from the researcher and practitioner communities by using the latest technology, such as artificial intelligence, blockchain, and big data analytics.

Hijji Mohammad, Alam Gulzar

2021

COVID-19, Multivocal literature review, cyber-attacks and threats, prospective solutions, security and privacy, social engineering

General General

Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach.

In IEEE access : practical innovations, open solutions

Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests and provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different methods based on a deep neural network. These are the first steps to develop an automatic COVID-19 diagnosis tool using chest X-Ray images to differentiate between controls, pneumonia, or COVID-19 groups. The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79, 500 X-Ray images compiled from different sources, including more than 8, 500 COVID-19 examples. Three different experiments following three preprocessing schemes are carried out to evaluate and compare the developed models. The aim is to evaluate how preprocessing the data affects the results and improves its explainability. Likewise, a critical analysis of different variability issues that might compromise the system and its effects is performed. With the employed methodology, a 91.5% classification accuracy is obtained, with an 87.4% average recall for the worst but most explainable experiment, which requires a previous automatic segmentation of the lung region.

Arias-Londono Julian D, Gomez-Garcia Jorge A, Moro-Velazquez Laureano, Godino-Llorente Juan I

2020

COVID-19, chest X-ray, deep learning, pneumonia, radiological imaging

General General

Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices.

In IEEE access : practical innovations, open solutions

The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.

De Moura Joaquim, Garcia Lucia Ramos, Vidal Placido Francisco Lizancos, Cruz Milena, Lopez Laura Abelairas, Lopez Eva Castro, Novo Jorge, Ortega Marcos

2020

COVID-19, Chest X-ray imaging, X-ray portable device, deep learning

General General

Investigating the use of digital health technology to monitor COVID-19 and its effects: Protocol for Covid Collab, an observational study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : The ubiquity of mobile phones and increasing use of wearable fitness trackers offers a wide-ranging window into people's health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the current COVID-19 pandemic.

OBJECTIVE : The Covid Collab study was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of COVID-19 infection and recovery through remote monitoring technologies. Additionally, we will assess the impact of the COVID-19 pandemic and associated social measures on people's behaviour, physical health, and mental well-being.

METHODS : Participants remotely enrolled on the study through the Mass Science app to donate both historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19 and mental health related surveys. Data is being recorded for the period of the pandemic, notably including pre, during and post acute infection phase. We plan to carry out analyses in several areas, covering symptomatology, risk factors, machine learning-based classification of illness, and trajectories of recovery, mental well-being, and activity.

RESULTS : Covid Collab is a crowdsourced study using remote monitoring technologies to investigate the COVID-19 pandemic. As of June 2021 there are over 17000 participants, largely from the United Kingdom, with enrolment ongoing.

CONCLUSIONS : This paper introduces a remotely enrolled crowd-sourced study recording mobile health data throughout the COVID-19 pandemic. The data collected may help investigate a variety of areas, including COVID-19 disease progression, mental wellbeing during the pandemic, and adherence of remote, digitally enrolled participants.

Stewart Callum, Ranjan Yatharth, Conde Pauline, Rashid Zulqarnain, Sankesara Heet, Bai Xi, Dobson Richard, Folarin Amos

2021-Sep-29

General General

Vaccine Design by Reverse Vaccinology and Machine Learning.

In Methods in molecular biology (Clifton, N.J.)

Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction method based on calculating and filtering different criteria of proteins. Vaxign-ML is a new Vaxign machine learning (ML) method that predicts vaccine antigens based on extreme gradient boosting with the advance of new technologies and cumulation of protective antigen data. Using a benchmark dataset, Vaxign-ML showed superior performance in comparison to existing open-source RV tools. Vaxign-ML is also implemented within the web-based Vaxign platform to support easy and intuitive access. Vaxign-ML is also available as a command-based software package for more advanced and customizable vaccine antigen prediction. Both Vaxign and Vaxign-ML have been applied to predict SARS-CoV-2 (cause of COVID-19) and Brucella vaccine antigens to demonstrate the integrative approach to analyze and select vaccine candidates using the Vaxign platform.

Ong Edison, He Yongqun

2022

Antigen, Machine learning, Reverse vaccinology, Vaccine, Vaxign, Vaxign-ML, Vaxitop

General General

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection.

In Journal of proteome research

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

Zhang Ying, Cai Xue, Ge Weigang, Wang Donglian, Zhu Guangjun, Qian Liujia, Xiang Nan, Yue Liang, Liang Shuang, Zhang Fangfei, Wang Jing, Zhou Kai, Zheng Yufen, Lin Minjie, Sun Tong, Lu Ruyue, Zhang Chao, Xu Luang, Sun Yaoting, Zhou Xiaoxu, Yu Jing, Lyu Mengge, Shen Bo, Zhu Hongguo, Xu Jiaqin, Zhu Yi, Guo Tiannan

2021-Nov-16

COVID-19, disease course monitoring, machine learning, proteomics, serum

General General

An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques.

In BioMed research international ; h5-index 102.0

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

Subramaniam Umashankar, Subashini M Monica, Almakhles Dhafer, Karthick Alagar, Manoharan S

2021

General General

Non-contact screening system based for COVID-19 on XGBoost and logistic regression.

In Computers in biology and medicine

BACKGROUND : The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary.

OBJECTIVE : We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients.

METHODS : We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects.

RESULTS : The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification.

CONCLUSION : The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

Dong Chunjiao, Qiao Yixian, Shang Chunheng, Liao Xiwen, Yuan Xiaoning, Cheng Qin, Li Yuxuan, Zhang Jianan, Wang Yunfeng, Chen Yahong, Ge Qinggang, Bao Yurong

2021-Nov-03

COVID-19, Logistic regression, Non-contact vital signs, Screening system, XGBoost

General General

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images.

In Journal of healthcare engineering

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.

Hamida Soufiane, El Gannour Oussama, Cherradi Bouchaib, Raihani Abdelhadi, Moujahid Hicham, Ouajji Hassan

2021

General General

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.

In Journal of signal processing systems

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

Teodoro Arthur A M, Silva Douglas H, Saadi Muhammad, Okey Ogobuchi D, Rosa Renata L, Otaibi Sattam Al, Rodríguez Demóstenes Z

2021-Nov-08

COVID-19; non-COVID-19, Computed tomography images, Convolutional neural networks, Machine learning, Transfer learning

General General

Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning.

In Cognitive neurodynamics

** : The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanism of actions (MoAs) based on the cell image features of 1105 drugs in the  LINCS database. As the multi-dimensional features of cell images are affected by non-experimental factors, the characteristics of similar drugs vary considerably, and it is difficult to effectively identify the MoA of drugs as there is substantial noise. By applying the supervised information theoretic metric-learning (ITML) algorithm, a linear transformation made drugs with the same MoA aggregate. By clustering drugs to communities and performing enrichment analysis, we found that transferred image features were more conducive to the recognition of drug MoAs. Image features analysis showed that different features play important roles in identifying different drug functions. Drugs that significantly affect cell survival or proliferation, such as cyclin-dependent kinase inhibitors, were more likely to be enriched in communities, whereas other drugs might be decentralized. Chloroquine and clomiphene, which block the entry of virus, were clustered into the same community, indicating that similar MoA could be reflected by the cell image. Overall, the findings of the present study laid the foundation for the discovery of MoAs of new drugs, based on image data. In addition, it provided a new method of drug repurposing for COVID-19.

Supplementary Information : The online version contains supplementary material available at 10.1007/s11571-021-09727-5.

Han Lu, Shan Guangcun, Chu Bingfeng, Wang Hongyu, Wang Zhongjian, Gao Shengqiao, Zhou Wenxia

2021-Nov-05

Cell image feature, Coronavirus, Drug repurposing, LINCS, Machine learning

General General

A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images.

In Biomedical signal processing and control

A dangerous infectious disease of the current century, the COVID-19 has apparently originated in a city in China and turned into a widespread pandemic within a short time. In this paper, a novel method has been presented for improving the screening and classification of COVID-19 patients based on their chest X-Ray (CXR) images. This method eliminates the severe dependence of the deep learning models on large datasets and the deep features extracted from them. In this approach, we have not only resolved the data limitation problem by combining the traditional data augmentation techniques with the generative adversarial networks (GANs), but also have enabled a deeper extraction of features by applying different filter banks such as the Sobel, Laplacian of Gaussian (LoG) and the Gabor filters. To verify the satisfactory performance of the proposed approach, it was applied on several deep transfer models and the results in each step were compared with each other. For training the entire models, we used 4560 CXR images of various patients with the viral, bacterial, fungal, and other diseases; 360 of these images are in the COVID-19 category and the rest belong to the non-COVID-19 diseases. According to the results, the Gabor filter bank achieves the highest growth in the values of the defined evaluation criteria and in just 45 epochs, it is able to elevate the accuracy by up to 32%. We then applied the proposed model on the DenseNet-201 model and compared its performance in terms of the detection accuracy with the performances of 10 existing COVID-19 detection techniques. Our approach was able to achieve an accuracy of 98.5% in the two-class classification procedure; which makes it a state-of-the-art method for detecting the COVID-19.

Barshooi Amir Hossein, Amirkhani Abdollah

2022-Feb

COVID-19, Classification, Data augmentation, Deep learning, Gabor, Generative adversarial network

General General

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2.

In Frontiers in microbiology

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Zhang Yang, Ye Taoyu, Xi Hui, Juhas Mario, Li Junyi

2021

SARS-CoV-2, antibiotics, antimalarial drug, database, deep learning, drug discovery, drug repurposing

General General

Contactless In-Home Monitoring of the Long-Term Respiratory and Behavioral Phenotypes in Older Adults With COVID-19: A Case Series.

In Frontiers in psychiatry

Currently, there is a limited understanding of long-term outcomes of COVID-19, and a need for in-home measurements of patients through the whole course of their disease. We study a novel approach for monitoring the long-term trajectories of respiratory and behavioral symptoms of COVID-19 patients at home. We use a sensor that analyzes the radio signals in the room to infer patients' respiration, sleep and activities in a passive and contactless manner. We report the results of continuous monitoring of three residents of an assisted living facility for 3 months, through the course of their disease and subsequent recovery. In total, we collected 4,358 measurements of gait speed, 294 nights of sleep, and 3,056 h of respiration. The data shows differences in the respiration signals between asymptomatic and symptomatic patients. Longitudinally, we note sleep and motor abnormalities that persisted for months after becoming COVID negative. Our study represents a novel phenotyping of the respiratory and behavioral trajectories of COVID recovery, and suggests that the two may be integral components of the COVID-19 syndrome. It further provides a proof-of-concept that contactless passive sensors may uniquely facilitate studying detailed longitudinal outcomes of COVID-19, particularly among older adults.

Zhang Guo, Vahia Ipsit V, Liu Yingcheng, Yang Yuzhe, May Rose, Cray Hailey V, McGrory William, Katabi Dina

2021

COVID-19, behavior, case report, contactless monitoring, long-term outcomes, older adults, phenotypes, respiration

General General

COVID-19 pandemic, predictions and control in Saudi Arabia using SIR-F and age-structured SEIR model.

In The Journal of supercomputing

COVID-19 has affected every individual physically or physiologically, leading to substantial impacts on how they perceive and respond to the pandemic's danger. Due to the lack of vaccines or effective medicines to cure the infection, an urgent control measure is required to prevent the continued spread of COVID-19. This can be achieved using advanced computing, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), cloud computing, and edge computing. To control the exponential spread of the novel virus, it is crucial for countries to contain and mitigate interventions. To prevent exponential growth, several control measures have been applied in the Kingdom of Saudi Arabia to mitigate the COVID-19 epidemic. As the pandemic has been spreading globally for more than a year, an ample amount of data is available for researchers to predict and forecast the effect of the pandemic in the near future. This article interprets the effects of COVID-19 using the Susceptible-Infected-Recovered (SIR-F) while F-stands for 'Fatal with confirmation,' age-structured SEIR (Susceptible Exposed Infectious Removed) and machine learning for smart health care and the well-being of citizens of Saudi Arabia. Additionally, it examines the different control measure scenarios produced by the modified SEIR model. The evolution of the simulation results shows that the interventions are vital to flatten the virus spread curve, which can delay the peak and decrease the fatality rate.

Durai C Anand Deva, Begum Arshiya, Jebaseeli Jemima, Sabahath Asfia

2021-Nov-10

COVID-19, Control measurements, Critical cases, Interventions, Mathematical SIR, SEIR, SIR-F

General General

COVID-19 and erosion of democracy.

In Economic modelling

The main research question of this study is about the drivers of democracy backsliding during the COVID-19 pandemic, with a special focus on the rule of law and the state of democracy just before the shock. There is growing interest in the political implications of the coronavirus pandemic, debating mostly the misuse of emergencies and violations of various norms by governments; however the links between the current democracy erosion with institutional environment remain unclear. We use a novel global dataset covering the period of the first two waves of the pandemic (January-December 2020), and apply various econometric and machine learning tools to identify institutional, economic and social factors influencing democracy. Our results are of scientific and practical importance and imply that the stronger the rule of law and the higher the level of democracy, the lower the risk of democracy backsliding in the face of the pandemic.

Lewkowicz Jacek, Woźniak Michał, Wrzesiński Michał

2022-Jan

COVID-19, Democracy backsliding, Institutional economics, Law & economics, Pandemic, Political economy, Rule of law

General General

Literature-Augmented Clinical Outcome Prediction

ArXiv Preprint

Predictive models for medical outcomes hold great promise for enhancing clinical decision-making. These models are trained on rich patient data such as clinical notes, aggregating many patient signals into an outcome prediction. However, AI-based clinical models have typically been developed in isolation from the prominent paradigm of Evidence Based Medicine (EBM), in which medical decisions are based on explicit evidence from existing literature. In this work, we introduce techniques to help bridge this gap between EBM and AI-based clinical models, and show that these methods can improve predictive accuracy. We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information, aggregates relevant papers and fuses them with internal admission notes to form outcome predictions. Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines; for in-hospital mortality, we are able to boost top-10% precision by a large margin of over 25%.

Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope

2021-11-16

General General

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.

In Computers in biology and medicine

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.

Ahamed Khabir Uddin, Islam Manowarul, Uddin Ashraf, Akhter Arnisha, Paul Bikash Kumar, Yousuf Mohammad Abu, Uddin Shahadat, Quinn Julian M W, Moni Mohammad Ali

2021-Nov-04

Convolutional neural network, Coronavirus, Deep learning, Pneumonia, Rediology, Respiratory diseases

General General

Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves.

OBJECTIVE : This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment.

METHODS : A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues.

RESULTS : Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security.

CONCLUSIONS : Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients' intention to use PHSs on P2P networks by making them safe to use.

Abdullahi Yari Imrana, Dehling Tobias, Kluge Felix, Geck Juergen, Sunyaev Ali, Eskofier Bjoern

2021-Nov-15

COVID-19 proximity trackers, attacks, decentralization, edge computing, health care, information infrastructures, mobile health, mobile phone, patient-centered, peer-to-peer, security, threats, vulnerabilities

General General

The association between interferon lambda 3 and 4 gene single-nucleotide polymorphisms and the recovery of COVID-19 patients.

In Virology journal ; h5-index 35.0

BACKGROUND : The recent pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has elevated several clinical and scientific questions. These include how host genetic factors influence the pathogenesis and disease susceptibility. Therefore, the aim of this study was to evaluate the impact of interferon lambda 3 and 4 (IFNL3/4) gene polymorphisms and clinical parameters on the resistance and susceptibility to coronavirus disease 2019 (COVID-19) infection.

METHODS : A total of 750 SARS-CoV-2 positive patients (375 survivors and 375 nonsurvivors) were included in this study. All single-nucleotide polymorphisms (SNPs) on IFNL3 (rs12979860, rs8099917, and rs12980275) and IFNL4 rs368234815 were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method.

RESULTS : In this study, a higher viral load (low PCR Ct value) was shown in nonsurvivor patients. In survivor patients, the frequency of the favorable genotypes of IFNL3/4 SNPs (rs12979860 CC, rs12980275 AA, rs8099917 TT, and rs368234815 TT/TT) was significantly higher than in nonsurvivor patients. Multivariate logistic regression analysis has shown that a higher low-density lipoprotein (LDL), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and PCR Ct value, and lower 25-hydroxyvitamin D, and also IFNL3 rs12979860 TT, IFNL3 rs8099917 GG, IFNL3 rs12980275 GG, and IFNL4 rs368234815 ∆G/∆G genotypes were associated with the severity of COVID-19 infection.

CONCLUSIONS : The results of this study proved that the severity of COVID-19 infection was associated with clinical parameters and unfavorable genotypes of IFNL3/IFNL4 SNPs. Further studies in different parts of the world are needed to show the relationship between severity of COVID-19 infection and host genetic factors.

Rahimi Pooneh, Tarharoudi Rahil, Rahimpour Alireza, Mosayebi Amroabadi Jalal, Ahmadi Iraj, Anvari Enayat, Siadat Seyed Davar, Aghasadeghi Mohammadreza, Fateh Abolfazl

2021-Nov-14

COVID-19, Interferon lambda 3, Interferon lambda 4, SARS-CoV-2, Single-nucleotide polymorphisms

General General

Federated Learning for Smart Healthcare: A Survey

ArXiv Preprint

Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang

2021-11-16

Pathology Pathology

Improving motion-mask segmentation in thoracic CT with multi-planar U-nets.

In Medical physics ; h5-index 59.0

PURPOSE : Motion-mask segmentation from thoracic CT images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, i.e., sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design.

METHODS : A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of non-small cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network.

RESULTS : The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from 53% to 79% without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from 53% to 83%.

CONCLUSION : With 5-second processing time on a mid-range GPU and success rates around 80%, the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available. This article is protected by copyright. All rights reserved.

Penarrubia Ludmilla, Pinon Nicolas, Roux Emmanuel, Serrano Eduardo Enrique Dávila, Richard Jean-Christophe, Orkisz Maciej, Sarrut David

2021-Nov-14

deep learning, segmentation, thoracic CT

General General

Machine Learning and Network Medicine approaches for Drug Repositioning for COVID-19.

In Patterns (New York, N.Y.)

We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorisation algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 Repositioning Explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.

de Siqueira Santos Suzana, Torres Mateo, Galeano Diego, Sánchez María Del Mar, Cernuzzi Luca, Paccanaro Alberto

2021-Nov-09

COVID-19, SARS-CoV-2, drug repurposing, graph visualization, kernels on graphs, network medicine, non-negative matrix factorisation

General General

Pandemic Analytics: How Countries are Leveraging Big Data Analytics and Artificial Intelligence to Fight COVID-19?

In SN computer science

Emergence of coronavirus in December 2019 and its spread across the world in the following months has made it a global health concern. The uncertainty about its evolution, transmission and effect of SARS-CoV-2, has left the countries and their governments in a worrisome state. Ambiguity about the strategies that would work towards mitigating the impact of virus has prompted them to use data-driven methods. Several countries started applying big data and advanced analytics technology for management of the crisis. This study aims to understand how different nations have employed analytics to deal with COVID-19. This paper reviews various strategies employed by different governments and organizations across nations that use advanced analytics to tackle pandemic. In the current emergency of corona virus, there have been several measures that organizations have taken to mitigate its impact, thanks to the evolution of computing technology. Big data and analytical tools provide various solutions like detection of existing COVID-19 cases, prediction of future outbreak, anticipation of potential preventive and therapeutic agents, and assistance in informed decision-making. This review discusses the big data analytics and artificial intelligence approaches that policy makers, researchers, epidemiologists and private organizations have adopted. By examining the different ways and areas where data analytics has been utilized, this study provides the other nations with the progressive scheme to address the pandemic.

Mehta Nishita, Shukla Sharvari

2022

Advanced analytics, Artificial intelligence, Big data, COVID-19, Coronavirus, Pandemic analytics

General General

Forecasting COVID-19 infections in the Arabian Gulf region.

In Modeling earth systems and environment

In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method. The results also indicate that the poorer forecast performance of long short-term memory neural networks occurs in particular when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes. One important result of this study is the possible relationship between data complexity and forecast accuracy with different models as suggested in the entropy analysis. It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data.

Khedhiri Sami

2021-Nov-06

COVID-19, Data complexity, Long short-term memory network, State space model

General General

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

In Frontiers in public health

Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions. Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI). Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

Wang Shui-Hua, Zhu Ziquan, Zhang Yu-Dong

2021

Grad-CAM, PatchShuffle, convolutional neural network, data augmentation, deep learning, stochastic pooling

Public Health Public Health

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union.

In Frontiers in public health

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.

