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General General

Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics

bioRxiv Preprint

The COVID-19 pandemic is a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Future pandemics could be prevented by accessible, easily deployable broad-spectrum oral antivirals and open knowledge bases that derisk and accelerate novel antiviral discovery and development. Here, we report the results of the COVID Moonshot, a fully open-science structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical scaffold that is differentiated to current clinical candidates in terms of toxicity and pharmacokinetics liabilities, and developed it into orally-bioavailable inhibitors with clinical potential. Our approach leverages crowdsourcing, high throughput structural biology, machine learning, and exascale molecular simulations. In the process, we generated a detailed map of the structural plasticity of the main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>500 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

The COVID Moonshot Consortium, ; Achdout, H.; Aimon, A.; Bar-David, E.; Barr, H.; Ben-Shmuel, A.; Bennett, J.; Boby, M. L.; Borden, B.; Bowman, G. R.; Brun, J.; BVNBS, S.; Calmiano, M.; Carbery, A.; Cattermole, E.; Chernyshenko, E.; Chodera, J. D.; Clyde, A.; Coffland, J. E.; Cohen, G.; Cole, J.; Contini, A.; Cox, L.; Cvitkovic, M.; Dias, A.; Donckers, K.; Dotson, D. L.; Douangamath, A.; Duberstein, S.; Dudgeon, T.; Dunnett, L.; Eastman, P. K.; Erez, N.; Eyermann, C. J.; Fairhead, M.; Fate, G.; Fearon, D.; Fedorov, O.; Ferla, M.; Fernandes, R. S.; Ferrins, L.; Foster, R.; Foster, H.; Gabizon,

2021-10-18

General General

A Deep Learning-based Approach for Real-time Facemask Detection

ArXiv Preprint

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask becomes one of the effective protection solutions adopted by many governments. Manual real-time monitoring of facemask wearing for a large group of people is becoming a difficult task. The goal of this paper is to use deep learning (DL), which has shown excellent results in many real-life applications, to ensure efficient real-time facemask detection. The proposed approach is based on two steps. An off-line step aiming to create a DL model that is able to detect and locate facemasks and whether they are appropriately worn. An online step that deploys the DL model at edge computing in order to detect masks in real-time. In this study, we propose to use MobileNetV2 to detect facemask in real-time. Several experiments are conducted and show good performances of the proposed approach (99% for training and testing accuracy). In addition, several comparisons with many state-of-the-art models namely ResNet50, DenseNet, and VGG16 show good performance of the MobileNetV2 in terms of training time and accuracy.

Wadii Boulila, Ayyub Alzahem, Aseel Almoudi, Muhanad Afifi, Ibrahim Alturki, Maha Driss

2021-10-17

General General

COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble.

In Computers in biology and medicine

The COVID-19 pandemic has collapsed the public healthcare systems, along with severely damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus, led to community spread, causing the death of more than a million people worldwide. The primary reason for the uncontrolled spread of the virus is the lack of provision for population-wise screening. The apparatus for RT-PCR based COVID-19 detection is scarce and the testing process takes 6-9 h. The test is also not satisfactorily sensitive (71% sensitive only). Hence, Computer-Aided Detection techniques based on deep learning methods can be used in such a scenario using other modalities like chest CT-scan images for more accurate and sensitive screening. In this paper, we propose a method that uses a Sugeno fuzzy integral ensemble of four pre-trained deep learning models, namely, VGG-11, GoogLeNet, SqueezeNet v1.1 and Wide ResNet-50-2, for classification of chest CT-scan images into COVID and Non-COVID categories. The proposed framework has been tested on a publicly available dataset for evaluation and it achieves 98.93% accuracy and 98.93% sensitivity on the same. The model outperforms state-of-the-art methods on the same dataset and proves to be a reliable COVID-19 detector. The relevant source codes for the proposed approach can be found at: https://github.com/Rohit-Kundu/Fuzzy-Integral-Covid-Detection.