Šušteršič Tijana, Blagojević Andjela, Cvetković Danijela, Cvetković Aleksandar, Lorencin Ivan, Šegota Sandi Baressi, Milovanović Dragan, Baskić Dejan, Car Zlatan, Filipović Nenad

2021

COVID-19, LSTM model, SEIRD model, disease spread modeling, epidemiological model

General General

Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space.

In Complex & intelligent systems

The COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations.

Gong Yunhong, Sun Yanan, Peng Dezhong, Chen Peng, Yan Zhongtai, Yang Ke

2021-Sep-06

Batch normalization, COVID-19, Evolutionary algorithms, Variable-length chromosomes

General General

A systematic review on AI/ML approaches against COVID-19 outbreak.

In Complex & intelligent systems

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

Dogan Onur, Tiwari Sanju, Jabbar M A, Guggari Shankru

2021-Jul-05

Artificial intelligence, COVID-19, Machine learning, Pandemic, Research analysis, Systematic review

General General

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images.

In Complex & intelligent systems

COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

Shankar K, Perumal Eswaran

2020-Nov-12

COVID-19, Classification, Convolutional neural network, Feature extraction, Fusion model, Preprocessing

General General

Thinking on the informatization development of China's healthcare system in the post-COVID-19 era.

In Intelligent medicine

With the application of Internet of Things, big data, cloud computing, artificial intelligence, and other cutting-edge technologies, China's medical informatization is developing rapidly. In this paper, we summaried the role of information technology in healthcare sector's battle against the coronavirus disease 2019 (COVID-19) from the perspectives of early warning and monitoring, screening and diagnosis, medical treatment and scientific research, analyzes the bottlenecks of the development of information technology in the post-COVID-19 era, and puts forward feasible suggestions for further promoting the construction of medical informatization from the perspectives of sharing, convenience, and safety.

Zhang Ming, Dai Danyun, Hou Siliang, Liu Wei, Gao Feng, Xu Dong, Hu Yu

2021-May

Coronavirus disease 2019, Healthcare system, Informatization

Public Health Public Health

Results of the COVID-19 mental health international for the general population (COMET-G) study.

In European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology

INTRODUCTION : There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study.

MATERIAL AND METHODS : During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS : Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS : Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.

CONCLUSIONS : The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

Fountoulakis Konstantinos N, Karakatsoulis Grigorios, Abraham Seri, Adorjan Kristina, Ahmed Helal Uddin, Alarcón Renato D, Arai Kiyomi, Auwal Sani Salihu, Berk Michael, Bjedov Sarah, Bobes Julio, Bobes-Bascaran Teresa, Bourgin-Duchesnay Julie, Bredicean Cristina Ana, Bukelskis Laurynas, Burkadze Akaki, Abud Indira Indiana Cabrera, Castilla-Puentes Ruby, Cetkovich Marcelo, Colon-Rivera Hector, Corral Ricardo, Cortez-Vergara Carla, Crepin Piirika, De Berardis Domenico, Zamora Delgado Sergio, De Lucena David, De Sousa Avinash, Stefano Ramona Di, Dodd Seetal, Elek Livia Priyanka, Elissa Anna, Erdelyi-Hamza Berta, Erzin Gamze, Etchevers Martin J, Falkai Peter, Farcas Adriana, Fedotov Ilya, Filatova Viktoriia, Fountoulakis Nikolaos K, Frankova Iryna, Franza Francesco, Frias Pedro, Galako Tatiana, Garay Cristian J, Garcia-Álvarez Leticia, García-Portilla Maria Paz, Gonda Xenia, Gondek Tomasz M, González Daniela Morera, Gould Hilary, Grandinetti Paolo, Grau Arturo, Groudeva Violeta, Hagin Michal, Harada Takayuki, Hasan Tasdik M, Hashim Nurul Azreen, Hilbig Jan, Hossain Sahadat, Iakimova Rossitza, Ibrahim Mona, Iftene Felicia, Ignatenko Yulia, Irarrazaval Matias, Ismail Zaliha, Ismayilova Jamila, Jakobs Asaf, Jakovljević Miro, Jakšić Nenad, Javed Afzal, Kafali Helin Yilmaz, Karia Sagar, Kazakova Olga, Khalifa Doaa, Khaustova Olena, Koh Steve, Kopishinskaia Svetlana, Kosenko Korneliia, Koupidis Sotirios A, Kovacs Illes, Kulig Barbara, Lalljee Alisha, Liewig Justine, Majid Abdul, Malashonkova Evgeniia, Malik Khamelia, Malik Najma Iqbal, Mammadzada Gulay, Mandalia Bilvesh, Marazziti Donatella, Marčinko Darko, Martinez Stephanie, Matiekus Eimantas, Mejia Gabriela, Memon Roha Saeed, Martínez Xarah Elenne Meza, Mickevičiūtė Dalia, Milev Roumen, Mohammed Muftau, Molina-López Alejandro, Morozov Petr, Muhammad Nuru Suleiman, Mustač Filip, Naor Mika S, Nassieb Amira, Navickas Alvydas, Okasha Tarek, Pandova Milena, Panfil Anca-Livia, Panteleeva Liliya, Papava Ion, Patsali Mikaella E, Pavlichenko Alexey, Pejuskovic Bojana, Pinto Da Costa Mariana, Popkov Mikhail, Popovic Dina, Raduan Nor Jannah Nasution, Ramírez Francisca Vargas, Rancans Elmars, Razali Salmi, Rebok Federico, Rewekant Anna, Flores Elena Ninoska Reyes, Rivera-Encinas María Teresa, Saiz Pilar, de Carmona Manuel Sánchez, Martínez David Saucedo, Saw Jo Anne, Saygili Görkem, Schneidereit Patricia, Shah Bhumika, Shirasaka Tomohiro, Silagadze Ketevan, Sitanggang Satti, Skugarevsky Oleg, Spikina Anna, Mahalingappa Sridevi Sira, Stoyanova Maria, Szczegielniak Anna, Tamasan Simona Claudia, Tavormina Giuseppe, Tavormina Maurilio Giuseppe Maria, Theodorakis Pavlos N, Tohen Mauricio, Tsapakis Eva Maria, Tukhvatullina Dina, Ullah Irfan, Vaidya Ratnaraj, Vega-Dienstmaier Johann M, Vrublevska Jelena, Vukovic Olivera, Vysotska Olga, Widiasih Natalia, Yashikhina Anna, Prezerakos Panagiotis E, Smirnova Daria

2021-Oct-15

Anxiety, COVID-19, Conspiracy theories, Depression, Mental disorders, Mental health, Psychiatry, Suicidality

General General

Chest computed tomography in the diagnosis of COVID-19 in patients with false negative RT-PCR.

In Einstein (Sao Paulo, Brazil)

OBJECTIVE : To evaluate the role of chest computed tomography in patients with COVID-19 who presented initial negative result in reverse transcriptase-polymerase chain reaction (RT-PCR).

METHODS : A single-center, retrospective study that evaluated 39 patients with negative RT-PCR for COVID-19, who underwent chest computed tomography and had a final clinical or serological diagnosis of COVID-19. The visual tomographic classification was evaluated according to the Consensus of the Radiological Society of North America and software developed with artificial intelligence for automatic detection of findings and chance estimation of COVID-19.

RESULTS : In the visual tomographic analysis, only one of them (3%) presented computed tomography classified as negative, 69% were classified as typical and 28% as indeterminate. In the evaluation using the software, only four (about 10%) had a probability of COVID-19 <25%.

CONCLUSION : Computed tomography can play an important role in management of suspected cases of COVID-19 with initial negative results in RT-PCR, especially considering those patients outside the ideal window for sample collection for RT-PCR.

Fonseca Eduardo Kaiser Ururahy Nunes, Ferreira Lorena Carneiro, Loureiro Bruna Melo Coelho, Strabelli Daniel Giunchetti, Farias Lucas de Pádua Gomes de, Queiroz Gabriel Abrantes de, Garcia José Vitor Rassi, Teixeira Renato de Freitas, Gama Victor Arcanjo Almeida, Chate Rodrigo Caruso, Assunção Júnior Antonildes Nascimento, Sawamura Márcio Valente Yamada, Nomura Cesar Higa

2021

General General

Prediction of Epidemic Disease Dynamics on the Infection Risk Using Machine Learning Algorithms.

In SN computer science

Accurate forecast for the public is more important to many organisations especially health organisations on infectious disease dynamics that prevails in prevention or decrease in disease transmission. With multiple data availability in healthcare and medical sectors, precise analysis of such data helps in disease detection and better health care of all individuals. With the existing computational power and big data, there are more chances in predicting an epidemic outbreak. The basic idea of this paper is to analyse and predict the spread of epidemic diseases mainly on the focus on infection risk. A machine learning model using Multivariate Logistic Regression on Modified SEIR has to be built to predict the epidemic disease dynamics on the infection risk.

Palaniappan Shanthi, V Ragavi, David Beaulah, S Pathur Nisha

2022

COVID-19, Epidemics, Mathematical model, Pandemic

General General

Validated tool for early prediction of intensive care unit admission in COVID-19 patients.

In World journal of clinical cases

BACKGROUND : The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.

AIM : To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission.

METHODS : The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models.

RESULTS : There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.

CONCLUSION : Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

Huang Hao-Fan, Liu Yong, Li Jin-Xiu, Dong Hui, Gao Shan, Huang Zheng-Yang, Fu Shou-Zhi, Yang Lu-Yu, Lu Hui-Zhi, Xia Liao-You, Cao Song, Gao Yi, Yu Xia-Xia

2021-Oct-06

COVID-19, Intensive care units, Machine learning, Prognostic predictive model, Risk stratification

General General

Hybrid-based framework for COVID-19 prediction via federated machine learning models.

In The Journal of supercomputing

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.

Kallel Ameni, Rekik Molka, Khemakhem Mahdi

2021-Nov-05

Batch/streaming data, COVID-19 pandemic, Decision-making, Federated MLaaS, Hybrid fog-cloud federation, IoT devices, Machine learning, Quantitative and qualitative evaluation, Real-time prediction

Public Health Public Health

Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020.

In BMJ open

OBJECTIVE : To rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques.

SETTING : One hundred and eighty countries' patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020.

DESIGN : Using the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries' final scores for each measure.

PRIMARY OUTCOME : The primary outcomes are the COVID-19 cases and fatality grades in 2020.

RESULTS : The United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries' preparedness.

CONCLUSION : COVID-19 fatality can be a good proxy for countries' resources and system's resilience in managing the pandemic. These findings suggest that countries' economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.

Sadeghi Banafsheh, Cheung Rex C Y, Hanbury Meagan

2021-Nov-09

COVID-19, epidemiology, public health, statistics & research methods

Public Health Public Health

Predictive Modeling of Vaccination Uptake in U.S. Counties: A Machine Learning-based Approach.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : While the COVID-19 pandemic has left an unprecedented impact globally, countries such as the United States of America have reported the most significant incidence of COVID-19 cases worldwide. Within the US, various sociodemographic factors have played an essential role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modelling strategies to inform public health officials and reduce the burden on healthcare systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the US, vaccination rates have become stagnant, necessitating predictive modelling to identify important factors impacting vaccination uptake.

OBJECTIVE : To determine the association between sociodemographic factors and vaccine uptake across counties in the US.

METHODS : Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases, such as the US Centre for Disease Control and US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data.

RESULTS : Our model predicted COVID-19 vaccination uptake across US countries with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by healthcare authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns.

CONCLUSIONS : Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rate across counties in the US and if leveraged appropriately can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.

CLINICALTRIAL :

Cheong Queena, Au-Yeung Martin, Quon Stephanie, Concepcion Katsy, Kong Jude Dzevela

2021-Nov-01

Public Health Public Health

Using infrared imaging and deep learning in fit-checking of respiratory protective devices among healthcare professionals.

In Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing

AIMS : This study aimed to investigate the application of infrared thermal imaging and adopt deep learning to detect air leakage for determining the fitness of respirators during fit-checks.

BACKGROUND : The outbreak of Covid-19 virus constitutes a public health crisis with substantial resultant morbidities and mortalities; has exerted profound impacts.

METHODS : This was a prospective observational study, employing a non-probability sampling method on a convenience sample to recruit the participants and followed the Strengthening the Reporting of Observational Studies in Epidemiology statement guidelines.

RESULTS : The use of infrared thermal imaging identified air leakage points as a disruption to the facial thermal pattern distribution at (a) front of face; (b) right lateral of the face; (c) left lateral of the face; (d) top of the facemask with the head facing down; and (e) bottom of the facemask with the head facing up. Results also indicated that artificial intelligence tools and the proliferation of deep learning have the potential to detect the location of air leakage locations.

CONCLUSION : The use of infrared thermal imaging provides evidence of the feasibility and applicability of infrared thermal imaging techniques in detecting air leakage for individuals wearing respirators.

CLINICAL RELEVANCE : The use of infrared thermal technology can serve a potential role in complement fit-checking of respiratory protective devices and offers promising practical utility in determining the fitness of respirators for nurses at the frontline to protect against the air-borne viruses.

Siah Chiew-Jiat Rosalind, Lau Siew Tiang, Tng Sian Soo, Chua Chin Heng Matthew

2021-Nov-08

air-borne disease, infrared, mask, quality, respiratory protective device, technology

General General

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.

In NPJ digital medicine

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

Richards Dylan M, Tweardy MacKenzie J, Steinhubl Steven R, Chestek David W, Hoek Terry L Vanden, Larimer Karen A, Wegerich Stephan W

2021-Nov-08

General General

Pandemics past, present, and future: Their impact on oral health care.

In Journal of the American Dental Association (1939)

BACKGROUND : Pandemics have significantly modified our societal behaviour over the millennia, and the COVID-19 pandemic is no exception.

TYPES OF ARTICLES REVIEWED : In this article, the authors review the history of pandemics, the probable reasons for their emergence, and the COVID-19 pandemic due to the severe acute respiratory syndrome virus 2 (SARS-CoV-2) and its variants, as well as its possible impact on dentistry during the postpandemic period.

RESULTS : There are multiple reasons why catastrophic pandemics occur due to new infectious organisms that cross the species barrier from animals to humans. These include, population explosion, mass migration, and prolonged survival of debilitated and susceptible cohorts on various immunosuppressants. Coupled with global warming and the resultant loss of habitats, such vicissitudes of humans and nature lead to microbes evolving and mutating at an exponential pace, paving the way for pandemics. The contemporary epidemics and pandemics beginning with the HIV pandemic have modulated dentistry beyond recognition, now with assiduous and robust infection control measures in place.

CONCLUSIONS AND PRACTICAL IMPLICATIONS : Because COVID-19 may become an endemic disease, particularly due to emerging SARS-CoV-2 variants the dental community should adopt modified infection control measures, teledentistry, and point-of-care diagnostics, among other measures. It is likely, that clinical ecosystems in future would be rendered even safer by predicting how pathogens evolve and priming the human immune system for the next wave of microbial combatants through vaccines produced using deep mutational scanning in which artificial intelligence and machine learning can predict the next variants even before their arrival.

Samaranayake Lakshman, Fakhruddin Kausar Sadia

2021-Nov-05

Pandemics, future, impact, oral health care, past, present

Public Health Public Health

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis.

In Frontiers in public health

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa. Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package. Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette-Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden. Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Bouba Yagai, Tsinda Emmanuel Kagning, Fonkou Maxime Descartes Mbogning, Mmbando Gideon Sadikiel, Bragazzi Nicola Luigi, Kong Jude Dzevela

2021

Africa, COVID-19, cross-country analysis, mortality, transmission

Public Health Public Health

Gender Differences in Anxiety Among COVID-19 Inpatients Under Isolation: A Questionnaire Survey During the First and Second Waves of the COVID-19 Pandemic in Japan.

In Frontiers in public health

This study assesses the gender differences in health and anxiety, especially pertaining to mental health problems and time-course effects. We surveyed 121 patients admitted to a hospital with a COVID-19 diagnosis between March 1 and August 31, 2020. Their mental status was evaluated on admission using the Japanese General Health Questionnaire-28 (GHQ-28) and State-Trait Anxiety Inventory-Form JYZ (STAI). The patients were divided into two groups depending on the period of prevalence, that is, the first and second waves of the pandemic in Japan (from the beginning of March to the end of May 2020, Time 1 = T1; and from the beginning of June to the end of August 2020, Time 2 = T2). A multivariate analysis of covariance revealed significant differences in gender by time interactions in the GHQ-28 subscale "Insomnia and anxiety" and STAI subscale "State-Anxiety." Post-hoc t-tests revealed that the scores of "Insomnia and Anxiety" and "State-Anxiety" were higher in women than in men at T1. However, no difference was observed at T2. Further, "Insomnia and Anxiety" and "State-Anxiety" were significantly higher at T1 than at T2 in female patients. There was no significant difference in males. Thus, female patients were more anxious and depressed in the early phase of the pandemic, whereas male patients had difficulties in coping with anxiety. We suggest more gender-specific mental care, particularly for women at the early stages of infection.

Tsukamoto Ryo, Kataoka Yuki, Mino Koichi, Ishibashi Naoki, Shibata Mariko, Matsuo Hiroo, Fujiwara Hironobu

2021

COVID-19, Japan, anxiety, coping, gender differences, isolation, mental health

General General

Employing Multimodal Machine Learning for Stress Detection.

In Journal of healthcare engineering

In the current information age, the human lifestyle has become more knowledge-oriented, leading to sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected, however crucial, aspects of today's fast-paced world. Mental health issues can, both directly and indirectly, affect other sections of human physiology and impede an individual's day-to-day activities and performance. However, identifying the stress and finding the stress trend for an individual that may lead to serious mental ailments is challenging and involves multiple factors. Such identification can be achieved accurately by fusing these multiple modalities (due to various factors) arising from a person's behavioral patterns. Specific techniques are identified in the literature for this purpose; however, very few machine learning-based methods are proposed for such multimodal fusion tasks. In this work, a multimodal AI-based framework is proposed to monitor a person's working behavior and stress levels. We propose a methodology for efficiently detecting stress due to workload by concatenating heterogeneous raw sensor data streams (e.g., face expressions, posture, heart rate, and computer interaction). This data can be securely stored and analyzed to understand and discover personalized unique behavioral patterns leading to mental strain and fatigue. The contribution of this work is twofold: firstly, proposing a multimodal AI-based strategy for fusion to detect stress and its level and, secondly, identifying a stress pattern over a period of time. We were able to achieve 96.09% accuracy on the test set in stress detection and classification. Further, we were able to reduce the stress scale prediction model loss to 0.036 using these modalities. This work can prove important for the community at large, specifically those working sedentary jobs, to monitor and identify stress levels, especially in current times of COVID-19.

Walambe Rahee, Nayak Pranav, Bhardwaj Ashmit, Kotecha Ketan

2021

General General

Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia.

In Journal of healthcare engineering

Objective : This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia.

Methods : First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity β, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable β and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19.

Results : There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%.

Conclusion : The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.

Liu Ting, Bai Yanling, Du Mingmei, Gao Yueming, Liu Yunxi

2021

General General

Deep Learning Approach for Early Detection of Alzheimer's Disease.

In Cognitive computation

Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer's disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages. Two methods are used to classify the medical images and detect AD. The first method uses simple CNN architectures that deal with 2D and 3D structural brain scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on 2D and 3D convolution. The second method applies the transfer learning principle to take advantage of the pre-trained models for medical image classifications, such as the VGG19 model. Due to the COVID-19 pandemic, it is difficult for people to go to hospitals periodically to avoid gatherings and infections. As a result, Alzheimer's checking web application is proposed using the final qualified proposed architectures. It helps doctors and patients to check AD remotely. It also determines the AD stage of the patient based on the AD spectrum and advises the patient according to its AD stage. Nine performance metrics are used in the evaluation and the comparison between the two methods. The experimental results prove that the CNN architectures for the first method have the following characteristics: suitable simple structures that reduce computational complexity, memory requirements, overfitting, and provide manageable time. Besides, they achieve very promising accuracies, 93.61% and 95.17% for 2D and 3D multi-class AD stage classifications. The VGG19 pre-trained model is fine-tuned and achieved an accuracy of 97% for multi-class AD stage classifications.

Helaly Hadeer A, Badawy Mahmoud, Haikal Amira Y

2021-Nov-03

Alzheimer’s disease, Brain MRI, Convolutional neural network (CNN), Deep learning, Medical image classification

General General

ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection.

In Biomedical signal processing and control

Recently, people around the world are being vulnerable to the pandemic effect of the novel Corona Virus. It is very difficult to detect the virus infected chest X-ray (CXR) image during early stages due to constant gene mutation of the virus. It is also strenuous to differentiate between the usual pneumonia from the COVID-19 positive case as both show similar symptoms. This paper proposes a modified residual network based enhancement (ENResNet) scheme for the visual clarification of COVID-19 pneumonia impairment from CXR images and classification of COVID-19 under deep learning framework. Firstly, the residual image has been generated using residual convolutional neural network through batch normalization corresponding to each image. Secondly, a module has been constructed through normalized map using patches and residual images as input. The output consisting of residual images and patches of each module are fed into the next module and this goes on for consecutive eight modules. A feature map is generated from each module and the final enhanced CXR is produced via up-sampling process. Further, we have designed a simple CNN model for automatic detection of COVID-19 from CXR images in the light of 'multi-term loss' function and 'softmax' classifier in optimal way. The proposed model exhibits better result in the diagnosis of binary classification (COVID vs. Normal) and multi-class classification (COVID vs. Pneumonia vs. Normal) in this study. The suggested ENResNet achieves a classification accuracy 99.7 % and 98.4 % for binary classification and multi-class detection respectively in comparison with state-of-the-art methods.

Ghosh Swarup Kr, Ghosh Anupam

2022-Feb

COVID-19, Chest X-ray image, Deep learning, Image enhancement, Pneumonia, ResNet

General General

A Contactless Method for Measuring Full-Day, Naturalistic Motor Behavior Using Wearable Inertial Sensors.

In Frontiers in psychology ; h5-index 92.0

How can researchers best measure infants' motor experiences in the home? Body position-whether infants are held, supine, prone, sitting, or upright-is an important developmental experience. However, the standard way of measuring infant body position, video recording by an experimenter in the home, can only capture short instances, may bias measurements, and conflicts with physical distancing guidelines resulting from the COVID-19 pandemic. Here, we introduce and validate an alternative method that uses machine learning algorithms to classify infants' body position from a set of wearable inertial sensors. A laboratory study of 15 infants demonstrated that the method was sufficiently accurate to measure individual differences in the time that infants spent in each body position. Two case studies showed the feasibility of applying this method to testing infants in the home using a contactless equipment drop-off procedure.

Franchak John M, Scott Vanessa, Luo Chuan

2021

body position, human activity recognition, machine learning, motor development, posture, wearable sensors

General General

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data.

In New generation computing

We visualize the rates of stay-home for residents by region using the difference between day-time and night-time populations to detect residential areas, and then observing the numbers of people leaving residential areas. There are issues with measuring stay-home rates by observing numbers of people visiting downtown areas, such as central urban shopping centers and major train stations. The first is that we cannot eliminate the possibility that people will avoid areas being observed and go to other areas. The second is that for people visiting downtown areas, we cannot know where they reside. These issues can be resolved if we quantify the degree of stay-home using the number of people leaving residential areas. There are significant differences in stay-home levels by region throughout Japan. By this visualization, residents of each region can see whether their level of stay-home is adequate or not, and this can provide incentive toward compliance suited to the residents of the region.

Mizuno Takayuki, Ohnishi Takaaki, Watanabe Tsutomu

2021-Nov-02

COVID-19, Mobile phone location, Social and behavior change (SBC), Stay-at-home

General General

Identifying the Risks of Chronic Diseases Using BMI Trajectories

ArXiv Preprint

Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of obesity identified by cross-sectional BMI recordings has been heavily studied, the role of BMI trajectories is much less explored. In this study, we use a machine learning approach to subtype individuals' risk of developing 18 major chronic diseases by using their BMI trajectories extracted from a large and geographically diverse EHR dataset capturing the health status of around two million individuals for a period of six years. We define nine new interpretable and evidence-based variables based on the BMI trajectories to cluster the patients into subgroups using the k-means clustering method. We thoroughly review each clusters' characteristics in terms of demographic, socioeconomic, and physiological measurement variables to specify the distinct properties of the patients in the clusters. In our experiments, direct relationship of obesity with diabetes, hypertension, Alzheimer's, and dementia have been re-established and distinct clusters with specific characteristics for several of the chronic diseases have been found to be conforming or complementary to the existing body of knowledge.

Md Mozaharul Mottalib, Jessica C Jones-Smith, Bethany Sheridan, Rahmatollah Beheshti

2021-11-09

General General

[Forefront of AI Applications for COVID-19 Imaging Diagnosis].

In Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics

The intra- and inter-observer variability in diagnosis of thoracic CT images may affect the diagnosis of COVID-19. Therefore, several studies have been reported to develop artificial intelligence (AI) approaches using deep learning (DL) and radiomics technologies. The difference between them is automatic feature extraction (DL) and hand-crafted one (radiomics). The advantages of the AI-based imaging approaches for the COVID-19 are fast throughput, non-invasion, quantification, and integration of PCR results, CT findings, and clinical information. To the best of my knowledge, three types of the AI approaches have been studied: detection, severity differentiation, and prognosis prediction of COVID-19. AI technologies on assessment of severity/prediction of prognosis for COVID-19 may be more crucial than detection of COVID-19 pneumonia after COVID-19 becomes one of common diseases.

Arimura Hidetaka, Iwasaki Takahiro

2021

deep learning, differentiation, radiomics, severity, triage

General General

Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives

ArXiv Preprint

Background: Electronic Health Records (EHRs) contain rich information of patients' health history, which usually include both structured and unstructured data. There have been many studies focusing on distilling valuable information from structured data, such as disease codes, laboratory test results, and treatments. However, relying on structured data only might be insufficient in reflecting patients' comprehensive information and such data may occasionally contain erroneous records. Objective: With the recent advances of machine learning (ML) and deep learning (DL) techniques, an increasing number of studies seek to obtain more accurate results by incorporating unstructured free-text data as well. This paper reviews studies that use multimodal data, i.e. a combination of structured and unstructured data, from EHRs as input for conventional ML or DL models to address the targeted tasks. Materials and Methods: We searched in the Institute of Electrical and Electronics Engineers (IEEE) Digital Library, PubMed, and Association for Computing Machinery (ACM) Digital Library for articles related to ML-based multimodal EHR studies. Results and Discussion: With the final 94 included studies, we focus on how data from different modalities were combined and interacted using conventional ML and DL techniques, and how these algorithms were applied in EHR-related tasks. Further, we investigate the advantages and limitations of these fusion methods and indicate future directions for ML-based multimodal EHR research.

Ziyi Liu, Jiaqi Zhang, Yongshuai Hou, Xinran Zhang, Ge Li, Yang Xiang

2021-11-09

General General

A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).

In Computers in biology and medicine

COVID-19 has had a detrimental impact on normal activities, public safety, and the global financial system. To identify the presence of this disease within communities and to commence the management of infected patients early, positive cases should be diagnosed as quickly as possible. New results from X-ray imaging indicate that images provide key information about COVID-19. Advanced deep-learning (DL) models can be applied to X-ray radiological images to accurately diagnose this disease and to mitigate the effects of a shortage of skilled medical personnel in rural areas. However, the performance of DL models strongly depends on the methodology used to design their architectures. Therefore, deep neuroevolution (DNE) techniques are introduced to automatically design DL architectures accurately. In this paper, a new paradigm is proposed for the automated diagnosis of COVID-19 from chest X-ray images using a novel two-stage improved DNE Algorithm. The proposed DNE framework is evaluated on a real-world dataset and the results demonstrate that it provides the highest classification performance in terms of different evaluation metrics.

Ahmadian Sajad, Jalali Seyed Mohammad Jafar, Islam Syed Mohammed Shamsul, Khosravi Abbas, Fazli Ebrahim, Nahavandi Saeid

2021-Nov-01

COVID-19 diagnosis, Convolutional neural network, Evolutionary computation, Improved salp swarm algorithm

Radiology Radiology

COVID-19 infection localization and severity grading from chest X-ray images.

In Computers in biology and medicine

The immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved outstanding performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation of Deep Learning models with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by an elegant human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.

Tahir Anas M, Chowdhury Muhammad E H, Khandakar Amith, Rahman Tawsifur, Qiblawey Yazan, Khurshid Uzair, Kiranyaz Serkan, Ibtehaz Nabil, Rahman M Sohel, Al-Maadeed Somaya, Mahmud Sakib, Ezeddin Maymouna, Hameed Khaled, Hamid Tahir

2021-Oct-30

COVID-19, Chest X-ray, Convolutional Neural Networks, Deep Learning, Infection Segmentation, Lung Segmentation

General General

Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases.

In Drug discovery today ; h5-index 68.0

The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences.

Bess Adam, Berglind Frej, Mukhopadhyay Supratik, Brylinski Michal, Griggs Nicholas, Cho Tiffany, Galliano Chris, Wasan Kishor M

2021-Nov-05

COVID-19, antimicrobial agents, artificial intelligence, infectious diseases

General General

MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans.

In IEEE transactions on cybernetics

The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19) lesions from computerized tomography (CT) scans are of great significance for developing an efficient computer-aided diagnosis system. Deep learning (DL) has emerged as one of the best choices for developing such a system. However, several challenges limit the efficiency of DL approaches, including data heterogeneity, considerable variety in the shape and size of the lesions, lesion imbalance, and scarce annotation. In this article, a novel multitask regression network for segmenting COVID-19 lesions is proposed to address these challenges. We name the framework MT-nCov-Net. We formulate lesion segmentation as a multitask shape regression problem that enables partaking the poor-, intermediate-, and high-quality features between various tasks. A multiscale feature learning (MFL) module is presented to capture the multiscale semantic information, which helps to efficiently learn small and large lesion features while reducing the semantic gap between different scale representations. In addition, a fine-grained lesion localization (FLL) module is introduced to detect infection lesions using an adaptive dual-attention mechanism. The generated location map and the fused multiscale representations are subsequently passed to the lesion regression (LR) module to segment the infection lesions. MT-nCov-Net enables learning complete lesion properties to accurately segment the COVID-19 lesion by regressing its shape. MT-nCov-Net is experimentally evaluated on two public multisource datasets, and the overall performance validates its superiority over the current cutting-edge approaches and demonstrates its effectiveness in tackling the problems facing the diagnosis of COVID-19.

Ding Weiping, Abdel-Basset Mohamed, Hawash Hossam, Elkomy Osama M

2021-Nov-08

General General

Quantifying Face Mask Comfort.

In Journal of occupational and environmental hygiene

Face mask usage is one of the most effective ways to limit SARS-CoV-2 transmission, but a mask is only useful if user compliance is high. Through anonymous surveys (n = 679), it was shown that mask discomfort is the primary source of noncompliance in mask wearing. Further, through these surveys, three critical predicting variables that dictate mask comfort were identified: air resistance, water vapor permeability, and face temperature change. To validate these predicting variables in a physiological context, experiments (n = 9) were performed to measure the respiratory rate and change in face temperature while wearing different types of three commonly used masks. Finally, using values of these predicting variables from experiments and the literature, and surveys asking users to rate the comfort of various masks, three machine learning algorithms were trained and tested to generate overall comfort scores for those masks. Although all three models performed with an accuracy of approximately 70%, the multiple linear regression model provides a simple analytical expression to predict the comfort scores for common face masks provided the input predicting variables. As face mask usage is crucial during the COVID-19 pandemic, the goal of this quantitative framework to predict mask comfort is hoped to improve user experience and prevent discomfort-induced noncompliance.

Koh Esther, Ambatipudi Mythri, Boone DaLoria L, Luehr Julia B W, Blaise Alena, Gonzalez Jose, Sule Nishant, Mooney David J, He Emily M

2021-Nov-08

COVID-19, SARS-CoV-2, machine learning, mask, respiration, thermal conductivity

General General

Computational Intelligence Enabled Student Performance Estimation in the Age of COVID-19.

In SN computer science

The sudden advent of COVID-19 pandemic left educational institutions in a difficult situation for the semester evaluation of students; especially where the online participation was difficult for the students. Such a situation may also happen during a similar disaster in the future. Through this work, we want to study the question: can the deep learning methods be leveraged to predict student grades based on the available performance of students. To this end, this paper presents an in-depth analysis of deep learning and machine learning approaches for the formulation of an automated students' performance estimation system that works on partially available students' academic records. Our main contributions are: (a) a large dataset with 15 courses (shared publicly for academic research); (b) statistical analysis and ablations on the estimation problem for this dataset; (c) predictive analysis through deep learning approaches and comparison with other arts and machine learning algorithms. Unlike previous approaches that rely on feature engineering or logical function deduction, our approach is fully data-driven and thus highly generic with better performance across different prediction tasks. The main takeaways from this study are: (a) for better prediction rates, it is desirable to have multiple low weightage tests than few very high weightage exams; (b) the latent space models are better estimators than sequential models; (c) deep learning models have the potential to very accurately estimate the student performance and their accuracy only improves as the training data are increased.

Bansal Vipul, Buckchash Himanshu, Raman Balasubramanian

2022

COVID-19, Deep neural network, Educational institutions, Variational auto-encoder

General General

Design and development of multilayer cotton masks via machine learning.

In Materials today. Advances

With the ongoing COVID-19 pandemic, reusable high-performance cloth masks are recommended for the public to minimize virus spread and alleviate the demand for disposable surgical masks. However, the approach to design a high-performance cotton mask is still unclear. In this study, we aimed to find out the relationship between fabric properties and mask performance via experimental design and machine learning. Our work is the first reported work of employing machine learning to develop protective face masks. Here, we analyzed the characteristics of Egyptian cotton (EC) fabrics with different thread counts and measured the efficacy of triple-layered masks with different layer combinations and stacking orders. The filtration efficiencies of the triple-layered masks were related to the cotton properties and the layer combination. Stacking EC fabrics in the order of thread count 100-300-100 provides the best particle filtration efficiency (45.4%) and bacterial filtration efficiency (98.1%). Furthermore, these key performance metrics were correctly predicted using machine-learning models based on the physical characteristics of the constituent EC layers using Lasso and XGBoost machine-learning models. Our work showed that the machine learning-based prediction approach can be generalized to other material design problems to improve the efficiency of product development.

Leow Y, Shi J K, Liu W, Ni X P, Yew P Y M, Liu S, Li Z, Xue Y, Kai D, Loh X J

2021-Dec

Bacterial filtration efficiency, COVID-19, Facial mask, Optimization, Particle filtration efficiency, Regression

General General

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

In Scientific reports ; h5-index 158.0

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.

Nikparvar Behnam, Rahman Md Mokhlesur, Hatami Faizeh, Thill Jean-Claude

2021-Nov-05

Surgery Surgery

Gut microbiome, Vitamin D, ACE2 interactions are critical factors in immune-senescence and inflammaging: key for vaccine response and severity of COVID-19 infection.

In Inflammation research : official journal of the European Histamine Research Society ... [et al.]

BACKGROUND : The SARS-CoV-2 pandemic continues to spread sporadically in the Unites States and worldwide. The severity and mortality excessively affected the frail elderly with co-existing medical diseases. There is growing evidence that cross-talk between the gut microbiome, Vitamin D and RAS/ACE2 system is essential for a balanced functioning of the elderly immune system and in regulating inflammation. In this review, we hypothesize that the state of gut microbiome, prior to infection determines the outcome associated with COVID-19 sepsis and may also be a critical factor in success to vaccination.

METHODS : Articles from PubMed/Medline searches were reviewed using a combination of terms "SARS-CoV-2, COVID-19, Inflammaging, Immune-senescence, Gut microbiome, Vitamin D, RAS/ACE2, Vaccination".

CONCLUSION : Evidence indicates a complex association between gut microbiota, ACE-2 expression and Vitamin D in COVID-19 severity. Status of gut microbiome is highly predictive of the blood molecular signatures and inflammatory markers and host responses to infection. Vitamin D has immunomodulatory function in innate and adaptive immune responses to viral infection. Anti-inflammatory functions of Vit D include regulation of gut microbiome and maintaining microbial diversity. It promotes growth of gut-friendly commensal strains of Bifida and Fermicutus species. In addition, Vitamin D is a negative regulator for expression of renin and interacts with the RAS/ ACE/ACE-2 signaling axis. Collectively, this triad may be the critical, link in determination of outcomes in SARS-CoV-2 infection. The presented data are empirical and informative. Further research using advanced systems biology techniques and artificial intelligence-assisted integration could assist with correlation of the gut microbiome with sepsis and vaccine responses. Modulating these factors may impact in guiding the success of vaccines and clinical outcomes in COVID-19 infections.

Shenoy Santosh

2021-Nov-05

ACE2, COVID-19 sepsis, Gut microbiome, Immune-senescence, Inflammaging, Vaccination, Vitamin D

General General

Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets.

In Computational intelligence and neuroscience

COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people's sentiments and psychologies. People's written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people's sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods-fastText-based (ft), domain-specific (ds), and domain-agnostic (da)-for the representation of tweets. Among these three methods, two methods ("ds" and "da") are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.

Sitaula C, Basnet A, Mainali A, Shahi T B

2021

Pathology Pathology

Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus.

In Nature genetics ; h5-index 174.0

The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) disease (COVID-19) pandemic has caused millions of deaths worldwide. Genome-wide association studies identified the 3p21.31 region as conferring a twofold increased risk of respiratory failure. Here, using a combined multiomics and machine learning approach, we identify the gain-of-function risk A allele of an SNP, rs17713054G>A, as a probable causative variant. We show with chromosome conformation capture and gene-expression analysis that the rs17713054-affected enhancer upregulates the interacting gene, leucine zipper transcription factor like 1 (LZTFL1). Selective spatial transcriptomic analysis of lung biopsies from patients with COVID-19 shows the presence of signals associated with epithelial-mesenchymal transition (EMT), a viral response pathway that is regulated by LZTFL1. We conclude that pulmonary epithelial cells undergoing EMT, rather than immune cells, are likely responsible for the 3p21.31-associated risk. Since the 3p21.31 effect is conferred by a gain-of-function, LZTFL1 may represent a therapeutic target.

Downes Damien J, Cross Amy R, Hua Peng, Roberts Nigel, Schwessinger Ron, Cutler Antony J, Munis Altar M, Brown Jill, Mielczarek Olga, de Andrea Carlos E, Melero Ignacio, Gill Deborah R, Hyde Stephen C, Knight Julian C, Todd John A, Sansom Stephen N, Issa Fadi, Davies James O J, Hughes Jim R

2021-Nov-04

General General

Analysis of the effectiveness of face-coverings on the death ratio of COVID-19 using machine learning.

In Scientific reports ; h5-index 158.0

The recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states' governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people's abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

Lafzi Ali, Boodaghi Miad, Zamani Siavash, Mohammadshafie Niyousha, Hasti Veeraraghava Raju

2021-Nov-04

Public Health Public Health

Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic.

In Population health metrics

BACKGROUND : Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system.

METHODS : Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre-post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data.

RESULTS : For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre-post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used.

CONCLUSIONS : We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.

Feng Shuo, Hategeka Celestin, Grépin Karen Ann

2021-Nov-04

Health management information system (HMIS), Health services research, Low- and middle-income countries (LMICs), Missing data, Multiple imputation, Routine health information systems (RHIS)

General General

Aptasensing nucleocapsid protein on nanodiamond assembled gold interdigitated electrodes for impedimetric SARS-CoV-2 infectious disease assessment.

In Biosensors & bioelectronics

In an aim of developing portable biosensor for SARS-CoV-2 pandemic, which facilitates the point-of-care aptasensing, a strategy using 10 μm gap-sized gold interdigitated electrode (AuIDE) is presented. The silane-modified AuIDE surface was deposited with ∼20 nm diamond and enhanced the detection of SARS-CoV-2 nucleocapsid protein (NCP). The characteristics of chemically modified diamond were evidenced by structural analyses, revealing the cubic crystalline nature at (220) and (111) planes as observed by XRD. XPS analysis denotes a strong interaction of carbon element, composed ∼95% as seen in EDS analysis. The C-C, CC, CO, CN functional groups were well-refuted from XPS spectra of carbon and oxygen elements in diamond. The interrelation between elements through FTIR analysis indicates major intrinsic bondings at 2687-2031 cm-1. The aptasensing was evaluated through electrochemical impedance spectroscopy measurements, using NCP spiked human serum. With a good selectivity the lower detection limit was evidenced as 0.389 fM, at a linear detection range from 1 fM to 100 pM. The stability, and reusability of the aptasensor were demonstrated, showing ∼30% and ∼33% loss of active state, respectively, after ∼11 days. The detection of NCP was evaluated by comparing anti-NCP aptamer and antibody as the bioprobes. The determination coefficients of R2 = 0.9759 and R2 = 0.9772 were obtained for aptamer- and antibody-based sensing, respectively. Moreover, the genuine interaction of NCP aptamer and protein was validated by enzyme linked apta-sorbent assay. The aptasensing strategy proposed with AuIDE/diamond enhanced sensing platform is highly recommended for early diagnosis of SARS-CoV-2 infection.