Kundu Rohit, Singh Pawan Kumar, Mirjalili Seyedali, Sarkar Ram

2021-Oct-01

COVID-19, CT-Scan images, Computer-aided detection, Deep learning, Ensemble, Fuzzy integral, Sugeno integral, Transfer learning

Pathology Pathology

Horizon Scanning: Teaching Genomics and Personalized Medicine in the Digital Age.

In Omics : a journal of integrative biology

Digital transformation is currently impacting not only health care but also education curricula for medicine and life sciences. The COVID-19 pandemic has accelerated the deployment of digital technologies such as the Internet of Things and artificial intelligence in diverse fields of biomedicine. Genomics and related fields of inquiry such as pharmacogenomics and personalized medicine have been making important progress over the past decades. However, the genomics knowledge of health care professionals and other stakeholders in society is not commensurate with the current state of progress in these scientific fields. The rise of digital health offers unprecedented opportunities both for health care professionals and the general public to expand their genomics literacy and education. This expert review offers an analysis of the bottlenecks that affect and issues that need to be addressed to catalyze genomics and personalized medicine education in the digital era. In addition, we summarize and critically discuss the various educational and awareness opportunities that presently exist to catalyze the delivery of genomics knowledge in ways closely attuned to the emerging field of digital health.

Patrinos George P, Mitropoulou Christina

2021-Oct-13

digital health, e-learning courses, genomic education, personalized medicine, portable molecular biology laboratory, public engagement

Public Health Public Health

Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers.

In PloS one ; h5-index 176.0

Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies together with app-based cognitive assessments, may be used to forecast the onset of symptoms consistent with a viral illness. We describe our strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset. The model correctly predicts, almost all of the time (97%), individuals who will not develop viral-like illness symptoms in the next three days. Conversely, the model correctly predicts as positive 34% of the time, individuals who will develop viral-like illness symptoms in the next three days. This model uses a conservative framework, warning potentially pre-symptomatic individuals to socially isolate while minimizing warnings to individuals with a low likelihood of developing viral-like symptoms in the next three days. To our knowledge, this is the first study using wearables and apps with machine learning to predict the occurrence of viral illness-like symptoms. The demonstrated approach to forecasting the onset of viral illness-like symptoms offers a novel, digital decision-making tool for public health safety by potentially limiting viral transmission.

D’Haese Pierre-François, Finomore Victor, Lesnik Dmitry, Kornhauser Laura, Schaefer Tobias, Konrad Peter E, Hodder Sally, Marsh Clay, Rezai Ali R

2021

Cardiology Cardiology

Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and Remdesivir.

In Journal of medical virology

BACKGROUND : We aimed to create the prediction model of in-hospital mortality using machine learning methods for patients with COVID-19 treated with steroid and remdesivir.

STUDY DESIGN AND SETTING : We reviewed 1,571 hospitalized patients with laboratory confirmed COVID-19 from the Mount Sinai Health System treated with both steroid and remdesivir. The important variables associated with in-hospital mortality were identified using LASSO and SHAP through light gradient boosting model (GBM). The data before February 17th , 2021 (N=769) was randomly split into training and testing datasets; 80% vs. 20%, respectively. Light GBM models were created with train data and area under the curves (AUCs) were calculated. Additionally, we calculated AUC with the data between February 17th , 2021 and March 30th , 2021 (N=802).

RESULTS : Of the 1,571 patients admitted due to COVID-19, 331 (21.1%) died during hospitalization. Through LASSO and SHAP, we selected 6 important variables; age, hypertension, oxygen saturation, blood urea nitrogen, intensive care unit admission and endotracheal intubation. AUCs using training and testing datasets derived from the data before February 17th , 2021 were 0.871/0.911. Additionally, light GBM model has high predictability for the latest data (AUC: 0.881). (https://covid-risk-model.herokuapp.com/).