Ramanathan Santheraleka, Gopinath Subash C B, Ismail Zool Hilmi, Md Arshad M K, Poopalan Prabakaran

2021-Oct-27

Biomarker, Biosensor, COVID-19, Corona virus, Pandemic, Respiratory virus

General General

Analysis of 329,942 SARS-CoV-2 records retrieved from GISAID database.

In Computers in biology and medicine

BACKGROUND : The SARS-CoV-2 virus caused a worldwide pandemic - although none of its predecessors from the coronavirus family ever achieved such a scale. The key to understanding the global success of SARS-CoV-2 is hidden in its genome.

MATERIALS AND METHODS : We retrieved data for 329,942 SARS-CoV-2 records uploaded to the GISAID database from the beginning of the pandemic until the January 8, 2021. A Python variant detection script was developed to process the data using pairwise2 from the BioPython library. Sequence alignments were performed for every gene separately (except ORF1ab, which was not studied). Genomes less than 26,000 nucleotides long were excluded from the research. Clustering was performed using HDBScan.

RESULTS : Here, we addressed the genetic variability of SARS-CoV-2 using 329,942 samples. The analysis yielded 155 SNPs and deletions in more than 0.3% of the sequences. Clustering results suggested that a proportion of people (2.46%) was infected with a distinct subtype of the B.1.1.7 variant, which contained four to six additional mutations (G28881A, G28882A, G28883С, A23403G, A28095T, G25437T). Two clusters were formed by mutations in the samples uploaded predominantly by Denmark and Australia (1.48% and 2.51%, respectively). A correlation coefficient matrix detected 160 pairs of mutations (correlation coefficient greater than 0.7). We also addressed the completeness of the GISAID database, patient gender, and age. Finally, we found ORF6 and E to be the most conserved genes (96.15% and 94.66% of the sequences totally match the reference, respectively). Our results indicate multiple areas for further research in both SARS-CoV-2 studies and health science.

Zelenova Maria, Ivanova Anna, Semyonov Semyon, Gankin Yuriy

2021-Oct-26

Bioinformatics, Clustering, Correlation coefficient matrix, GISAID, Machine learning, Pandemic, SARS-CoV-2, SNP, Sequencing

Radiology Radiology

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

In PloS one ; h5-index 176.0

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.

Ikenoue Tatsuyoshi, Kataoka Yuki, Matsuoka Yoshinori, Matsumoto Junichi, Kumasawa Junji, Tochitatni Kentaro, Funakoshi Hiraku, Hosoda Tomohiro, Kugimiya Aiko, Shirano Michinori, Hamabe Fumiko, Iwata Sachiyo, Fukuma Shingo

2021

General General

Detection of COVID-19 from Chest CT Images Using CNN with MLP Hybrid Model.

In Studies in health technology and informatics ; h5-index 23.0

COVID-19 when left undetected can lead to a hazardous infection spread, leading to an unfortunate loss of life. It's of utmost importance to diagnose COVID-19 in Infected patients at the earliest, to avoid further complications. RT-PCR, the gold standard method is routinely used for the diagnosis of COVID-19 infection. Yet, this method comes along with few limitations such as its time-consuming nature, a scarcity of trained manpower, sophisticated laboratory equipment and the possibility of false positive and negative results. Physicians and global health care centers use CT scan as an alternate for the diagnosis of COVID-19. But this process of detection too, might demand more manual work, effort and time. Thus, automating the detection of COVID-19 using an intelligent system has been a recent research topic, in the view of pandemic. This will also help in saving the physician's time for carrying out further treatment. In this paper, a hybrid learning model has been proposed to identify the COVID-19 infection using CT scan images. The Convolutional Neural Network (CNN) was used for feature extraction and Multilayer Perceptron was used for classification. This hybrid learning model's results were also compared with traditional CNN and MLP models in terms of Accuracy, F1-Score, Precision and Recall. This Hybrid CNN-MLP model showed an Accuracy of 94.89% when compared with CNN and MLP giving 86.95% and 80.77% respectively.

Rajasekar Sakthi Jaya Sundar, Narayanan Vasumathi, Perumal Varalakshmi

2021-Oct-27

CNN, COVID-19, Classification, Deep Learning, Multilayer Perceptron

Public Health Public Health

Regulation Modelling and Analysis Using Machine Learning During the Covid-19 Pandemic in Russia.

In Studies in health technology and informatics ; h5-index 23.0

Due to the specific circumstances related to the COVID-19 pandemic, many countries have enforced emergency measures such as self-isolation and restriction of movement and assembly, which are also directly affecting the functioning of their respective public health and judicial systems. The goal of this study is to identify the efficiency of the criminal sanctions in Russia that were introduced in the beginning of COVID-19 outbreak using machine learning methods. We have developed a regression model for the fine handed out, using random forest regression and XGBoost regression, and calculated the features importance parameters. We have developed classification models for the remission of the penalty and for setting a sentence using a gradient boosting classifier.

Trofimov Egor, Metsker Oleg, Kopanitsa Georgy, Pashoshev David

2021-Oct-27

COVID-19, Russia, machine learning, regulation

General General

Predictive Modeling of COVID and non-COVID Pneumonia Trajectories.

In Studies in health technology and informatics ; h5-index 23.0

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.

Mramorov Nikita, Derevitskii Ilya, Kovalchuk Sergei

2021-Oct-27

Pneumonia, bayesian network, disease simulation modeling, hidden markov models, machine learning

Radiology Radiology

Corrigendum to "Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review".

In Journal of healthcare engineering

[This corrects the article DOI: 10.1155/2021/6677314.].

Ghaderzadeh Mustafa, Asadi Farkhondeh

2021

General General

Deep Learning-Based Real-Time AI Virtual Mouse System Using Computer Vision to Avoid COVID-19 Spread.

In Journal of healthcare engineering

The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.

Shriram S, Nagaraj B, Jaya J, Shankar S, Ajay P

2021

Public Health Public Health

Companionship and Sexual Issues in the Aging Population.

In Indian journal of psychological medicine

Loneliness and social isolation are significant public health crises in older adults. The issues about companionship have many psychosocial and cultural dimensions, which is further compounded by the current COVID-19 pandemic. In modern-day India, there is a significant increase in the number of older adults left to live alone because of sociocultural changes in our society. Companionship in late life is known to promote the quality of life and decrease the mental health morbidity. There is an increasing role of pets as companions to the elderly. Novel technologies such as artificial intelligence in the form of robots are being explored to support the elderly. Sexuality is another complex issue related to older adults that is often ignored. The sexuality and sexual functioning in older adults largely depend on physiological, psychological, and sociocultural factors. The principles of ageism have influenced sexuality in older adults. Sociocultural issues and the aging-related pathophysiological changes can contribute to an increased risk for legal issues related to sexuality in this population. There is a need for more systematic research into the multifaceted concept of companionship and sexuality in the older adult population. This review article addresses these two distinct subjects separately.

Ramesh Abhishek, Issac Thomas Gregor, Mukku Shiva Shanker Reddy, Sivakumar Palanimuthu T

2021-Sep

Elderly, aging, companionship, legal, loneliness, sexual issues, sexuality

Radiology Radiology

A bone suppression model ensemble to improve COVID-19 detection in chest X-rays

ArXiv Preprint

Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities like COVID-19 may become difficult due to that they are obscured by the presence of bony structures like the ribs and the clavicles, thereby resulting in screening/diagnostic misinterpretations. Automated bone suppression methods would help suppress these bony structures and increase soft tissue visibility. In this study, we propose to build an ensemble of convolutional neural network models to suppress bones in frontal CXRs, improve classification performance, and reduce interpretation errors related to COVID-19 detection. The ensemble is constructed by (i) measuring the multi-scale structural similarity index (MS-SSIM) score between the sub-blocks of the bone-suppressed image predicted by each of the top-3 performing bone-suppression models and the corresponding sub-blocks of its respective ground truth soft-tissue image, and (ii) performing a majority voting of the MS-SSIM score computed in each sub-block to identify the sub-block with the maximum MS-SSIM score and use it in constructing the final bone-suppressed image. We empirically determine the sub-block size that delivers superior bone suppression performance. It is observed that the bone suppression model ensemble outperformed the individual models in terms of MS-SSIM and other metrics. A CXR modality-specific classification model is retrained and evaluated on the non-bone-suppressed and bone-suppressed images to classify them as showing normal lungs or other COVID-19-like manifestations. We observed that the bone-suppressed model training significantly outperformed the model trained on non-bone-suppressed images toward detecting COVID-19 manifestations.

Sivaramakrishnan Rajaraman, Gregg Cohen, Les folio, Sameer Antani

2021-11-05

General General

Uses of digital technologies in the time of Covid-19: opportunities and challenges for professionals in psychiatry and mental health care.

In JMIR human factors

BACKGROUND : The Covid-19 pandemic has required psychiatric and mental health professionals to change their practices to reduce the risk of transmission of SARS-CoV-2, in particular by favoring remote monitoring and assessment via digital technologies.

OBJECTIVE : As part of a research project that was co-funded by the French National Research Agency (ARN) and the Centre-Val de Loire Region, we carried out a systematic literature review to investigate how such uses of digital technologies have been developing.

METHODS : The present systematic review was conducted following the PRISMA guidelines. The search was carried out in MEDLINE (PubMed) and Cairn databases, as well as in a platform specializing in mental health, Ascodocpsy. The search yielded 558 results for the year 2020. After applying inclusion and exclusion criteria, first on titles and abstracts, and then on full texts, 61 articles were included.

RESULTS : The analysis of the literature revealed a heterogeneous integration of digital technologies, not only depending on countries, contexts, and local regulations, but also depending on the modalities of care. Notwithstanding these variations, the use of videoconferencing has developed significantly, affecting working conditions and therapeutic relationships. For many psychiatric and mental health professionals, the pandemic has been an opportunity to build up an experience of remote care, and thus better identify the possibilities and limits of these digital technologies.

CONCLUSIONS : The new uses of such technologies essentially consist in a transition from the classic consultation model towards teleconsultation and makes less use of the specific potential of artificial intelligence. As professionals were not prepared for these uses, they were confronted with practical difficulties and ethical questions, such as the place of digital technology in care, confidentiality and protection of personal data, and equity in access to care. The health crisis questions how the organization of health care integrates the possibilities offered by digital technology, in particular so as to promote the autonomy and empowerment of mental health service users.

CLINICALTRIAL :

Hélène Kane, Gourret Baumgart Jade, El Hage Wissam, Deloyer Jocelyn, Maes Christine, Lebas Marie-Clotilde, Marazziti Donatella, Thome Johannes, Fond-Harmant Laurence, Denis Frédéric

2021-Oct-09

Public Health Public Health

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

In PloS one ; h5-index 176.0

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt.

Price Bradley S, Khodaverdi Maryam, Halasz Adam, Hendricks Brian, Kimble Wesley, Smith Gordon S, Hodder Sally L

2021

Radiology Radiology

Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software tool.

In Medicine

The COVID-19 pandemic has challenged institutions' diagnostic processes worldwide. The aim of this study was to assess the feasibility of an artificial intelligence (AI)-based software tool that automatically evaluates chest computed tomography for findings of suspected COVID-19.Two groups were retrospectively evaluated for COVID-19-associated ground glass opacities of the lungs (group A: real-time polymerase chain reaction positive COVID patients, n = 108; group B: asymptomatic pre-operative group, n = 88). The performance of an AI-based software assessment tool for detection of COVID-associated abnormalities was compared with human evaluation based on COVID-19 reporting and data system (CO-RADS) scores performed by 3 readers.All evaluated variables of the AI-based assessment showed significant differences between the 2 groups (P < .01). The inter-reader reliability of CO-RADS scoring was 0.87. The CO-RADS scores were substantially higher in group A (mean 4.28) than group B (mean 1.50). The difference between CO-RADS scoring and AI assessment was statistically significant for all variables but showed good correlation with the clinical context of the CO-RADS score. AI allowed to predict COVID positive cases with an accuracy of 0.94.The evaluated AI-based algorithm detects COVID-19-associated findings with high sensitivity and may support radiologic workflows during the pandemic.

Gashi Andi, Kubik-Huch Rahel A, Chatzaraki Vasiliki, Potempa Anna, Rauch Franziska, Grbic Sasa, Wiggli Benedikt, Friedl Andrée, Niemann Tilo

2021-Oct-15

Public Health Public Health

COVID-PA Bulletin: reports on artificial intelligence-based forecasting in coping with COVID-19 pandemic in the state of Pará, Brazil.

In Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil

OBJECTIVE : To report the university extension research result entitled 'The COVID-PA Bulletin', which presented forecasts on the behavior of the pandemic in the state of Pará, Brazil.

METHODS : The artificial intelligence technique also known as 'artificial neural networks' was used to generate 13 bulletins with short-term forecasts based on historical data from the State Department of Public Health information system.

RESULTS : After eight months of predictions, the technique generated reliable results, with an average accuracy of 97% (observed for147 days) for confirmed cases, 96% (observed for 161 days) for deaths and 86% (observed for 72 days) for Intensive Care Unit bed occupancy.

CONCLUSION : These bulletins have become a useful decision-making tool for public managers, assisting in the reallocation of hospital resources and optimization of COVID-19 control strategies in various regions of the state of Pará.

Souza Gilberto Nerino de, Braga Marcus de Barros, Rodrigues Luana Lorena Silva, Fernandes Rafael da Silva, Ramos Rommel Thiago Jucá, Carneiro Adriana Ribeiro, Brito Silvana Rossy de, Dolácio Cícero Jorge Fonseca, Tavares Ivaldo da Silva, Noronha Fernando Napoleão, Pinheiro Raphael Rodrigues, Diniz Hugo Alex Carneiro, Botelho Marcel do Nascimento, Vallinoto Antonio Carlos Rosário, Rocha Jonas Elias Castro da

2021

General General

Estimating underdiagnosis of COVID-19 with nowcasting and machine learning.

In Revista brasileira de epidemiologia = Brazilian journal of epidemiology

OBJECTIVE : To analyze the underdiagnosis of COVID-19 through nowcasting with machine learning in a Southern Brazilian capital city.

METHODS : Observational ecological design and data from 3916 notified cases of COVID-19 from April 14th to June 2nd, 2020 in Florianópolis, Brazil. A machine-learning algorithm was used to classify cases that had no diagnosis, producing the nowcast. To analyze the underdiagnosis, the difference between data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms were compared.

RESULTS : The number of new cases throughout the entire period without nowcasting was 389. With nowcasting, it was 694 (95%CI 496-897). During the six-day period, the number without nowcasting was 19 and 104 (95%CI 60-142) with nowcasting. The underdiagnosis was 37.29% in the entire period and 81.73% in the six-day period. The underdiagnosis was more critical in the six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period.

CONCLUSION : The use of nowcasting with machine learning techniques can help to estimate the number of new disease cases.

Garcia Leandro Pereira, Gonçalves André Vinícius, Andrade Matheus Pacheco, Pedebôs Lucas Alexandre, Vidor Ana Cristina, Zaina Roberto, Hallal Ana Luiza Curi, Canto Graziela de Luca, Traebert Jefferson, Araújo Gustavo Medeiros de, Amaral Fernanda Vargas

2021

Ophthalmology Ophthalmology

Machine Learning for Health: Algorithm Auditing & Quality Control.

In Journal of medical systems ; h5-index 48.0

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Oala Luis, Murchison Andrew G, Balachandran Pradeep, Choudhary Shruti, Fehr Jana, Leite Alixandro Werneck, Goldschmidt Peter G, Johner Christian, Schörverth Elora D M, Nakasi Rose, Meyer Martin, Cabitza Federico, Baird Pat, Prabhu Carolin, Weicken Eva, Liu Xiaoxuan, Wenzel Markus, Vogler Steffen, Akogo Darlington, Alsalamah Shada, Kazim Emre, Koshiyama Adriano, Piechottka Sven, Macpherson Sheena, Shadforth Ian, Geierhofer Regina, Matek Christian, Krois Joachim, Sanguinetti Bruno, Arentz Matthew, Bielik Pavol, Calderon-Ramirez Saul, Abbood Auss, Langer Nicolas, Haufe Stefan, Kherif Ferath, Pujari Sameer, Samek Wojciech, Wiegand Thomas

2021-Nov-02

Algorithm, Artificial intelligence, Auditing, Health, Machine learning, Quality control

General General

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic.

In SN computer science

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.

Razavi Moein, Alikhani Hamed, Janfaza Vahid, Sadeghi Benyamin, Alikhani Ehsan

2022

Construction Sites, Deep Learning, Facemask Detection, Faster RCNN, Social Distance Detection

General General

AIoT Used for COVID-19 Pandemic Prevention and Control.

In Contrast media & molecular imaging

The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future.

Chen Shu-Wen, Gu Xiao-Wei, Wang Jia-Ji, Zhu Hui-Sheng

2021

General General

Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis.

In Knowledge-based systems

The importance of medical data and the crucial nature of the decisions that are based on such data, as well as the large increase in its volume, has encouraged researchers to develop feature selection (FS)-based approaches to identify the most relevant data for specific medical problems In this paper, two intelligent wrapper FS approaches based on a new metaheuristic algorithm named the coronavirus herd immunity optimizer (CHIO) were applied with and without the incorporation of a greedy crossover (GC) operator strategy to enhance exploration of the search space by CHIO. The two proposed approaches, CHIO and CHIO-GC, were evaluated using 23 medical benchmark datasets and a real-world COVID-19 dataset. The experimental results indicated that CHIO-GC outperformed CHIO in terms of search capability, as reflected in classification accuracy, selection size, F-measure, standard deviation and convergence speed. The GC operator was able to enhance the balance between exploration and exploitation of the CHIO in the search and correct suboptimal solutions for faster convergence. The proposed CHIO-GC was also compared with two previous wrapper FS approaches, namely, binary moth flame optimization with Lévy flight (LBMFO_V3) and the hyper learning binary dragonfly algorithm (HLBDA), as well as four filter methods namely, Chi-square, Relief, correlation-based feature selection and information gain. CHIO-GC surpassed LBMFO_V3 and the four filter methods with an accuracy rate of 0.79 on 23 medical benchmark datasets. CHIO-GC also surpassed HLBDA with an accuracy rate of 0.93 when applied to the COVID-19 dataset. These encouraging results were obtained by striking a sufficient balance between the two search phases of CHIO-GC during the hunt for correct solutions, which also increased the convergence rate. This was accomplished by integrating a greedy crossover technique into the CHIO algorithm to remedy the inferior solutions found during premature convergence and while locked into a local optimum search space.

Alweshah Mohammed, Alkhalaileh Saleh, Al-Betar Mohammed Azmi, Bakar Azuraliza Abu

2021-Oct-29

Coronavirus herd immunity optimizer, Feature selection, Greedy crossover, Medical diagnosis, Optimization

General General

nnTwo-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans.

In Pattern recognition letters

COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia on lung CT scans. Thus, this study introduces a novel two-stage DL framework for discriminating COVID-19 from community-acquired pneumonia (CAP) depending on the detected infection region within CT slices. Firstly, a novel U-shaped network is presented to segment the lung area where the infection appears. Then, the concept of transfer learning is applied to the feature extraction network to empower the network capabilities in learning the disease patterns. After that, multi-scale information is captured and pooled via an attention mechanism for powerful classification performance. Thirdly, we propose an infection prediction module that use the infection location to guide the classification decision and hence provides interpretable classification decision. Finally, the proposed model was evaluated on public datasets and achieved great segmentation and classification performance outperforming the cutting-edge studies.

Abdel-Basset Mohamed, Hawash Hossam, Moustafa Nour, Elkomy Osama M

2021-Oct-29

General General

Medicines Question Answering System, MeQA

ArXiv Preprint

In this paper we present the first system in Spanish capable of answering questions about medicines for human use, called MeQA (Medicines Question Answering), a project created by the Spanish Agency for Medicines and Health Products (AEMPS, for its acronym in Spanish). Online services that offer medical help have proliferated considerably, mainly due to the current pandemic situation due to COVID-19. For example, websites such as Doctoralia, Savia, or SaludOnNet, offer Doctor Answers type consultations, in which patients or users can send questions to doctors and specialists, and receive an answer in less than 24 hours. Many of the questions received are related to medicines for human use, and most can be answered through the leaflets. Therefore, a system such as MeQA capable of answering these types of questions automatically could alleviate the burden on these websites, and it would be of great use to such patients.

Jesús Santamaría

2021-11-04

General General

Fighting COVID-19 with Artificial Intelligence.

In Methods in molecular biology (Clifton, N.J.)

The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is still pertinent. To this end, scientists continue to identify and repurpose marketed drugs for this new disease. Many of these drugs are currently undergoing clinical trials and, so far, only one has been officially approved by FDA. Drug repurposing is a much faster route to the clinic than standard drug development of novel molecules, nevertheless in a pandemic this process is still not fast enough to halt the spread of the virus. Artificial intelligence has already played a large part in hastening the drug discovery process, not only by facilitating the selection of potential drug candidates but also in monitoring the pandemic and enabling faster diagnosis of patients. In this chapter, we focus on the impact and challenges that artificial intelligence has demonstrated thus far with respect to drug repurposing of therapeutics for the treatment of COVID-19.

Monteleone Stefania, Kellici Tahsin F, Southey Michelle, Bodkin Michael J, Heifetz Alexander

2022

Artificial intelligence, COVID-19, Drug repurposing, Machine learning, SARS-CoV-2

Public Health Public Health

Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study.

In JMIR public health and surveillance

BACKGROUND : The implementation of novel techniques represents an additional opportunity for the rapid analysis acting as a complement to the traditional disease surveillance systems.

OBJECTIVE : The objective of this work is to describe a web-based participatory surveillance strategy among healthcare workers (HCW) in two Swiss hospitals during the first wave of COVID-19.

METHODS : A prospective cohort of HCW was initiated in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children's Hospital. For data analysis, we used a combination of the following techniques: loess regression, spearman correlation, anomaly detection and random forest.

RESULTS : From March 23rd to August 23rd 2020, 127,684 SMS were sent generating 90,414 valid reports among 1,004 participants, achieving a weekly average of 4.5 reports per user (SD 1.9). The symptom showing the strongest correlation with a positive PCR result was loss of taste. Symptoms like red eyes or runny nose were negatively associated with a positive test. The area under the ROC curve showed favorable performance of the classification tree, with an accuracy of 88% for the training and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low at 10.6%.

CONCLUSIONS : Loss of taste was the symptom which paralleled best with COVID-19 activity on the population level. On the resident level, using machine-learning based random forest classification, reporting of loss of taste and limb/muscle pain, as well as absence of runny nose and red eyes were the best predictors of COVID-19.

CLINICALTRIAL :

Leal-Neto Onicio, Egger Thomas, Schlegel Matthias, Flury Domenica, Sumer Johannes, Albrich Werner, Babouee Flury Baharak, Kuster Stefan, Vernazza Pietro, Kahlert Christian, Kohler Philipp

2021-Oct-05

General General

EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets.

In Online social networks and media

The adversarial impact of the Covid-19 pandemic has created a health crisis globally all over the world. This unprecedented crisis forced people to lockdown and changed almost every aspect of the regular activities of the people. Thus, the pandemic is also impacting everyone physically, mentally, and economically, and it, therefore, is paramount to analyze and understand emotional responses during the crisis affecting mental health. Negative emotional responses at fine-grained labels like anger and fear during the crisis might also lead to irreversible socio-economic damages. In this work, we develop a neural network model and train it using manually labeled data to detect various emotions at fine-grained labels in the Covid-19 tweets automatically. We present a manually labeled tweets dataset on COVID-19 emotional responses along with regular tweets data. We created a custom Q&A roBERTa model to extract phrases from the tweets that are primarily responsible for the corresponding emotions. None of the existing datasets and work currently provide the selected words or phrases denoting the reason for the corresponding emotions. Our classification model outperforms other systems and achieves a Jaccard score of 0.6475 with an accuracy of 0.8951. The custom RoBERTa Q&A model outperforms other models by achieving a Jaccard score of 0.7865. Further, we present a historical emotion analysis using COVID-19 tweets over the USA including each state level analysis.

Kabir Md Yasin, Madria Sanjay

2021-May

COVID-19 data, Coronavirus, Data analytics, Emotion analysis, Machine learning, Topics tracker, Twitter Data

General General

Role of artificial intelligence in peptide vaccine design against RNA viruses.

In Informatics in medicine unlocked

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

Mohanty Eileena, Mohanty Anima

2021

Artificial intelligence, Machine learning, Peptide, RNA-Virus, Vaccine

General General

Weighted butterfly optimization algorithm with intuitionistic fuzzy gaussian function based adaptive-neuro fuzzy inference system for covid-19 prediction.

In Materials today. Proceedings

Covid-19 cases are increasing each day, however none of the countries successfully came up with a proper approved vaccine. Studies suggest that the virus enters the body causing a respiratory infection post contact with a disease. Measures like screening and early diagnosis contribute towards the management of COVID- 19 thereby reducing the load of health care systems. Recent studies have provided promising methods that will be applicable for the current pandemic situation. The previous system designed a various Machine Learning (ML) algorithms such as Decision Tree (DT), Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) for predicting COVID-19 disease with symptoms. However, it does not produce satisfactory results in terms of true positive rate. And also, better optimization methods are required to enhance the precision rate with minimum execution time. To solve this problem the proposed system designed a Weighted Butterfly Optimization Algorithm (WBOA) with Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier for predicting the magnitude of COVID- 19 disease. The principle aim of this method is to design an algorithm that could predict and assess the COVID-19 parameters. Initially, the dataset regarding COVID-19 is taken as an input and preprocessed. The parameters included are age, sex, history of fever, travel history, presence of cough and lung infection. Then the optimal features are selected by using Weighted Butterfly Optimization Algorithm (WBOA) to improve the classification accuracy. Based on the selected features, an Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier is utilized for classifying the people having infection possibility. The studies conducted on this proposed system indicates that it is capable of producing better results than the other systems especially in terms of accuracy, precision, recall and f-measure.

Sundaravadivel T, Mahalakshmi V

2021-Oct-25

Adaptive-Neuro Fuzzy Inference System (IFGF-(ANFIS) and classification accuracy, Covid-19 prediction, Intuitionistic fuzzy Gaussian function, Weighted Butterfly Optimization Algorithm (WBOA)

General General

External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count.

In Health information science and systems

Purpose : The rRT-PCR for COVID-19 diagnosis is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Routine hematochemical tests are a faster and less expensive alternative for diagnosis. Thus, Machine Learning (ML) has been applied to hematological parameters to develop diagnostic tools and help clinicians in promptly managing positive patients. However, few ML models have been externally validated, making their real-world applicability unclear.

Methods : We externally validate 6 state-of-the-art diagnostic ML models, based on Complete Blood Count (CBC) and trained on a dataset encompassing 816 COVID-19 positive cases. The external validation was performed based on two datasets, collected at two different hospitals in northern Italy and encompassing 163 and 104 COVID-19 positive cases, in terms of both error rate and calibration.

Results and Conclusion : We report an average AUC of 95% and average Brier score of 0.11, out-performing existing ML methods, and showing good cross-site transportability. The best performing model (SVM) reported an average AUC of 97.5% (Sensitivity: 87.5%, Specificity: 94%), comparable with the performance of RT-PCR, and was also the best calibrated. The validated models can be useful in the early identification of potential COVID-19 patients, due to the rapid availability of CBC exams, and in multiple test settings.

Campagner Andrea, Carobene Anna, Cabitza Federico

2021-Dec

COVID-19, Calibration, Complete Blood count, External validation, Machine Learning

Radiology Radiology

Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease.

In Computational and mathematical methods in medicine

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).

Senan Ebrahim Mohammed, Alzahrani Ali, Alzahrani Mohammed Y, Alsharif Nizar, Aldhyani Theyazn H H

2021

General General

Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions.

In Neural computing & applications

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) also named COVID-19, aggressively spread all over the world in just a few months. Since then, it has multiple variants that are far more contagious than its parent. Rapid and accurate diagnosis of COVID-19 and its variants are crucial for its treatment, analysis of lungs damage and quarantine management. Deep learning-based solution for efficient and accurate diagnosis to COVID-19 and its variants using Chest X-rays, and computed tomography images could help to counter its outbreak. This work presents a novel depth-wise residual network with an atrous mechanism for accurate segmentation and lesion location of COVID-19 affected areas using volumetric CT images. The proposed framework consists of 3D depth-wise and 3D residual squeeze and excitation block in cascaded and parallel to capture uniformly multi-scale context (low-level detailed, mid-level comprehensive and high-level rich semantic features). The squeeze and excitation block adaptively recalibrates channel-wise feature responses by explicitly modeling inter-dependencies between various channels. We further have introduced an atrous mechanism with a different atrous rate as the bottom layer. Extensive experiments on benchmark CT datasets showed considerable gain (5%) for accurate segmentation and lesion location of COVID-19 affected areas.

Qayyum Abdul, Mazhar Mona, Razzak Imran, Bouadjenek Mohamed Reda

2021-Oct-26

Atrous mechanism, COVID19, CT, Channel-wise CNN, Depth-wise, Segmentation

Surgery Surgery

A collaborative robot for COVID-19 oropharyngeal swabbing.

In Robotics and autonomous systems

The coronavirus disease 2019 (COVID-19) outbreak has increased mortality and morbidity world-wide. Oropharyngeal swabbing is a well-known and commonly used sampling technique for COVID-19 diagnose around the world. We developed a robot to assist with COVID-19 oropharyngeal swabbing to prevent frontline clinical staff from being infected. The robot integrates a UR5 manipulator, rigid-flexible coupling (RFC) manipulator, force-sensing and control subsystem, visual subsystem and haptic device. The robot has strength in intrinsically safe and high repeat positioning accuracy. In addition, we also achieve one-dimensional constant force control in the automatic scheme (AS). Compared with the rigid sampling robot, the developed robot can perform the oropharyngeal swabbing procedure more safely and gently, reducing risk. Alternatively, a novel robot control schemes called collaborative manipulation scheme (CMS) which combines a automatic phase and teleoperation phase is proposed. At last, comparative experiments of three schemes were conducted, including CMS, AS, and teleoperation scheme (TS). The experimental results shows that CMS obtained the highest score according to the evaluation equation. CMS has the excellent performance in quality, experience and adaption. Therefore, the proposal of CMS is meaningful which is more suitable for robot-sampling.

Chen Yongquan, Wang Qiwen, Chi Chuliang, Wang Chengjiang, Gao Qing, Zhang Heng, Li Zheng, Mu Zonggao, Xu Ruihuan, Sun Zhenglong, Qian Huihuan

2021-Oct-26

Collaborative manipulation scheme, Evaluation metrics, Micro-pneumatic actuator, Oropharyngeal swabbing robot, Rigid–flexible coupling manipulator

Radiology Radiology

WOANet: Whale Optimized Deep Neural Network for the Classification of COVID-19 from Radiography Images.

In Biocybernetics and biomedical engineering

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.

Murugan R, Goel Tripti, Mirjalili Seyedali, Chakrabartty Deba Kumar

2021-Oct-23

COVID-19, Deep Learning, Early diagnosis, Machine Learning, Whale Optimization Algorithm

General General

Classification by a stacking model using CNN features for COVID-19 infection diagnosis.

In Journal of X-ray science and technology

Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3,486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.

Taspinar Yavuz Selim, Cinar Ilkay, Koklu Murat

2021-Oct-26

COVID-19, Convolutional neural network, Stacking model, X-ray chest images

General General

Psychotherapists' Acceptance of Telepsychotherapy During the COVID-19 Pandemic: A Machine Learning Approach.

In Clinical psychology & psychotherapy

OBJECTIVE : This study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the COVID-19 pandemic; attitudes towards TPT technology, concerns about using TPT technology and intention to use TPT technology in the future.

METHOD : Therapists (N = 795) responded to a survey about their TPT experiences during the pandemic, including quality of the therapeutic relationship, professional self-doubt, vicarious trauma, and TPT acceptance. Regression decision trees machine learning analyses were used to build prediction models for each of three aspects of TPT acceptance in a training subset of the data, and subsequently tested in the remaining subset of the total sample.

RESULTS : Attitudes toward TPT were most positive for therapists who reported a neutral or strong online working alliance with their patients, especially if they experienced little professional self-doubt and were younger than 40 years old. Therapists who were most concerned about TPT, were those who reported higher levels of professional self-doubt, particularly if they also reported vicarious trauma experiences. Therapists who reported low working alliance with their patients were least likely to use TPT in the future. Performance metrics for the decision trees indicated that these three models held up well in an out-of-sample dataset.

CONCLUSIONS : Therapists' professional self-doubt and the quality of their working alliance with their online patients appear to be the most pertinent factors associated with therapists' acceptance of TPT technology during COVID-19, and should be addressed in future training and research.

Békés Vera, Aafjes-van Doorn Katie, Zilcha-Mano Sigal, Prout Tracy, Hoffman Leon

2021-Nov-01

COVID-19; therapists, UTAUT model, machine learning, online therapy, telepsychotherapy

Public Health Public Health

Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions.

In Journal of emergency management (Weston, Mass.)

Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response-ie, timing of lockdown, timing of reopening, etc.-and disease outbreak indicators-ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics-eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.

Ahmed Md Ashraf, Sadri Arif Mohaimin, Amini M Hadi

General General

Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients.

In Patterns (New York, N.Y.)

Deep Learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus-disease 2019 (COVID-19) pandemic where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL) which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR data set to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full data set and a restricted data set. CL models consistently outperform CEL models with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC.

Wanyan Tingyi, Honarvar Hossein, Jaladanki Suraj K, Zang Chengxi, Naik Nidhi, Somani Sulaiman, De Freitas Jessica K, Paranjpe Ishan, Vaid Akhil, Zhang Jing, Miotto Riccardo, Wang Zhangyang, Nadkarni Girish N, Zitnik Marinka, Azad Ariful, Wang Fei, Ding Ying, Glicksberg Benjamin S

2021-Oct-25

General General

A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification.

In SN computer science

The COVID-19 pandemic creates a significant impact on everyone's life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.

Chakraborty Soarov, Paul Shourav, Hasan K M Azharul

2022

COVID-19, Deep learning, MongoDB, Pneumonia, Transfer learning

General General

An Encoder-Decoder-Based Method for Segmentation of COVID-19 Lung Infection in CT Images.

In SN computer science

The novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen-Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78..6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.

Elharrouss Omar, Subramanian Nandhini, Al-Maadeed Somaya

2022

COVID-19, CT-scan image, Encoder–decoder network, Lung infection segmentation

Public Health Public Health

Well-Being Data Gathering during COVID-19: Exploring the Feasibility of a Contact Tracing and Community Well-Being Safeguarding Framework.

In International journal of community well-being

Given the need for real time data to aid in decision-making at the community level, contact tracing applications (apps) are explored as a potential method of gauging overall community well-being. The context of contact tracing effectiveness and integration with artificial intelligence is provided, as well as ideas and suggestions for how to expand for use as a community-wide data gathering approach. This commentary seeks to explore dimensions around the use of such apps to help manage in times of crisis given the widespread and destructive impacts the pandemic has on community well-being, including negative economic impacts and social declines. By connecting with community well-being, the idea of a contact tracing framework would enable communities to track data and make decisions to help foster well-being across public health, economic and social domains.

Musikanski Laura, Phillips Rhonda, Rogers Paul

2021-Jan-07

COVID-19, Community well-being, Contract tracing, Resilience

General General

Identifying causal associations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021

ArXiv Preprint

Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations shared within the diabetes online community from a causality perspective. Materials and Methods: More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect-tweet dataset was manually labeled and used to train 1) a fine-tuned Bertweet model to detect causal sentences containing a causal association 2) a CRF model with BERT based features to extract possible cause-effect associations. Causes and effects were clustered in a semi-supervised approach and visualised in an interactive cause-effect-network. Results: Causal sentences were detected with a recall of 68% in an imbalanced dataset. A CRF model with BERT based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68%. This led to 96,676 sentences with cause-effect associations. "Diabetes" was identified as the central cluster followed by "Death" and "Insulin". Insulin pricing related causes were frequently associated with "Death". Conclusions: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multi-word cause and corresponding effect as expressed in diabetes-related tweets leveraging BERT-based architectures and visualised as cause-effect-network. Extracting causal associations on real-life, patient reported outcomes in social media data provides a useful complementary source of information in diabetes research.

Adrian Ahne, Vivek Khetan, Xavier Tannier, Md Imbessat Hassan Rizvi, Thomas Czernichow, Francisco Orchard, Charline Bour, Andrew Fano, Guy Fagherazzi

2021-11-01

Public Health Public Health

Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios.

METHODS : Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning.

RESULTS : Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes.

CONCLUSIONS :  If predominantly respiratory symptoms are used for test-triaging,  a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.

French Nigel, Jones Geoff, Heuer Cord, Hope Virginia, Jefferies Sarah, Muellner Petra, McNeill Andrea, Haslett Stephen, Priest Patricia

2021-Oct-30

COVID-19, Epidemiology, Machine learning, Symptoms, Triaging

General General

Carbon nanotube field-effect transistor (CNT-FET)-based biosensor for rapid detection of SARS-CoV-2 (COVID-19) surface spike protein S1.

In Bioelectrochemistry (Amsterdam, Netherlands)

The large-scale diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important for traceability and treatment during pandemic outbreaks. We developed a fast (2-3 min), easy-to-use, low-cost, and quantitative electrochemical biosensor based on carbon nanotube field-effect transistor (CNT-FET) that allows digital detection of the SARS-CoV-2 S1 in fortifited saliva samples for quick and accurate detection of SARS-CoV-2 S1 antigens. The biosensor was developed on a Si/SiO2 surface by CNT printing with the immobilization of a anti-SARS-CoV-2 S1. SARS-CoV-2 S1 antibody was immobilized on the CNT surface between the S-D channel area using a linker 1-pyrenebutanoic acid succinimidyl ester (PBASE) through non-covalent interaction. A commercial SARS-CoV-2 S1 antigen was used to characterize the electrical output of the CNT-FET biosensor. The SARS-CoV-2 S1 antigen in the 10 mM AA buffer pH 6.0 was effectively detected by the CNT-FET biosensor at concentrations from 0.1 fg/mL to 5.0 pg/mL. The limit of detection (LOD) of the developed CNT-FET biosensor was 4.12 fg/mL. The selectivity test was performed by using target SARS-CoV-2 S1 and non-target SARS-CoV-1 S1 and MERS-CoV S1 antigens in the 10 mM AA buffer pH 6.0. The biosensor showed high selectivity (no response to SARS-CoV-1 S1 or MERS-CoV S1 antigen) with SARS-CoV-2 S1 antigen detection in the 10 mM AA buffer pH 6.0. The biosensor is highly sensitive, saves time, and could be a helpful platform for rapid detection of SARS-CoV-2 S1 antigen from the patients saliva.

Zamzami Mazin A, Rabbani Gulam, Ahmad Abrar, Basalah Ahmad A, Al-Sabban Wesam H, Nate Ahn Saeyoung, Choudhry Hani

2021-Oct-15

Biosensor, Carbon nanotubes, Electrical immunosensor, Field-effect transistor, Severe acute respiratory syndrome coronavirus-2

Internal Medicine Internal Medicine

Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients.

In ACS nano ; h5-index 203.0

Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1-10,000 pg mL-1 and ultralow detection limits down to 0.46-1.36 pg mL-1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.

Gao Zhuangqiang, Song Yujing, Hsiao Te Yi, He Jiacheng, Wang Chuanyu, Shen Jialiang, MacLachlan Alana, Dai Siyuan, Singer Benjamin H, Kurabayashi Katsuo, Chen Pengyu

2021-Oct-29

coronavirus disease 2019, cytokine storm, digital/single-molecule detection, machine learning, microfluidic immunoassay, nanoplasmonics

General General

Searching for diagnostic certainty, governing risk: Patients' ambivalent experiences of medical testing.

In Sociology of health & illness

Diagnosis is pivotal to medicine's epistemic system: it serves to explain individual symptoms, classify them into recognizable conditions and determine their prognosis and treatment. Medical tests, or investigative procedures for detecting and monitoring disease, play a central role in diagnosis. While testing promises diagnostic certainty or a definitive risk assessment, it often produces uncertainties and new questions which call for yet further tests. In short, testing, regardless of its specific application, is imbued with meaning and emotionally fraught. In this article, we explore individuals' ambivalent experiences of testing as they search for diagnostic certainty, and the anxieties and frustrations of those for whom it remains elusive. Combining insights from sociological work on ambivalence and the biopolitics of health, and drawing on qualitative interviews with Australian healthcare recipients who have undergone testing in the context of clinical practice, we argue that these experiences are explicable in light of the contradictory impulses and tensions associated with what we term 'bio-subjectification'. We consider the implications of our analysis in light of the development of new tests that produce ever finer delineations between healthy and diseased populations, concluding that their use will likely multiply uncertainties and heighten rather than lessen anxieties.

Pienaar Kiran, Petersen Alan

2021-Oct-29

Australia, COVID-19, ambivalence, artificial intelligence (AI), biopolitics, diagnostic testing, qualitative study, sick role, subjectivity

General General

Using Machine Learning and the National Health and Nutrition Examination Survey to Classify Individuals With Hearing Loss.