CONCLUSIONS : High-value prediction model was created to estimate in-hospital mortality for COVID-19 patients treated with steroid and remdesivir. This article is protected by copyright. All rights reserved.

Kuno Toshiki, Sahashi Yuki, Kawahito Shinpei, Takahashi Mai, Iwagami Masao, Egorova Natalia N

2021-Oct-14

COVID-19, New York, machine learning, mortality, remdesivir, steroid

General General

Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence

bioRxiv Preprint

As the COVID-19 pandemic continues, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Variant B.1.1.7 lineage clade GR from Global Initiative on Sharing All Influenza Data (GISAID) was first detected in the UK, and it appears to possess an increased transmissibility. At the same time, South African authorities reported variant B.1.351, that shares several mutations with B.1.1.7, and might also present high transmissibility. Earlier this year, a variant labelled P.1 with 17 non-synonymous mutations was detected in Brazil. Recently the World Health Organization has raised concern for the variants B.1.617.2 mainly detected in India but now exported worldwide. It is paramount to rapidly develop specific molecular tests to uniquely identify new variants. Using a completely automated pipeline built around deep learning and evolutionary algorithms techniques, we designed primer sets specific to variants B.1.1.7, B.1.351, P.1 and B.1.617.2 respectively. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for each variant. In-silico tests show that the sequences in the primer sets present high accuracy and are based on 2 mutations or more. In addition, we present an analysis of key mutations for SARS-CoV-2 variants. Finally, we tested the designed primers for B.1.1.7 using RT-PCR. The presented methodology can be exploited to swiftly obtain primer sets for each new variant, that can later be a part of a multiplexed approach for the initial diagnosis of COVID-19 patients.

Perez-Romero, C.; Tonda, A.; Mendoza-Maldonado, L.; Coz, E.; Tabeling, P.; Vanhomwegen, J.; Claassen, E.; Garssen, J.; Kraneveld, A. D.; Lopez-Rincon, A.

2021-10-15

General General

A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality.

In Applied soft computing

The COVID-19 epidemic has had a great adverse impact on the world, having taken a heavy toll, killing hundreds of thousands of people. In order to help the world better combat COVID-19 and reduce its death toll, this study focuses on the COVID-19 mortality. First, using the multiple stepwise regression analysis method, the factors from eight aspects (economy, society, climate etc.) that may affect the mortality rates of COVID-19 in various countries is examined. In addition, a two-layer nested heterogeneous ensemble learning-based prediction method that combines linear regression (LR), support vector machine (SVM), and extreme learning machine (ELM) is developed to predict the development trends of COVID-19 mortality in various countries. Based on data from 79 countries, the experiment proves that age structure (proportion of the population over 70 years old) and medical resources (number of beds) are the main factors affecting the mortality of COVID-19 in each country. In addition, it is found that the number of nucleic acid tests and climatic factors are correlated with COVID-19 mortality. At the same time, when predicting COVID-19 mortality, the proposed heterogeneous ensemble learning-based prediction method shows better prediction ability than state-of-the-art machine learning methods such as LR, SVM, ELM, random forest (RF), long short-term memory (LSTM) etc.

Cui Shaoze, Wang Yanzhang, Wang Dujuan, Sai Qian, Huang Ziheng, Cheng T C E

2021-Dec

COVID-19, Ensemble learning, Hybrid method, Mortality, Stepwise multiple regression, Time series prediction

General General

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

bioRxiv Preprint

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

Coutinho, G. G. F.; Camara, G. B. M.; Barbosa, R. d. M.; Fernandes, M. A. C.

2021-10-15

General General

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier.

In Arabian journal for science and engineering

COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation-Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.

Saygılı Ahmet

2021-Oct-07

COVID-19, Classification, Expectation–Maximization, GMM, Segmentation

General General

The impact of machine learning on UK financial services.