In Frontiers in digital health

Even before the COVID-19 pandemic, there was mounting interest in remote testing solutions for audiology. The ultimate goal of such work was to improve access to hearing healthcare for individuals that might be unable or reluctant to seek audiological help in a clinic. In 2015, Diane Van Tasell patented a method for measuring an audiogram when the precise signal level was unknown (patent US 8,968,209 B2). In this method, the slope between pure-tone thresholds measured at 2 and 4 kHz is calculated and combined with questionnaire information in order to reconstruct the most likely audiograms from a database of options. An approach like the Van Tasell method is desirable because it is quick and feasible to do in a patient's home where exact stimulus levels are unknown. The goal of the present study was to use machine learning to assess the effectiveness of such audiogram-estimation methods. The National Health and Nutrition Examination Survey (NHANES), a database of audiologic and demographic information, was used to train and test several machine learning algorithms. Overall, 9,256 cases were analyzed. Audiometric data were classified using the Wisconsin Age-Related Hearing Impairment Classification Scale (WARHICS), a method that places hearing loss into one of eight categories. Of the algorithms tested, a random forest machine learning algorithm provided the best fit with only a few variables: the slope between 2 and 4 kHz; gender; age; military experience; and self-reported hearing ability. Using this method, 54.79% of the individuals were correctly classified, 34.40% were predicted to have a milder loss than measured, and 10.82% were predicted to have a more severe loss than measured. Although accuracy was low, it is unlikely audibility would be severely affected if classifications were used to apply gains. Based on audibility calculations, underamplification still provided sufficient gain to achieve ~95% correct (Speech Intelligibility Index ≥ 0.45) for sentence materials for 88% of individuals. Fewer than 1% of individuals were overamplified by 10 dB for any audiometric frequency. Given these results, this method presents a promising direction toward remote assessment; however, further refinement is needed before use in clinical fittings.

Ellis Gregory M, Souza Pamela E

2021

CDC, NHANES, audiology, centers for disease control and prevention, machine learning, national health and nutrition examination survey, remote audiology

General General

Is the Automation of Digital Mental Health Ethical? Applying an Ethical Framework to Chatbots for Cognitive Behaviour Therapy.

In Frontiers in digital health

The COVID-19 pandemic has intensified the need for mental health support across the whole spectrum of the population. Where global demand outweighs the supply of mental health services, established interventions such as cognitive behavioural therapy (CBT) have been adapted from traditional face-to-face interaction to technology-assisted formats. One such notable development is the emergence of Artificially Intelligent (AI) conversational agents for psychotherapy. Pre-pandemic, these adaptations had demonstrated some positive results; but they also generated debate due to a number of ethical and societal challenges. This article commences with a critical overview of both positive and negative aspects concerning the role of AI-CBT in its present form. Thereafter, an ethical framework is applied with reference to the themes of (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. These themes are then discussed in terms of practical recommendations for future developments. Although automated versions of therapeutic support may be of appeal during times of global crises, ethical thinking should be at the core of AI-CBT design, in addition to guiding research, policy, and real-world implementation as the world considers post-COVID-19 society.

Vilaza Giovanna Nunes, McCashin Darragh

2021

artificial intelligence, cognitive behavioural therapy, conversational agents, ethics, mental health

General General

Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA.

In Frontiers in digital health

Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.

Gupta Akash, Aeron Shrey, Agrawal Anjali, Gupta Himanshu

2021

COVID-19, LitCovid, Pubmed, latent dirichlet allocation, natural language processing, topic model, trends

General General

Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases.

In Frontiers in digital health

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.

Babel Aditi, Taneja Richi, Mondello Malvestiti Franco, Monaco Alessandro, Donde Shaantanu

2021

NCD, artificial intelligence, big data, cardiovascular disease, compliance, digital health, machine learning, patient empowerment

Public Health Public Health

COVID-19 in Brazil-Preliminary Analysis of Response Supported by Artificial Intelligence in Municipalities.

In Frontiers in digital health

The novel coronavirus disease (COVID-19) forced rapid adaptations in the way healthcare is delivered and coordinated by health systems. Brazil has a universal public health system (Sistema Unico de Saúde-SUS), being the main source of care for 75% of the population. Therefore, a saturation of the system was foreseen with the continuous increase of cases. The use of Artificial Intelligence (AI) to empower telehealth could help to tackle this by increasing a coordinated patient access to the health system. In the present study we describe a descriptive case report analyzing the use of Laura Digital Emergency Room-an AI-powered telehealth platform-in three different cities. It was computed around 130,000 interactions made by the chatbot and 24,162 patients completed the digital triage. Almost half (44.8%) of the patients were classified as having mild symptoms, 33.6% were classified as moderate and only 14.2% were classified as severe. The implementation of an AI-powered telehealth to increase accessibility while maintaining safety and leveraging value amid the unprecedent impact of the COVID-19 pandemic was feasible in Brazil and may reduce healthcare overload. New efforts to yield sustainability of affordable and scalable solutions are needed to truly leverage value in health care systems, particularly in the context of middle-low-income countries.

Morales Hugo M P, Guedes Murilo, Silva Jennifer S, Massuda Adriano

2021

COVID-19, access, artificial intelligence, care coordination, chatbot, public health

Surgery Surgery

COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis.

In Frontiers in digital health

At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of COVID-19 directly or its symptoms such as breathing, dry, and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.

Schuller Björn W, Schuller Dagmar M, Qian Kun, Liu Juan, Zheng Huaiyuan, Li Xiao

2021

COVID-19, SARS-CoV-2, computational paralinguistics, computer audition, corona virus, machine listening

Public Health Public Health

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT.

In Physics in medicine and biology

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need of precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional method uses Cross-Entropy (CE) as loss function with Softmax layer following fully-connected layer. Most DL-based classification methods target intraclass relationship in certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression; i.e., from an early stage and progress to a late stage. To learn both intraclass and interclass relationship among different stages and improve accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB) and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional method when we use modified Resnet-18 as backbone. And precision, recall and F1-score are also improved. The experimental results show that our proposed method achieves a better performance than the traditional methods, which helps establish guidelines for classification of COVID-19 chest CT images.

Guo Xiaodong, Lei Yiming, He Peng, Zeng Wenbing, Yang Ran, Ma Yinjin, Feng Peng, Lyu Qing, Wang Ge, Shan Hongming

2021-Oct-29

COVID-19, chest CT images, ensemble learning, ordinal regression

Radiology Radiology

Radiologist-supervised Transfer Learning: Improving Radiographic Localization of Pneumonia and Prognostication of Patients With COVID-19.

In Journal of thoracic imaging

PURPOSE : To assess the potential of a transfer learning strategy leveraging radiologist supervision to enhance convolutional neural network-based (CNN) localization of pneumonia on radiographs and to further assess the prognostic value of CNN severity quantification on patients evaluated for COVID-19 pneumonia, for whom severity on the presenting radiograph is a known predictor of mortality and intubation.

MATERIALS AND METHODS : We obtained an initial CNN previously trained to localize pneumonia along with 25,684 radiographs used for its training. We additionally curated 1466 radiographs from patients who had a computed tomography (CT) performed on the same day. Regional likelihoods of pneumonia were then annotated by cardiothoracic radiologists, referencing these CTs. Combining data, a preexisting CNN was fine-tuned using transfer learning. Whole-image and regional performance of the updated CNN was assessed using receiver-operating characteristic area under the curve and Dice. Finally, the value of CNN measurements was assessed with survival analysis on 203 patients with COVID-19 and compared against modified radiographic assessment of lung edema (mRALE) score.

RESULTS : Pneumonia detection area under the curve improved on both internal (0.756 to 0.841) and external (0.864 to 0.876) validation data. Dice overlap also improved, particularly in the lung bases (R: 0.121 to 0.433, L: 0.111 to 0.486). There was strong correlation between radiologist mRALE score and CNN fractional area of involvement (ρ=0.85). Survival analysis showed similar, strong prognostic ability of the CNN and mRALE for mortality, likelihood of intubation, and duration of hospitalization among patients with COVID-19.

CONCLUSIONS : Radiologist-supervised transfer learning can enhance the ability of CNNs to localize and quantify the severity of disease. Closed-loop systems incorporating radiologists may be beneficial for continued improvement of artificial intelligence algorithms.

Hurt Brian, Rubel Meagan A, Masutani Evan M, Jacobs Kathleen, Hahn Lewis, Horowitz Michael, Kligerman Seth, Hsiao Albert

2021-Oct-28

General General

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.

In PloS one ; h5-index 176.0

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

Mondal M Rubaiyat Hossain, Bharati Subrato, Podder Prajoy

2021

Pathology Pathology

Accelerating the transition of clinical science to translational medicine.

In Clinical science (London, England : 1979)

The SARS-CoV-2 pandemic has shown the importance of medical research in responding to the urgent prevention and health needs to combat the devastating disease, COVID-19, that this β-coronavirus unleashed. Equally, it has demonstrated the importance of interdisciplinary working to translate scientific discovery into public and patient benefit. As we come to adjust to live with this new virus, it is important to look back and see what lessons we have learnt in the way scientific medical discoveries can be more effectively and rapidly moved into public benefit. Clinical Science has had a long and distinguished history with this Journal bearing the same name and being an important contributor to the rapidly increasing use of human pathobiological data to gain mechanistic understanding of disease mechanisms leading to new diagnostic tests and treatments. The recognition that many complex diseases engage multiple causal pathways that may vary from patient to patient, and at different times across the lifecourse, has led to the emergence of stratified or precision medicine in which the right treatment is given to the right patient at the right time and, in doing so, minimise 'non-responders' and off-target side effects. Applications of omics technologies, the digitalisation of biology and the applications of machine learning and artificial intelligence (AI) are accelerating disease insights at pace with translation of discoveries into new diagnostic tests and treatments. The future of clinical science, as it morphs into translational medicine, is now creating unique possibilities where even the most intractable diseases are now open to being conquered.

Holgate Stephen T

2021-Oct-29

asthma, interdisciplinary, pathology, personalized medicine, translational science

General General

Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19.

In PeerJ

The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted.

Mikkili Indira, Karlapudi Abraham Peele, Venkateswarulu T C, Kodali Vidya Prabhakar, Macamdas Deepika Sri Singh, Sreerama Krupanidhi

2021

Artificial intelligence, Computed tomography, Drug discovery, Homology modeling, Machine learning, Neural networks, Pharmacogenomics, Protein prediction, Reverse transcriptase polymerase chain reaction, SARS CoV-2

General General

Construct a Knowledge Graph for China Coronavirus (COVID-19) Patient Information Tracking.

In Risk management and healthcare policy

Since first outbreak of respiratory disease in China, the Coronavirus epidemic (COVID-19) spread on a large scale, causing huge losses to individuals, families, communities and society in the country. The conventional research on the transmission process is basically to study the law or trend of the transmission of infectious diseases from a macro perspective. For in-depth study of the critical data of the newly confirmed patients, one effective way to improve the social isolation measures requires the formation of an organized tracking knowledge system for the confirmed patients and the personnel who have been removed, and the deep data mining and application. Knowledge graph (KG) is one of the irreplaceable techniques to quickly gather patient contact information and outbreak event, which reflecting the relationship between knowledge evolution and structure of novel Coronavirus. Therefore, this paper proposes a method for the analysis of COVID-19 epidemic situation using knowledge graph combined with interactive visual analysis. Firstly, based on the key factors of novel Coronavirus disease, the entity model of the patient, the relationship type of the patient and the expression of knowledge modeling were proposed, and the knowledge graph of the action track of the COVID-19 patient was deeply explored and comparative summarized. Secondly, in the process of constructing knowledge graph, conditional random field (CRF) algorithm is used to extract entity and knowledge. Meanwhile, to better analyze the disease relationship between patients, the semantic relationship of knowledge graph was combined with the visualization of knowledge graph, and the semantic model was verified by deep learning calculation and node attribute similarity. To discover the community detection of patients in the patient knowledge graph, this paper uses PageRank combined with Label propagation algorithms to discover community propagation in the network. Finally, COVID-19 epidemic situation was analyzed from confirmed patient data and multi-view collaborative interactions, such as map distribution visualization, knowledge graph visualization, and track visualization. The results show that the construction of a knowledge graph of COVID-19 patient activity is feasible for the transmission process, analysis of key nodes and tracing of activity tracks.

Wu Jiajing

2021

COVID-19, CRF, Neo4j, knowledge graph, tracking

General General

Predictive usefulness of RT-PCR testing in different patterns of Covid-19 symptomatology: analysis of a French cohort of 12,810 outpatients.

In Scientific reports ; h5-index 158.0

Reverse transcriptase polymerase chain reaction (RT-PCR) is a key tool to diagnose Covid-19. Yet it may not be the most efficient test in all patients. In this paper, we develop a clinical strategy for prescribing RT-PCR to patients based on data from COVIDOM, a French cohort of 54,000 patients with clinically suspected Covid-19, including 12,810 patients tested by RT-PCR. We use a machine-learning algorithm (decision tree) in order to predict RT-PCR results based on the clinical presentation. We show that symptoms alone are sufficient to predict RT-PCR outcome with a mean average precision of 86%. We identify combinations of symptoms that are predictive of RT-PCR positivity (90% for anosmia/ageusia) or negativity (only 30% of RT-PCR+ for a subgroup with cardiopulmonary symptoms): in both cases, RT-PCR provides little added diagnostic value. We propose a prescribing strategy based on clinical presentation that can improve the global efficiency of RT-PCR testing.

**

2021-Oct-27

General General

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Murri Rita, Lenkowicz Jacopo, Masciocchi Carlotta, Iacomini Chiara, Fantoni Massimo, Damiani Andrea, Marchetti Antonio, Sergi Paolo Domenico Angelo, Arcuri Giovanni, Cesario Alfredo, Patarnello Stefano, Antonelli Massimo, Bellantone Rocco, Bernabei Roberto, Boccia Stefania, Calabresi Paolo, Cambieri Andrea, Cauda Roberto, Colosimo Cesare, Crea Filippo, De Maria Ruggero, De Stefano Valerio, Franceschi Francesco, Gasbarrini Antonio, Parolini Ornella, Richeldi Luca, Sanguinetti Maurizio, Urbani Andrea, Zega Maurizio, Scambia Giovanni, Valentini Vincenzo

2021-Oct-27

General General

Multi-label topic classification for COVID-19 literature annotation using an ensemble model based on PubMedBERT

bioRxiv Preprint

The BioCreative VII Track 5 calls for participants to tackle the multi-label classification task for automated topic annotation of COVID-19 literature. In our participation, we evaluated several deep learning models built on PubMedBERT, a pre-trained language model, with different strategies addressing the challenges of the task. Specifically, multi-instance learning was used to deal with the large variation in the lengths of the articles, and focal loss function was used to address the imbalance in the distribution of different topics. We found that the ensemble model performed the best among all the models we have tested. Test results of our submissions showed that our approach was able to achieve satisfactory performance with an F1 score of 0.9247, which is significantly better than the baseline model (F1 score: 0.8678) and the mean of all the submissions (F1 score: 0.8931).

Tian, S.; Zhang, J.

2021-10-29

Public Health Public Health

Machine Learning-based Predictive Modelling of Anxiety and Depressive Symptoms During Eight Months of the COVID-19 Global Pandemic: Repeated Cross-Sectional Survey Study.

In JMIR mental health

BACKGROUND : Background: The COVID-19 global pandemic has increased the burden of mental illness on Canadian adults. However, the complex combination of demographic, economic, lifestyle, and perceived health risks contributing to patterns of anxiety and depression have not been explored.

OBJECTIVE : Objectives: To harness flexible machine learning methods to identify constellations of factors related to symptoms of mental illness, and to understand their changes over time during the COVID-19 pandemic.

METHODS : Methods: Cross-sectional samples of Canadian adults (≥18yrs) completed online surveys in six waves, May-Dec 2020 (n=6,021), using quota sampling strategies to match the English-speaking Canadian population on age, gender, and region. Surveys measured anxiety and depression symptoms, socio-demographics, substance use, and perceived COVID-19 risks and worries. First, principal components analysis was used to condense highly comorbid anxiety and depression symptoms into a single data-driven measure of emotional distress. Second, eXtreme Gradient Boosting (XGBoost), a machine learning algorithm that can model non-linear and interactive relationships, was used to regress this measure on all included explanatory variables. Variable importance and effects across time were explored using SHapley Additive exPlanations (SHAP).

RESULTS : Results: PCA of responses to nine anxiety and depression questions on an ordinal scale revealed a primary latent factor, termed "emotional distress", explaining 76% of variation in all nine measures. Our XGBoost model explained a substantial proportion of variance in emotional distress (r2=0.39). The three most important items predicting elevated emotional distress were increased worries about finances (SHAP=0.17), worries about getting COVID-19 (0.17), and younger age (0.13). Hopefulness was associated with emotional distress and moderated the impacts of several other factors. Predicted emotional distress exhibited a non-linear pattern over time, with highest predicted symptoms in May and November, and lowest in June.

CONCLUSIONS : Conclusions: Our results highlight factors which may exacerbate emotional distress during the current and possible future pandemics, including a role of hopefulness in moderating distressing effects of other factors. The pandemic disproportionately affected emotional distress among younger adults and those economically impacted.

Hueniken Katrina, Somé Nibene H, Abdelhack Mohamed, Taylor Graham, Elton Marshall Tara, Wickens Christine M, Hamilton Hayley A, Wells Samantha, Felsky Daniel

2021-Oct-20

Public Health Public Health

Response to comment on "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2".

In Science translational medicine ; h5-index 138.0

[Figure: see text].

Nicholson Michael D, Endler Lukas, Popa Alexandra, Genger Jakob-Wendelin, Bock Christoph, Michor Franziska, Bergthaler Andreas

2021-Oct-27

Public Health Public Health

Using machine learning to create a decision tree model to predict outcomes of COVID-19 cases in the Philippines.

In Western Pacific surveillance and response journal : WPSAR

Objective : The aim of this study was to create a decision tree model with machine learning to predict the outcomes of COVID-19 cases from data publicly available in the Philippine Department of Health (DOH) COVID Data Drop.

Methods : The study design was a cross-sectional records review of the DOH COVID Data Drop for 25 August 2020. Resolved cases that had either recovered or died were used as the final data set. Machine learning processes were used to generate, train and validate a decision tree model.

Results : A list of 132 939 resolved COVID-19 cases was used. The notification rates and case fatality rates were higher among males (145.67 per 100 000 and 2.46%, respectively). Most COVID-19 cases were clustered among people of working age, and older cases had higher case fatality rates. The majority of cases were from the National Capital Region (590.20 per 100 000), and the highest case fatality rate (5.83%) was observed in Region VII. The decision tree model prioritized age and history of hospital admission as predictors of mortality. The model had high accuracy (81.42%), sensitivity (81.65%), specificity (81.41%) and area under the curve (0.876) but a poor F-score (16.74%).

Discussion : The model predicted higher case fatality rates among older people. For cases aged > 51 years, a history of hospital admission increased the probability of COVID-19-related death. We recommend that more comprehensive primary COVID-19 data sets be used to create more robust prognostic models.

Migriño Julius R, Batangan Ani Regina U

General General

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.

In Neurocomputing

The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus, there is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. While epidemiological models can be computationally expensive, recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs. Here we introduce two digital twins of a SEIRS model applied to an idealised town. The SEIRS model has been modified to take account of spatial variation and, where possible, the model parameters are based on official virus spreading data from the UK. We compare predictions from one digital twin based on a data-corrected Bidirectional Long Short- Term Memory network with predictions from another digital twin based on a predictive Generative Adversarial Network. The predictions given by these two frameworks are accurate when compared to the original SEIRS model data. Additionally, these frameworks are data-agnostic and could be applied to towns, idealised or real, in the UK or in other countries. Also, more compartments could be included in the SEIRS model, in order to study more realistic epidemiological behaviour.

Quilodrán-Casas César, Silva Vinicius L S, Arcucci Rossella, Heaney Claire E, Guo YiKe, Pain Christopher C

2021-Oct-22

Deep Learning, Digital Twins, Generative Adversarial Networks, Long Short-Term Memory networks, Reduced Order Models

Public Health Public Health

AI for COVID-19: Battling the pandemic with computational intelligence.

In Intelligent medicine

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June, 2021, according to the World Health Organization. Since the initial reported from December 2019 in Wuhan, China, COVID-19 has demonstrated a high transmission rate (with a R0 > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and tremendous burden on health care systems around the world. To understand the serious and complex disease and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex disease. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

Xu Zhenxing, Su Chang, Xiao Yunyu, Wang Fei

2021-Oct-21

Artificial intelligence, COVID-19 pandemic, Electronic health record, Machine learning

General General

Modeling Employee Flexible Work Scheduling As A Classification Problem.

In Procedia computer science

Many organizations have adapted flexible working arrangements during COVID19 pandemic because of restrictions on the number of employees required on site at any time. Unfortunately, current employee scheduling methods are more suited for compressed working arrangements. The problem of automating compressed employee scheduling has been studied by many researchers and is widely adopted by many organizations in an attempt to achieve high quality scheduling. During process of employee scheduling many constraints may have to be considered and may require negotiating a large dimension of constraints like in flexible working. These constraints make scheduling a challenging task in these working arrangements. Most scheduling algorithms are modeled as constraint optimization problems and suited for compressed work but for flexible working with large constraint dimensions, achieving accurate scheduling is even more challenging. In this research, we propose a machine learning approach that takes advantage of mining user-defined constraints or soft constraints and transform employee scheduling into a classification problem. We propose automatically extracting employee personal schedules like calendars in order to extract their availability. We then show how to use the extracted knowledge in a multi-label classification approach in order to generate a schedule for faculty staff in a University that supports flexible working. We show that the results of this approach are comparable to that of a constraint satisfaction and optimization method that is commonly used in literature. Results show that our approach achieved accuracy of 93.1% of satisfying constraints as compared to 92.7% of a common constraint programming approach.

Kiwanuka Fred N, Karadsheh Louay, Alqatawna Ja’far, Muhamad Amin Anang Hudaya

2021

Constraint Programming, Data mining, Employee Scheduling, Machine Learning

General General

CNN-based bi-directional and directional long-short term memory network for determination of face mask.

In Biomedical signal processing and control

Context : The COVID-19 virus, exactly like in numerous other diseases, can be contaminated from person to person by inhalation. In order to prevent the spread of this virus, which led to a pandemic around the world, a series of rules have been set by governments that people must follow. The obligation to use face masks, especially in public spaces, is one of these rules.

Objective : The aim of this study is to determine whether people are wearing the face mask correctly by using deep learning methods.

Methods : A dataset consisting of 2000 images was created. In the dataset, images of a person from three different angles were collected in four classes, which are "masked", "non-masked", "masked but nose open", and "masked but under the chin". Using this data, new models are proposed by transferring the learning through AlexNet and VGG16, which are the Convolutional Neural network architectures. Classification layers of these models were removed and, Long-Short Term Memory and Bi-directional Long-Short Term Memory architectures were added instead.

Result and conclusions : Although there are four different classes to determine whether the face masks are used correctly, in the six models proposed, high success rates have been achieved. Among all models, the TrVGG16 + BiLSTM model has achieved the highest classification accuracy with 95.67%.

Significance : The study has proven that it can take advantage of the proposed models in conjunction with transfer learning to ensure the proper and effective use of the face mask, considering the benefit of society.

Koklu Murat, Cinar Ilkay, Taspinar Yavuz Selim

2022-Jan

AlexNet, BiLSTM, Convolutional neural network, LSTM, Transfer learning, VGG16

General General

Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19.

In Technology in society

The evolution of Artificial Intelligence (AI) has revolutionized many aspects of human life, including healthcare. Amidst the Covid-19 pandemic, AI-enabled smartwatches are being used to help users to self-monitor and self-manage their health. Using a framework based on Stimulus-Organism-Response (S-O-R) theory, this present study aimed to explore the use of AI-enabled smartwatches for health purposes, in particular the effects of product quality, service quality, perceived convenience, and perceived ease of use on user experience, trust and user satisfaction. Based on a purposive survey sample of 486 smartphone users in Bangladesh, data collected was analyzed using SPSS software for elementary analyses and PLS-SEM for hypotheses testing. The findings showed that the predictors, namely product quality, service quality, perceived convenience, and perceived ease of use, significantly affected user experience and trust. Similarly, user experience and trust were influential on user satisfaction and played partial mediating roles between predictors and user satisfaction. Besides, gender and age moderate the relationships of experience and trust with customer satisfaction. These findings support the S-O-R theoretical framework and have practical implications for brand and marketing managers of smartwatches in developing product features and understanding users' attitudes and behaviours.

Uzir Md Uzir Hossain, Al Halbusi Hussam, Lim Rodney, Jerin Ishraq, Abdul Hamid Abu Bakar, Ramayah Thurasamy, Haque Ahasanul

2021-Nov

Applied artificial intelligence, COVID-19, Smartwatches, User experience, User satisfaction, User trust

General General

Development of a Recombinant RBD Subunit Vaccine for SARS-CoV-2.

In Viruses ; h5-index 58.0

The novel coronavirus pneumonia (COVID-19) pandemic is a great threat to human society and now is still spreading. Although several vaccines have been authorized for emergency use, only one recombinant subunit vaccine has been permitted for widespread use. More subunit vaccines for COVID-19 should be developed in the future. The receptor binding domain (RBD), located at the S protein of SARS-CoV-2, contains most of the neutralizing epitopes. However, the immunogenicity of RBD monomers is not strong enough. In this study, we fused the RBD-monomer with a modified Fc fragment of human IgG1 to form an RBD-Fc fusion protein. The recombinant vaccine candidate based on the RBD-Fc protein could induce high levels of IgG and neutralizing antibody in mice, and these could last for at least three months. The secretion of IFN-γ, IL-2 and IL-10 in the RBD-stimulated splenocytes of immunized mice also increased significantly. Our results first showed that the RBD-Fc vaccine could induce both humoral and cellular immune responses and might be an optional strategy to control COVID-19.

Sun Yi-Sheng, Zhou Jing-Jing, Zhu Han-Ping, Xu Fang, Zhao Wen-Bin, Lu Hang-Jing, Wang Zhen, Chen Shu-Qing, Yao Ping-Ping, Jiang Jian-Min, Zhou Zhan

2021-Sep-26

COVID-19, RBD-Fc, cellular immune response, fusion protein, neutralizing antibody, vaccine

General General

Predicting COVID-19 Vaccination Intention: The Determinants of Vaccine Hesitancy.

In Vaccines

Do people want to be vaccinated against COVID-19? Herd immunity is dependent on individuals' willingness to be vaccinated since vaccination is not mandatory. Our main goal was to investigate people's intention to be vaccinated and their intentions to vaccinate their children. Moreover, we were interested in understanding the role of the personal characteristics, psychological factors, and the lockdown context on that decision. Therefore, we conducted an online survey during the lockdown in Portugal (15 January 2021 until 14 March 2021). Participants completed a socio-demographic questionnaire, questions about their intentions of being vaccinated, concerns about the vaccine, a COVID-19 attitudes and beliefs scale, a COVID-19 vaccine attitudes and beliefs scale, and the Domain-Specific Risk-Taking (DOSPERT) Scale. Our results showed that from the 649 participants, 63% of the participants reported being very likely to have the vaccine, while 60% reported being very likely to vaccinate their children. We conducted two linear regression models, explaining 65% of the variance for personal vaccination and 56% of the variance for children vaccination. We found that the COVID-19 vaccine general beliefs and attitudes were the main determinants of vaccination intention. Additionally, our proposed artificial neural network model was able to predict with 85% accuracy vaccination intention. Thus, our results suggest that psychological factors are an essential determinant of vaccination intention. Thus, public policy decision makers may use these insights for predicting vaccine hesitancy and designing effective vaccination communication strategies.

Fernandes Nuno, Costa Daniela, Costa Diogo, Keating José, Arantes Joana

2021-Oct-11

COVID-19, children vaccination, machine learning, vaccination barriers, vaccine hesitancy

Public Health Public Health

Universal Predictors of Dental Students' Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach.

In Vaccines

BACKGROUND : young adults represent a critical target for mass-vaccination strategies of COVID-19 that aim to achieve herd immunity. Healthcare students, including dental students, are perceived as the upper echelon of health literacy; therefore, their health-related beliefs, attitudes and behaviors influence their peers and communities. The main aim of this study was to synthesize a data-driven model for the predictors of COVID-19 vaccine willingness among dental students.

METHODS : a secondary analysis of data extracted from a recently conducted multi-center and multi-national cross-sectional study of dental students' attitudes towards COVID-19 vaccination in 22 countries was carried out utilizing decision tree and regression analyses. Based on previous literature, a proposed conceptual model was developed and tested through a machine learning approach to elicit factors related to dental students' willingness to get the COVID-19 vaccine.

RESULTS : machine learning analysis suggested five important predictors of COVID-19 vaccination willingness among dental students globally, i.e., the economic level of the country where the student lives and studies, the individual's trust of the pharmaceutical industry, the individual's misconception of natural immunity, the individual's belief of vaccines risk-benefit-ratio, and the individual's attitudes toward novel vaccines.

CONCLUSIONS : according to the socio-ecological theory, the country's economic level was the only contextual predictor, while the rest were individual predictors. Future research is recommended to be designed in a longitudinal fashion to facilitate evaluating the proposed model. The interventions of controlling vaccine hesitancy among the youth population may benefit from improving their views of the risk-benefit ratio of COVID-19 vaccines. Moreover, healthcare students, including dental students, will likely benefit from increasing their awareness of immunization and infectious diseases through curricular amendments.

Riad Abanoub, Huang Yi, Abdulqader Huthaifa, Morgado Mariana, Domnori Silvi, Koščík Michal, Mendes José João, Klugar Miloslav, Kateeb Elham

2021-Oct-10

COVID-19 vaccines, decision making, decision trees, dental education, international association of dental students, machine learning, mass vaccination, regression analysis

General General

Reactogenicity Correlates Only Weakly with Humoral Immunogenicity after COVID-19 Vaccination with BNT162b2 mRNA (Comirnaty®).

In Vaccines

mRNA vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), such as BNT162b2 (Comirnaty®), have proven to be highly immunogenic and efficient but also show marked reactogenicity, leading to adverse effects (AEs). Here, we analyzed whether the severity of AEs predicts the antibody response against the SARS-CoV-2 spike protein. Healthcare workers without prior SARS-CoV-2 infection, who received a prime-boost vaccination with BNT162b2, completed a standardized electronic questionnaire on the duration and severity of AEs. Serum specimens were collected two to four weeks after the boost vaccination and tested with the COVID-19 ELISA IgG (Vircell-IgG), the LIAISON® SARS-CoV-2 S1/S2 IgG CLIA (DiaSorin-IgG) and the iFlash-2019-nCoV NAb surrogate neutralization assay (Yhlo-NAb). A penalized linear regression model fitted by machine learning was used to correlate AEs with antibody levels. Eighty subjects were enrolled in the study. Systemic, but not local, AEs occurred more frequently after the boost vaccination. Elevated SARS-CoV-2 IgG antibody levels were measured in 92.5% of subjects with Vircell-IgG and in all subjects with DiaSorin-IgG and Yhlo-NAb. Gender, age and BMI showed no association with the antibody levels or with the AEs. The linear regression model identified headache, malaise and nausea as AEs with the greatest variable importance for higher antibody levels (Vircell-IgG and DiaSorin-IgG). However, the model performance for predicting antibody levels from AEs was very low for Vircell-IgG (squared correlation coefficient r2 = 0.04) and DiaSorin-IgG (r2 = 0.06). AEs did not predict the surrogate neutralization (Yhlo-NAb) results. In conclusion, AEs correlate only weakly with the SARS-CoV-2 spike protein antibody levels after COVID-19 vaccination with BNT162b2 mRNA.

Held Jürgen, Esse Jan, Tascilar Koray, Steininger Philipp, Schober Kilian, Irrgang Pascal, Alsalameh Rayya, Tenbusch Matthias, Seggewies Christof, Bogdan Christian

2021-Sep-24

BioNTech, Pfizer, SARS-CoV-2, adverse effects, adverse reactions, antibody, mRNA vaccine, machine learning, side effects

Public Health Public Health

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter.

In Vaccines

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

Karami Amir, Zhu Michael, Goldschmidt Bailey, Boyajieff Hannah R, Najafabadi Mahdi M

2021-Sep-23

COVID-19, sentiment analysis, social media, text mining, topic modeling, vaccine

General General

Deep autoencoder enables interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

bioRxiv Preprint

Single-cell RNA-seq has become a powerful tool for researchers to study biologically significant characteristics at explicitly high resolution, but its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce TAPE, a deep learning method that connects bulk RNA-seq and single-cell RNA-seq to balance the demands of big data and precision. By taking advantage of constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with existing methods on several benchmarking datasets, TAPE is more accurate (up to 40% performance improvement on the real bulk data) and faster than previous methods. For example, only TAPE can predict the tendency of increasing monocytes-to-lymphocytes (MLR) ratio in COVID-19 patients from mild to serious symptoms, whose estimated indices are consistent with laboratory data. More importantly, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. Combining with single-sample gene set enrichment analysis (ssGSEA), TAPE also provides valuable clues for people to investigate the immune response in different virus-infected patients. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.

Chen, Y.; Wang, Y.; Chen, Y.; Wei, Y.; Li, Y.; Chan, T.-F.; Li, Y.

2021-10-27

General General

A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA.

In Frontiers in public health

Background: The outbreak of the novel coronavirus disease 2019 (COVID-19) has been raging around the world for more than 1 year. Analysis of previous COVID-19 data is useful to explore its epidemic patterns. Utilizing data mining and machine learning methods for COVID-19 forecasting might provide a better insight into the trends of COVID-19 cases. This study aims to model the COVID-19 cases and perform forecasting of three important indicators of COVID-19 in the United States of America (USA), which are the adjusted percentage of daily admitted hospitalized COVID-19 cases (hospital admission), the number of daily confirmed COVID-19 cases (confirmed cases), and the number of daily death cases caused by COVID-19 (death cases). Materials and Methods: The actual COVID-19 data from March 1, 2020 to August 5, 2021 were obtained from Carnegie Mellon University Delphi Research Group. A novel forecasting algorithm was proposed to model and predict the three indicators. This algorithm is a hybrid of an unsupervised time series anomaly detection technique called matrix profile and an attention-based long short-term memory (LSTM) model. Several classic statistical models and the baseline recurrent neural network (RNN) models were used as the baseline models. All models were evaluated using a repeated holdout training and test strategy. Results: The proposed matrix profile-assisted attention-based LSTM model performed the best among all the compared models, which has the root mean square error (RMSE) = 1.23, 31612.81, 467.17, mean absolute error (MAE) = 0.95, 26259.55, 364.02, and mean absolute percentage error (MAPE) = 0.25, 1.06, 0.55, for hospital admission, confirmed cases, and death cases, respectively. Conclusion: The proposed model is more powerful in forecasting COVID-19 cases. It can potentially aid policymakers in making prevention plans and guide health care managers to allocate health care resources reasonably.

Liu Qian, Fung Daryl L X, Lac Leann, Hu Pingzhao

2021

COVID-19 forecasting, LSTM models, attention mechanism, epidemiological indicators, matrix profile

General General

Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression.

In Computational and mathematical methods in medicine

COVID-19 has swept through the world since December 2019 and caused a large number of patients and deaths. Spatial prediction on the spread of the epidemic is greatly important for disease control and management. In this study, we predicted the cumulative confirmed cases (CCCs) from Jan 17 to Mar 1, 2020, in mainland China at the city level, using machine learning algorithms, geographically weighted regression (GWR), and partial least squares regression (PLSR) based on population flow, geolocation, meteorological, and socioeconomic variables. The validation results showed that machine learning algorithms and GWR achieved good performances. These models could not effectively predict CCCs in Wuhan, the first city that reported COVID-19 cases in China, but performed well in other cities. Random Forest (RF) outperformed other methods with a CV-R 2 of 0.84. In this model, the population flow from Wuhan to other cities (WP) was the most important feature and the other features also made considerable contributions to the prediction accuracy. Compared with RF, GWR showed a slightly worse performance (CV-R 2 = 0.81) but required fewer spatial independent variables. This study explored the spatial prediction of the epidemic based on multisource spatial independent variables, providing references for the estimation of CCCs in the regions lacking accurate and timely.

Shao Qi, Xu Yongming, Wu Hanyi

2021

Radiology Radiology

CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification.

In Biomedical signal processing and control

The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score.

Verma Sourabh Singh, Prasad Ajay, Kumar Anil

2022-Jan

COVID-19, Chest X-ray images, Convolutional neural network, Coronavirus, Deep learning, SARS Cov-2

General General

Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review.

In Archives of computational methods in engineering : state of the art reviews

From the month of December-19, the outbreak of Coronavirus (COVID-19) triggered several deaths and overstated every aspect of individual health. COVID-19 has been designated as a pandemic by World Health Organization. The circumstances placed serious trouble on every country worldwide, particularly with health arrangements and time-consuming responses. The increase in the positive cases of COVID-19 globally spread every day. The quantity of accessible diagnosing kits is restricted because of complications in detecting the existence of the illness. Fast and correct diagnosis of COVID-19 is a timely requirement for the prevention and controlling of the pandemic through suitable isolation and medicinal treatment. The significance of the present work is to discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research. Abundant image resources of medical imaging as X-rays, Computed Tomography Scans, Magnetic Resonance imaging, formulate deep learning high-quality methods to fight against the pandemic COVID-19. The review presents a comprehensive idea of deep learning and its related applications in healthcare received over the past decade. At the last, some issues and confrontations to control the health crisis and outbreaks have been introduced. The progress in technology has contributed to developing individual's lives. The problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections have been also discussed.

Kaur Jaspreet, Kaur Prabhpreet

2021-Oct-19

General General

Artificial intelligence, public control, and supply of a vital commodity like COVID-19 vaccine.

In AI & society

The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system-a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico's actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico's control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico's control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico's control mechanism is considered on the case of supply of the COVID-19 vaccine in England.

Tsyganov Vladimir

2021-Oct-16

AI, COVID-19, Control, Digital, Machine learning, Political stability, Social system, Society, Vaccine

General General

Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission.

In Knowledge-based systems

In this study, a hybrid deep-learning model termed as ODANN, built upon neural networks (NN) coupled with data assimilation and natural language processing (NLP) features extraction methods, has been constructed to concurrently process daily COVID-19 time-series records and large volumes of COVID-19 related Twitter data, as representative of the global community's aggregated emotional responses towards the current pandemic, to model the growth rate in the number of confirmed COVID-19 cases globally via a proposed G parameter. Overall, there were 3 key components to ODANN's development phase, namely: (i) data hydration and pre-processing were performed on COVID-19 related Twitter data ranging between 23 January 2020 and 10 May 2020, which amounted to over 100 million Tweets written in English language; (ii) multiple NLP features extraction methods were subsequently leveraged to encode the hydrated Twitter data into useful semantic word vectors for training ODANN under an optimal set of hyperparameters; and (iii) historical time-series data of defined characteristics were also assimilated into ODANN's selected hidden layer(s) to model the G parameter daily with a lead-time of 1 day. By far, our experimental results demonstrated that by adopting a rolling time-window size of 5 days, with respect to the number of historical time-series records for assimilating different data features, enabled ODANN to outperform other traditional time-series models and recent studies, in terms of the computed RMSE and MAE scores attained from the model's testing step. Overall, the summarized results from ODANN demonstrated its competitive edge in modelling and forecasting the growth rate in the number of COVID-19 cases globally.

Chew Alvin Wei Ze, Pan Yue, Wang Ying, Zhang Limao

2021-Dec-05

Big-data, COVID-19, Deep learning, Natural language processing, Time-series prediction

General General

Time Series Predicting of COVID-19 based on Deep Learning.

In Neurocomputing

COVID-19 was declared a global pandemic by the World Health Organisation (WHO) on 11th March 2020. Many researchers have, in the past, attempted to predict a COVID outbreak and its effect. Some have regarded time-series variables as primary factors which can affect the onset of infectious diseases like influenza and severe acute respiratory syndrome (SARS). In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the COVID-19 outbreak to and throughout Malaysia, Morocco and Saudi Arabia. We have made use of certain effective deep learning (DL) models for this purpose. We assessed some specific major features for predicting the trend of the existing COVID-19 outbreak in these three countries. In this study, we also proposed a DL approach that includes recurrent neural network (RNN) and long short-term memory (LSTM) networks for predicting the probable numbers of COVID-19 cases. The LSTM models showed a 98.58% precision accuracy while the RNN models showed a 93.45% precision accuracy. Also, this study compared the number of coronavirus cases and the number of resulting deaths in Malaysia, Morocco and Saudi Arabia. Thereafter, we predicted the number of confirmed COVID-19 cases and deaths for a subsequent seven days. In this study, we presented their predictions using the data that was available up to December 3rd, 2020.