In Oxford review of economic policy

Machine learning is an increasingly key influence on the financial services industry. In this paper, we review the roles and impact of machine learning (ML) and artificial intelligence (AI) on the UK financial services industry. We survey the current AI/ML landscape in the UK. ML has had a considerable impact in the areas of fraud and compliance, credit scoring, financial distress prediction, robo-advising and algorithmic trading. We examine these applications using UK examples. We also review the importance of regulation and governance in ML applications to financial services. Finally, we assess the performance of ML during the Covid-19 pandemic and conclude with directions for future research.

Buchanan Bonnie G, Wright Danika

2021

AI, big data, financial services, machine learning

General General

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport- A machine learning approach.

In Chemical engineering journal (Lausanne, Switzerland : 1996)

Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250 μ m are investigated inside a bus. It is shown that humidity can profoundly affect the droplets distribution, such that 10% increase in relative humidity results in 30% increase in the droplets density at the farthest point from a sneezing passenger. Further, ventilation process is found to feature dual effects on the droplets distribution. Simple increases in the ventilation rate may accelerate the droplets transmission. However, carefully tailored injection of fresh air enhances deposition of droplets on the surfaces and thus reduces their concentration in the bus. Finally, the analysis identifies an optimal range of temperature, humidity and ventilation rate to maintain human comfort while minimising the transmission of droplets.

Mesgarpour Mehrdad, Abad Javad Mohebbi Najm, Alizadeh Rasool, Wongwises Somchai, Doranehgard Mohammad Hossein, Jowkar Saeed, Karimi Nader

2021-Oct-07

COVID-19, Computational Fluid Dynamics, Droplet Suspension, Droplets distribution, Machine learning, Prediction Models

General General

A sustainable-resilience healthcare network for handling COVID-19 pandemic.

In Annals of operations research

** : In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between - 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health.

Supplementary Information : The online version contains supplementary material available at 10.1007/s10479-021-04238-2.

Goodarzian Fariba, Ghasemi Peiman, Gunasekaren Angappa, Taleizadeh Ata Allah, Abraham Ajith

2021-Oct-07

COVID-19 pandemic, Healthcare network, Heuristics, Resiliency, Sustainability

General General

Deep neural networks ensemble to detect COVID-19 from CT scans.

In Pattern recognition

Research on Coronavirus Disease 2019 (COVID-19) detection methods has increased in the last months as more accurate automated toolkits are required. Recent studies show that CT scan images contain useful information to detect the COVID-19 disease. However, the scarcity of large and well balanced datasets limits the possibility of using detection approaches in real diagnostic contexts as they are unable to generalize. Indeed, the performance of these models quickly becomes inadequate when applied to samples captured in different contexts (e.g., different equipment or populations) from those used in the training phase. In this paper, a novel ensemble-based approach for more accurate COVID-19 disease detection using CT scan images is proposed. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet) evolved with a genetic algorithm, combined into an ensemble architecture for the classification of clustered images of lung lobes. The study is validated on a new dataset obtained as an integration of existing ones. The results of the experimental evaluation show that the ensemble classifier ensures effective performance, also exhibiting better generalization capabilities.

Aversano Lerina, Bernardi Mario Luca, Cimitile Marta, Pecori Riccardo

2021-Dec

COVID-19, CT Scan images, Coronavirus, Deep learning

Cardiology Cardiology

Estimated pulse wave velocity improves risk stratification for all-cause mortality in patients with COVID-19.

In Scientific reports ; h5-index 158.0

Accurate risk stratification in COVID-19 patients consists a major clinical need to guide therapeutic strategies. We sought to evaluate the prognostic role of estimated pulse wave velocity (ePWV), a marker of arterial stiffness which reflects overall arterial integrity and aging, in risk stratification of hospitalized patients with COVID-19. This retrospective, longitudinal cohort study, analyzed a total population of 1671 subjects consisting of 737 hospitalized COVID-19 patients consecutively recruited from two tertiary centers (Newcastle cohort: n = 471 and Pisa cohort: n = 266) and a non-COVID control cohort (n = 934). Arterial stiffness was calculated using validated formulae for ePWV. ePWV progressively increased across the control group, COVID-19 survivors and deceased patients (adjusted mean increase per group 1.89 m/s, P < 0.001). Using a machine learning approach, ePWV provided incremental prognostic value and improved reclassification for mortality over the core model including age, sex and comorbidities [AUC (core model + ePWV vs. core model) = 0.864 vs. 0.755]. ePWV provided similar prognostic value when pulse pressure or hs-Troponin were added to the core model or over its components including age and mean blood pressure (p < 0.05 for all). The optimal prognostic ePWV value was 13.0 m/s. ePWV conferred additive discrimination (AUC: 0.817 versus 0.779, P < 0.001) and reclassification value (NRI = 0.381, P < 0.001) over the 4C Mortality score, a validated score for predicting mortality in COVID-19 and the Charlson comorbidity index. We suggest that calculation of ePWV, a readily applicable estimation of arterial stiffness, may serve as an additional clinical tool to refine risk stratification of hospitalized patients with COVID-19 beyond established risk factors and scores.

Stamatelopoulos Kimon, Georgiopoulos Georgios, Baker Kenneth F, Tiseo Giusy, Delialis Dimitrios, Lazaridis Charalampos, Barbieri Greta, Masi Stefano, Vlachogiannis Nikolaos I, Sopova Kateryna, Mengozzi Alessandro, Ghiadoni Lorenzo, van der Loeff Ina Schim, Hanrath Aidan T, Ajdini Bajram, Vlachopoulos Charalambos, Dimopoulos Meletios A, Duncan Christopher J A, Falcone Marco, Stellos Konstantinos

2021-Oct-12

General General

COVID-19 Vaccine Hesitancy in the Month Following the Start of the Vaccination Process.

In International journal of environmental research and public health ; h5-index 73.0

The occurrence of the novel coronavirus has changed a series of aspects related to people's everyday life, the negative effects being felt all around the world. In this context, the production of a vaccine in a short period of time has been of great importance. On the other hand, obtaining a vaccine in such a short time has increased vaccine hesitancy and has activated anti-vaccination speeches. In this context, the aim of the paper is to analyze the dynamics of public opinion on Twitter in the first month after the start of the vaccination process in the UK, with a focus on COVID-19 vaccine hesitancy messages. For this purpose, a dataset containing 5,030,866 tweets in English was collected from Twitter between 8 December 2020-7 January 2021. A stance analysis was conducted after comparing several classical machine learning and deep learning algorithms. The tweets associated to COVID-19 vaccination hesitancy were examined in connection with the major events in the analyzed period, while the main discussion topics were determined using hashtags, n-grams and latent Dirichlet allocation. The results of the study can help the interested parties better address the COVID-19 vaccine hesitancy concerns.

Cotfas Liviu-Adrian, Delcea Camelia, Gherai Rareș

2021-Oct-04

COVID-19 vaccination, natural language processing, opinion mining, stance analysis, vaccine, vaccine hesitancy

Public Health Public Health

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach.

In International journal of environmental research and public health ; h5-index 73.0

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning's contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures -InceptionV3, ResNet50, and VGG19-on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.

Awan Mazhar Javed, Bilal Muhammad Haseeb, Yasin Awais, Nobanee Haitham, Khan Nabeel Sabir, Zain Azlan Mohd

2021-Sep-27

Apache Spark, CNN, COVID-19, InceptionV3, ResNet50, SparkDL, VGG19, big data, chest X-ray, corona virus, data bricks, deep learning, machine learning, pneumonia, public health, transfer learning

General General

Untargeted Metabolic Profiling of Extracellular Vesicles of SARS-CoV-2-Infected Patients Shows Presence of Potent Anti-Inflammatory Metabolites.