Alassafi Madini O, Jarrah Mutasem, Alotaibi Reem

2021-Oct-19

COVID-19, LSTM, Prediction, RNN, Time Series

Surgery Surgery

Video-based fully automatic assessment of open surgery suturing skills

ArXiv Preprint

The goal of this study was to develop new reliable open surgery suturing simulation system for training medical students in situation where resources are limited or in the domestic setup. Namely, we developed an algorithm for tools and hands localization as well as identifying the interactions between them based on simple webcam video data, calculating motion metrics for assessment of surgical skill. Twenty-five participants performed multiple suturing tasks using our simulator. The YOLO network has been modified to a multi-task network, for the purpose of tool localization and tool-hand interaction detection. This was accomplished by splitting the YOLO detection heads so that they supported both tasks with minimal addition to computer run-time. Furthermore, based on the outcome of the system, motion metrics were calculated. These metrics included traditional metrics such as time and path length as well as new metrics assessing the technique participants use for holding the tools. The dual-task network performance was similar to that of two networks, while computational load was only slightly bigger than one network. In addition, the motion metrics showed significant differences between experts and novices. While video capture is an essential part of minimally invasive surgery, it is not an integral component of open surgery. Thus, new algorithms, focusing on the unique challenges open surgery videos present, are required. In this study, a dual-task network was developed to solve both a localization task and a hand-tool interaction task. The dual network may be easily expanded to a multi-task network, which may be useful for images with multiple layers and for evaluating the interaction between these different layers.

Adam Goldbraikh, Anne-Lise D’Angelo, Carla M. Pugh, Shlomi Laufer

2021-10-26

General General

AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images.

In Biocybernetics and biomedical engineering

With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening. Deep learning-based approaches have been established as the most promising methods in this regard. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An intelligent feature merging scheme is introduced in the proposed merging network. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia. The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification.

Rashid Nayeeb, Hossain Md Adnan Faisal, Ali Mohammad, Islam Sukanya Mumtahina, Mahmud Tanvir, Fattah Shaikh Anowarul

2021-Oct-20

Autoencoder, COVID-19 diagnosis, Medical Image Analysis, Neural Network, X-ray

General General

DPCOVID: Privacy-Preserving Federated Covid-19 Detection

ArXiv Preprint

Coronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health. The number of COVID-19 cases has been rapidly increasing, and there is no sign of stopping. It leads to a severe shortage of test kits and accurate detection models. A recent study demonstrated that the chest X-ray radiography outperformed laboratory testing in COVID-19 detection. Therefore, using chest X-ray radiography analysis can help to screen suspected COVID-19 cases at an early stage. Moreover, the patient data is sensitive, and it must be protected to avoid revealing through model updates and reconstruction from the malicious attacker. In this paper, we present a privacy-preserving Federated Learning system for COVID-19 detection based on chest X-ray images. First, a Federated Learning system is constructed from chest X-ray images. The main idea is to build a decentralized model across multiple hospitals without sharing data among hospitals. Second, we first show that the accuracy of Federated Learning for COVID-19 identification reduces significantly for Non-IID data. We then propose a strategy to improve model's accuracy on Non-IID COVID-19 data by increasing the total number of clients, parallelism (client fraction), and computation per client. Finally, we apply a Differential Privacy Stochastic Gradient Descent (DP-SGD) to enhance the preserving of patient data privacy for our Federated Learning model. A strategy is also proposed to keep the robustness of Federated Learning to ensure the security and accuracy of the model.

Trang-Thi Ho, Yennun-Huang

2021-10-26

General General

Normative Approaches for Oral Health: Standards, Specifications, and Guidelines.

In Journal of dental research ; h5-index 65.0

Normative approaches have been developed with the aim of providing high-quality methods and strict criteria that, when applied correctly, lead to reliable results. Standards, specifications, and guidelines are needed to facilitate exchange of goods or information and secure comparability of data derived from different laboratories and sources. They are available along the whole flow from study development to test selection, study conduct, and reporting and are widely used for the evaluation of medical devices, market approval, and harmonization of terms and devices. Standards are developed by specific national and international organizations or by dedicated interest groups, mainly scientists in their respective fields. ISO (International Organization for Standardization) standards are developed following stringent regulations, and groups of experts formulate such standards. They should come from different areas (multistakeholder approach) to have as much and as broad input as possible and to avoid single-interest dominance. However, the presence of academia in such groups has been comparatively low. There is a clear need and responsibility of the oral health community to participate in the development of normative documents to provide methodological knowledge and experience, balance the interests of other stakeholders, and finally improve oral health. This will help to ensure that rapidly advancing fields of research, such as the oral health impacts of COVID-19 or the application of artificial intelligence in dentistry, benefit from standardization of approaches and reporting.

Schmalz G, Jakubovics N, Schwendicke F

2021-Oct-25

COVID-19, artificial intelligence, clinical practice guidelines, deep learning/machine learning, materials science, outcomes research

General General

PARIS: Personalized Activity Recommendation for Improving Sleep Quality

ArXiv Preprint

The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.

Meghna Singh, Saksham Goel, Abhiraj Mohan, Louis Kazaglis, Jaideep Srivastava

2021-10-26

General General

Artificial Intelligence-Based Portable Bioelectronics Platform for SARS-CoV-2 Diagnosis with Multi-nucleotide Probe Assay for Clinical Decisions.

In Analytical chemistry

In the context of the recent pandemic, the necessity of inexpensive and easily accessible rapid-test kits is well understood and need not be stressed further. In light of this, we report a multi-nucleotide probe-based diagnosis of SARS-CoV-2 using a bioelectronics platform, comprising low-cost chemiresistive biochips, a portable electronic readout, and an Android application for data acquisition with machine-learning-based decision making. The platform performs the desired diagnosis from standard nasopharyngeal and/or oral swabs (both on extracted and non-extracted RNA samples) without amplifying the viral load. Being a reverse transcription polymerase chain reaction-free hybridization assay, the proposed approach offers inexpensive, fast (time-to-result: ≤ 30 min), and early diagnosis, as opposed to most of the existing SARS-CoV-2 diagnosis protocols recommended by the WHO. For the extracted RNA samples, the assay accounts for 87 and 95.2% test accuracies, using a heuristic approach and a machine-learning-based classification method, respectively. In case of the non-extracted RNA samples, 95.6% decision accuracy is achieved using the heuristic approach, with the machine-learning-based best-fit model producing 100% accuracy. Furthermore, the availability of the handheld readout and the Android application-based simple user interface facilitates easy accessibility and portable applications. Besides, by eliminating viral RNA extraction from samples as a pre-requisite for specific detection, the proposed approach presents itself as an ideal candidate for point-of-care SARS-CoV-2 diagnosis.

Tripathy Suryasnata, Supraja Patta, Mohanty Swati, Sai Vallepu Mohan, Agrawal Tushant, Chowdary Ch Gajendranath, Taranikanti Madhuri, Bandaru Rajiv, Mudunuru Aswin Kumar, Tadi Lakshmi Jyothi, Suravaram Swathi, Siddiqui Imran Ahmed, Maddur Srinivas, Guntuka Rohith Kumar, Singh Ranjana, Singh Vikrant, Singh Shiv Govind

2021-Oct-25

Radiology Radiology

Severe dysbiosis and specific Haemophilus and Neisseria signatures as hallmarks of the oropharyngeal microbiome in critically ill COVID-19 patients.

In Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

BACKGROUND : At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with Coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict COVID-19 illness. However, the results are not conclusive, as critical illness can drastically alter a patient's microbiome through multiple confounders.

METHODS : To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multi-center, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate and severe COVID-19 (n=322 participants).

RESULTS : In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features.

CONCLUSION : In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.

de Castilhos Juliana, Zamir Eli, Hippchen Theresa, Rohrbach Roman, Schmidt Sabine, Hengler Silvana, Schumacher Hanna, Neubauer Melanie, Kunz Sabrina, Müller-Esch Tonia, Hiergeist Andreas, Gessner André, Khalid Dina, Gaiser Rogier, Cullin Nyssa, Papagiannarou Stamatia M, Beuthien-Baumann Bettina, Krämer Alwin, Bartenschlager Ralf, Jäger Dirk, Müller Michael, Herth Felix, Duerschmied Daniel, Schneider Jochen, Schmid Roland M, Eberhardt Johann F, Khodamoradi Yascha, Vehreschild Maria J G T, Teufel Andreas, Ebert Matthias P, Hau Peter, Salzberger Bernd, Schnitzler Paul, Poeck Hendrik, Elinav Eran, Merle Uta, Stein-Thoeringer Christoph K

2021-Oct-25

COVID-19, SARS-CoV-2, dysbiosis, intensive medical care, machine learning, microbiome

General General

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

In JMIR formative research

BACKGROUND : Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.

OBJECTIVE : This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.

METHODS : Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.

RESULTS : Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.

CONCLUSIONS : Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Popescu Christina, Golden Grace, Benrimoh David, Tanguay-Sela Myriam, Slowey Dominique, Lundrigan Eryn, Williams Jérôme, Desormeau Bennet, Kardani Divyesh, Perez Tamara, Rollins Colleen, Israel Sonia, Perlman Kelly, Armstrong Caitrin, Baxter Jacob, Whitmore Kate, Fradette Marie-Jeanne, Felcarek-Hope Kaelan, Soufi Ghassen, Fratila Robert, Mehltretter Joseph, Looper Karl, Steiner Warren, Rej Soham, Karp Jordan F, Heller Katherine, Parikh Sagar V, McGuire-Snieckus Rebecca, Ferrari Manuela, Margolese Howard, Turecki Gustavo

2021-Oct-25

artificial intelligence, clinical decision support system, feasibility, major depressive disorder, mobile phone, usability

General General

Smoking Status and Factors associated with COVID-19 In-hospital Mortality among U.S. Veterans.

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

INTRODUCTION : The role of smoking in risk of death among patients with COVID-19 remains unclear. We examined the association between in-hospital mortality from COVID-19 and smoking status and other factors in the United States Veterans Health Administration (VHA).

METHODS : This is an observational, retrospective cohort study using the VHA COVID-19 shared data resources for February 1 to September 11, 2020. Veterans admitted to the hospital who tested positive for SARS-CoV-2 and hospitalized by VHA were grouped into Never (as reference, NS), Former (FS), and Current smokers (CS). The main outcome was in-hospital mortality. Control factors were the most important variables (among all available) determined through a cascade of machine learning. We reported adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) from logistic regression models, imputing missing smoking status in our primary analysis.

RESULTS : Out of 8,667,996 VHA enrollees, 505,143 were tested for SARS-CoV-2 (NS=191,143; FS=240,336; CS=117,706; Unknown=45,533). The aOR of in-hospital mortality was 1.16 (95%CI 1.01, 1.32) for FS vs. NS and 0.97 (95%CI 0.78, 1.22; P > 0.05) for CS vs. NS with imputed smoking status. Among other factors, famotidine and non-steroidal anti-inflammatory drugs (NSAID) use before hospitalization were associated with lower risk while diabetes with complications, kidney disease, obesity, and advanced age were associated with higher risk of in-hospital mortality.

CONCLUSIONS : In patients admitted to the hospital with SARS-CoV-2 infection, our data demonstrate that FS are at higher risk of in-hospital mortality than NS. However, this pattern was not seen among CS highlighting the need for more granular analysis with high quality smoking status data to further clarify our understanding of smoking risk and COVID-19-related mortality. Presence of comorbidities and advanced age were also associated with increased risk of in-hospital mortality.

IMPLICATIONS : Veterans who were former smokers were at higher risk of in-hospital mortality compared to never smokers. Current smokers and never smokers were at similar risk of in-hospital mortality.The use of famotidine and non-steroidal anti-inflammatory drugs (NSAIDs) before hospitalization were associated with lower risk while uncontrolled diabetes mellitus, advanced age, kidney disease, and obesity were associated with higher risk of in-hospital mortality.

Razjouyan Javad, Helmer Drew A, Lynch Kristine E, Hanania Nicola A, Klotman Paul E, Sharafkhaneh Amir, Amos Christopher I

2021-Oct-25

COVID-19, Machine learning, SARS-CoV-2, Smoking, Veteran, hospitalization, mortality

Public Health Public Health

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence-Based Infodemiology Study.

In JMIR infodemiology

Background : Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines.

Objective : Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence-based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications.

Methods : The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy.

Results : We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: "flu," "influenza," "vaccination," "vaccine," and "vaxx." We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that "flu" and "covid" occurrences were inversely correlated as "flu" disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: "health and medicine (biological and clinical aspects)," "protection and responsibility," and "politics." By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events.

Conclusions : This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations' engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

Benis Arriel, Chatsubi Anat, Levner Eugene, Ashkenazi Shai

COVID-19, SARS-CoV-2, artificial intelligence, health communication, influenza, infodemiology, machine learning, social media, social networks, text mining, vaccination, vaccines

General General

Respiratory Outcomes in Patients Following COVID-19-Related Hospitalization: A Meta-Analysis.

In Frontiers in molecular biosciences

Background: To determine the respiratory outcomes in patients following COVID-19-related hospitalization. Methods: Systematic review and meta-analysis of the literature. Results: Forced vital capacity (FVC, % of predicted): 0-3 months post discharge: 96.1, 95% CI [82.1-110.0]; 3-6 months post discharge: 99.9, 95% CI [84.8, 115.0]; >6 months post discharge: 97.4, 95% CI [76.8-118.0]. Diffusing capacity of the lungs for carbon monoxide (DLCO, % of predicted): 0-3 months post discharge: 83.9, 95% CI [68.9-98.9]; 3-6 months post discharge: 91.2, 95% CI [74.8-107.7]; >6 months post discharge: 97.3, 95% CI [76.7-117.9]. Percentage of patients with FVC less than 80% of predicted: 0-3 months post discharge: 10%, 95% CI [6-14%]; 3-6 months post discharge: 10%, 95% CI [2-18%]; >6 months post discharge: 13%, 95% CI [8-18%]. Percentage of patients with DLCO less than 80% of predicted: 0-3 months post discharge: 48%, 95% CI [41-56%]; 3-6 months post discharge: 33%, 95% CI [23-44%]; >6 months post discharge: 43%, 95% CI [22-65%]. Conclusion: The meta-analysis confirms a high prevalence of persistent lung diffusion impairment in patients following COVID-19-related hospitalization. Routine respiratory follow-up is thus strongly recommended.

Guo Tao, Jiang Fangfang, Liu Yufei, Zhao Yunpeng, Li Yiran, Wang Yihua

2021

COVID-19, DLCO, FVC, follow-up, meta-analysis, pulmonary function test, synthesis review

General General

Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans.

In Journal of personalized medicine

BACKGROUND : Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists.

METHOD : A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients).

RESULTS : Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods.

CONCLUSIONS : These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.

Owais Muhammad, Baek Na Rae, Park Kang Ryoung

2021-Oct-07

COVID-19 infection segmentation, DAL-Net, artificial intelligence, computer-aided diagnosis, lung disease

Radiology Radiology

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment.

In Journal of personalized medicine

OBJECTIVE : To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified.

METHODS : Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC).

RESULTS : Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05).

CONCLUSIONS : Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

Fusco Roberta, Grassi Roberta, Granata Vincenza, Setola Sergio Venanzio, Grassi Francesca, Cozzi Diletta, Pecori Biagio, Izzo Francesco, Petrillo Antonella

2021-Sep-30

COVID-19, X-ray, artificial intelligence, computed tomography, deep learning, machine learning

Public Health Public Health

Exploring and Monitoring the Reasons for Hesitation with COVID-19 Vaccine Based on Social-Platform Text and Classification Algorithms.

In Healthcare (Basel, Switzerland)

(1) Background: The COVID-19 pandemic is globally rampant, and it is the common goal of all countries to eliminate hesitation in taking the COVID-19 vaccine and achieve herd immunity as soon as possible. However, people are generally more hesitant about the COVID-19 vaccine than about other conventional vaccines, and exploring the specific reasons for hesitation with the COVID-19 vaccine is crucial. (2) Methods: this paper selected text data from a social platform to conduct qualitative analysis of the text to structure COVID-19 vaccine hesitancy reasons, and then conducted semiautomatic quantitative content analysis of the text through a supervised machine-learning method to classify them. (3) Results: on the basis of a large number of studies and news reports on vaccine hesitancy, we structured 12 types of the COVID-19 vaccine hesitancy reasons. Then, in the experiment, we conducted comparative analysis of three classifiers: support vector machine (SVM), logistic regression (LR), and naive Bayes classifier (NBC). Results show that the SVM classification model with TF-IDF and SMOTE had the best performance. (4) Conclusions: our study structured 12 types of COVID-19 vaccine hesitancy reasons through qualitative analysis, filling in the gaps of previous studies. At the same time, this work provides public health institutions with a monitoring tool to support efforts to mitigate and eliminate COVID-19 vaccine hesitancy.

Liu Jingfang, Lu Shuangjinhua, Lu Caiying

2021-Oct-12

COVID-19 vaccine, text classification, vaccine hesitant

Surgery Surgery

The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic-A Scoping Review.

In Journal of clinical medicine

BACKGROUND : SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. In this overview, we concentrated on enhancing the two concepts in surgery after the pandemic, and we examined the factors on a global scale.

OBJECTIVE : The primary goal of this scoping review is to elaborate on how surgeons have used eHealth and AI before; during; and after the current global pandemic. More specifically, this review focuses on the empowerment of the concepts of electronic health and artificial intelligence after the pandemic; which mainly depend on the efforts of countries to advance the notions of surgery.

DESIGN : The use of an online search engine was the most applied method. The publication years of all the studies included in the study ranged from 2013 to 2021. Out of the reviewed studies; forty-four qualified for inclusion in the review.

DISCUSSION : We evaluated the prevalence of the concepts in different continents such as the United States; Europe; Asia; the Middle East; and Africa. Our research reveals that the success of eHealth and artificial intelligence adoption primarily depends on the efforts of countries to advance the notions in surgery.

CONCLUSIONS : The study's primary limitation is insufficient information on eHealth and artificial intelligence concepts; particularly in developing nations. Future research should focus on establishing methods of handling eHealth and AI challenges around confidentiality and data security.

Taha-Mehlitz Stephanie, Hendie Ahmad, Taha Anas

2021-Oct-19

COVID-19, SARS-CoV-2, artificial intelligence, eHealth, pandemic, surgery

General General

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach.

In International journal of environmental research and public health ; h5-index 73.0

The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition.

Jossa-Bastidas Oscar, Zahia Sofia, Fuente-Vidal Andrea, Sánchez Férez Néstor, Roda Noguera Oriol, Montane Joel, Garcia-Zapirain Begonya

2021-Oct-14

adherence, deep learning, eHealth, fitness app, mHealth, physical activity, regression

General General

Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany.

In International journal of environmental research and public health ; h5-index 73.0

(1) Background: In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. (3) Results: Counties with low socioeconomic status (SES) had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

Doblhammer Gabriele, Kreft Daniel, Reinke Constantin

2021-Oct-12

Shap values, boosting models, incidence, machine learning, mortality

Radiology Radiology

A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19.

In Diagnostics (Basel, Switzerland)

Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.

Wang Tianming, Chen Zhu, Shang Quanliang, Ma Cong, Chen Xiangyu, Xiao Enhua

2021-Oct-18

COVID-19, artificial intelligence, deep learning, machine learning, medical imaging

General General

Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI.

In Diagnostics (Basel, Switzerland)

The new 'normal' defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses.

Rehman Ikram Ur, Sobnath Drishty, Nasralla Moustafa M, Winnett Maria, Anwar Aamir, Asif Waqar, Sherazi Hafiz Husnain Raza

2021-Oct-17

COVID-19, applied behaviour analysis, artificial intelligence, augmentative and alternative communication, autism, mobile apps, special educational needs

Radiology Radiology

Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.

In Diagnostics (Basel, Switzerland)

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients' CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.

Bae Joseph, Kapse Saarthak, Singh Gagandeep, Gattu Rishabh, Ali Syed, Shah Neal, Marshall Colin, Pierce Jonathan, Phatak Tej, Gupta Amit, Green Jeremy, Madan Nikhil, Prasanna Prateek

2021-Sep-30

COVID-19, artificial intelligence, deep learning, machine learning, radiography, radiomics

General General

Pandemic model with data-driven phase detection, a study using COVID-19 data

ArXiv Preprint

The recent COVID-19 pandemic has promoted vigorous scientific activity in an effort to understand, advice and control the pandemic. Data is now freely available at a staggering rate worldwide. Unfortunately, this unprecedented level of information contains a variety of data sources and formats, and the models do not always conform to the description of the data. Health officials have recognized the need for more accurate models that can adjust to sudden changes, such as produced by changes in behavior or social restrictions. In this work we formulate a model that fits a ``SIR''-type model concurrently with a statistical change detection test on the data. The result is a piece