In International journal of molecular sciences ; h5-index 102.0

Extracellular vesicles (EVs) carry important biomolecules, including metabolites, and contribute to the spread and pathogenesis of some viruses. However, to date, limited data are available on EV metabolite content that might play a crucial role during infection with the SARS-CoV-2 virus. Therefore, this study aimed to perform untargeted metabolomics to identify key metabolites and associated pathways that are present in EVs, isolated from the serum of COVID-19 patients. The results showed the presence of antivirals and antibiotics such as Foscarnet, Indinavir, and lymecycline in EVs from patients treated with these drugs. Moreover, increased levels of anti-inflammatory metabolites such as LysoPS, 7-α,25-Dihydroxycholesterol, and 15-d-PGJ2 were detected in EVs from COVID-19 patients when compared with controls. Further, we found decreased levels of metabolites associated with coagulation, such as thromboxane and elaidic acid, in EVs from COVID-19 patients. These findings suggest that EVs not only carry active drug molecules but also anti-inflammatory metabolites, clearly suggesting that exosomes might play a crucial role in negotiating with heightened inflammation during COVID-19 infection. These preliminary results could also pave the way for the identification of novel metabolites that might act as critical regulators of inflammatory pathways during viral infections.

Alzahrani Faisal A, Shait Mohammed Mohammed Razeeth, Alkarim Saleh, Azhar Esam I, El-Magd Mohammed A, Hawsawi Yousef, Abdulaal Wesam H, Yusuf Abdulaziz, Alhatmi Abdulaziz, Albiheyri Raed, Fakhurji Burhan, Kurdi Bassem, Madani Tariq A, Alguridi Hassan, Alosaimi Roaa S, Khan Mohammad Imran

2021-Sep-28

15-d-PGJ2, 7-α,25-Dihydroxycholesterol, COVID-19, extracellular vesicles, metabolomics

Radiology Radiology

Making Document-Level Information Extraction Right for the Right Reasons

ArXiv Preprint

Document-level information extraction is a flexible framework compatible with applications where information is not necessarily localized in a single sentence. For example, key features of a diagnosis in radiology a report may not be explicitly stated, but nevertheless can be inferred from the report's text. However, document-level neural models can easily learn spurious correlations from irrelevant information. This work studies how to ensure that these models make correct inferences from complex text and make those inferences in an auditable way: beyond just being right, are these models "right for the right reasons?" We experiment with post-hoc evidence extraction in a predict-select-verify framework using feature attribution techniques. While this basic approach can extract reasonable evidence, it can be regularized with small amounts of evidence supervision during training, which substantially improves the quality of extracted evidence. We evaluate on two domains: a small-scale labeled dataset of brain MRI reports and a large-scale modified version of DocRED (Yao et al., 2019) and show that models' plausibility can be improved with no loss in accuracy.

Liyan Tang, Dhruv Rajan, Suyash Mohan, Abhijeet Pradhan, R. Nick Bryan, Greg Durrett

2021-10-14

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 medRxiv : the preprint server for health sciences

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-CXoV-2 infections. In this study, we describe and compare two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, R t Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, R t. The second method, ML+ R t , 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+R t method and 0.867 for the R t Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+R t method outperforms the R t Only method in identifying larger spikes. We also find that both methods perform adequately in both rural and non-rural predictions. Finally, we provide a detailed discussion on practical issues regarding implementing forecasting models for public health action based on R t , and the potential for further development of machine learning methods that are enhanced by R t.

Price Bradley S, Khodaverdi Maryam, Halasz Adam, Hendricks Brian, Kimble Wesley, Smith Gordon S, Hodder Sally L

2021-Oct-07

General General

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model.

In microLife

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.

Campos M, Sempere J M, Galán J C, Moya A, Llorens C, de-Los-Angeles C, Baquero-Artigao F, Cantón R, Baquero F

2021

COVID-19, interventions, membrane computing, modeling

General General

Improving classification of low-resource COVID-19 literature by using Named Entity Recognition.

In Genomics & informatics

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.

Lithgow-Serrano Oscar, Cornelius Joseph, Kanjirangat Vani, Méndez-Cruz Carlos-Francisco, Rinaldi Fabio

2021-Sep

COVID-19, NLP, Named Entity Recognition, classification

General General

Conditional GAN based augmentation for predictive modeling of respiratory signals.

In Computers in biology and medicine

Respiratory illness is the primary cause of mortality and impairment in the life span of an individual in the current COVID-19 pandemic scenario. The inability to inhale and exhale is one of the difficult conditions for a person suffering from respiratory disorders. Unfortunately, the diagnosis of respiratory disorders with the presently available imaging and auditory screening modalities are sub-optimal and the accuracy of diagnosis varies with different medical experts. At present, deep neural nets demand a massive amount of data suitable for precise models. In reality, the respiratory data set is quite limited, and therefore, data augmentation (DA) is employed to enlarge the data set. In this study, conditional generative adversarial networks (cGAN) based DA is utilized for synthetic generation of signals. The publicly available repository such as ICBHI 2017 challenge, RALE and Think Labs Lung Sounds Library are considered for classifying the respiratory signals. To assess the efficacy of the artificially created signals by the DA approach, similarity measures are calculated between original and augmented signals. After that, to quantify the performance of augmentation in classification, scalogram representation of generated signals are fed as input to different pre-trained deep learning architectures viz Alexnet, GoogLeNet and ResNet-50. The experimental results are computed and performance results are compared with existing classical approaches of augmentation. The research findings conclude that the proposed cGAN method of augmentation provides better accuracy of 92.50% and 92.68%, respectively for both the two data sets using ResNet 50 model.

Jayalakshmy S, Sudha Gnanou Florence

2021-Oct-08

Conditional generative adversarial networks, Correlation metrics, Data augmentation, Deep convolutional neural networks, Respiratory signals

General General

Mix-and-Interpolate: A Training Strategy to Deal with Source-biased Medical Data.

In IEEE journal of biomedical and health informatics

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) has reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

Li Yuexiang, Chen Jiawei, Dong Wei, Zhu Yanchun, Wu Jianrong, Xiong Junfeng, Gang Yadong, Sun Wenbo, Xu Haibo, Qian Tianyi, Ma Kai, Zheng Yefeng

2021-Oct-12

General General

Computational investigation of drug bank compounds against 3C-like protease (3CLpro) of SARS-CoV-2 using deep learning and molecular dynamics simulation.

In Molecular diversity

Blocking the main replicating enzyme, 3 Chymotrypsin-like protease (3CLpro) is the most promising drug development strategy against the SARS-CoV-2 virus, responsible for the current COVID-19 pandemic. In the present work, 9101 drugs obtained from the drug bank database were screened against SARS-CoV-2 3CLpro prosing deep learning, molecular docking, and molecular dynamics simulation techniques. In the initial stage, 500 drug-screened by deep learning regression model and subjected to molecular docking that resulted in 10 screened compounds with strong binding affinity. Further, five compounds were checked for their binding potential by analyzing molecular dynamics simulation for 100 ns at 300 K. In the final stage, two compounds {4-[(2s,4e)-2-(1,3-Benzothiazol-2-Yl)-2-(1h-1,2,3-Benzotriazol-1-Yl)-5-Phenylpent-4-Enyl]Phenyl}(Difluoro)Methylphosphonic Acid and 1-(3-(2,4-dimethylthiazol-5-yl)-4-oxo-2,4-dihydroindeno[1,2-c]pyrazol-5-yl)-3-(4-methylpiperazin-1-yl)urea were screened as potential hits by analyzing several parameters like RMSD, Rg, RMSF, MMPBSA, and SASA. Thus, our study suggests two potential drugs that can be tested in the experimental conditions to evaluate the efficacy against SARS-CoV-2. Further, such drugs could be modified to develop more potent drugs against COVID-19.

Joshi Tushar, Sharma Priyanka, Mathpal Shalini, Joshi Tanuja, Maiti Priyanka, Nand Mahesha, Pande Veena, Chandra Subhash

2021-Oct-12

3CLpro, COVID-19, Deep learning, Drug bank database, Drug repurposing, Molecular dynamics

General General

Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning.

In Scientific reports ; h5-index 158.0

Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.

Nogueira Marcelo Saito, Leal Leonardo Barbosa, Macarini Wena, Pimentel Raquel Lemos, Muller Matheus, Vassallo Paula Frizera, Campos Luciene Cristina Gastalho, Dos Santos Leonardo, Luiz Wilson Barros, Mill José Geraldo, Barauna Valerio Garrone, de Carvalho Luis Felipe das Chagas E Silva

2021-10-11

Radiology Radiology

Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic.

In World journal of radiology

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence via radiomics is evaluated. Lastly, the lesson from the first wave and preparation for the second wave from the point of view of radiology are summarized.

Pal Aman, Ali Abulhassan, Young Timothy R, Oostenbrink Juan, Prabhakar Akul, Prabhakar Amogh, Deacon Nina, Arnold Amar, Eltayeb Ahmed, Yap Charles, Young David M, Tang Alan, Lakshmanan Subramanian, Lim Ying Yi, Pokarowski Martha, Kakodkar Pramath

2021-Sep-28

Brixia score, COVID-19, Computed tomography, Coronavirus, MuLBSTA Scoring system, Radiological Assessment of Lung Edema classification, Ultrasound

General General

A CNN based Handwritten Numeral Recognition Model for Four Arithmetic Operations.

In Procedia computer science

The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition. The CNN is used to recognize four arithmetic operations in mathematical questions consisting of addition, deduction, multiplication and division. The performance CNN in handwritten numeral recognition have been optimized in terms of activation function and gradient descent algorithm. The proposed CNN is also trained and tested with the MNIST handwritten data set. The experimental results show that the recognition accuracy the improved CNN improves to a certain extent as compared to before optimization.

ShanWei Chen, LiWang Shir, Foo Ng Theam, Ramli Dzati Athiar

2021

CNN, deep learning, handwritten numeral recognition, image processing

Surgery Surgery

Architecture and organization of a Platform for diagnostics, therapy and post-covid complications using AI and mobile monitoring.

In Procedia computer science

Infectious diseases accompanied mankind throughout its existence. However, in the 20th century, with the implementation od mass vaccination, this problem was partially forgotten. It reappeared at the end of the 2019 with the COVID-19 pandemic. The diseases are associated with high mortality, the main causes of which are: respiratory failure, acute respiratory distress syndrome, thrombotic complications, etc. As many centuries ago, the key to fighting a pandemic is to diagnose patients with infections as quickly as possible, isolate them, and implement treatment procedures. In this paper we propose a Platform supporting medics in the fight against epidemic. Unlike alternative systems, the proposed IT Platform will ultimately cover all areas of fighting against COVID-19, from the diagnosis of infection, through treatment, to rehabilitation of post-disease complications. Like most clinical information systems, the Platform is based on Artificial Intelligence, in particular Federated Learning. Also, unlike known solutions, it uses all available historical data of the patient's health and information from real-time mobile diagnostics, using cellular communication and Internet of Things solutions. Such solutions could be helpful in fighting against any future mass infections.

Hajder Miroslaw, Hajder Piotr, Gil Tomasz, Krzywda Maciej, Kolbusz Janusz, Liput Mateusz

2021

AI-based decision making, COVID-19, Federated Learning, Medical Platform System

General General

Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example.

In Frontiers in plant science

Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants.

Yue JiaQi, Li WanYi, Wang YuanZhong

2021

Paris polyphylla var. yunnanensis, ResNet, deep learning, identification research, medicinal plant, superiority verification

General General

Student's Learning Strategies and Academic Emotions: Their Influence on Learning Satisfaction During the COVID-19 Pandemic.

In Frontiers in psychology ; h5-index 92.0

Background: Based on the control-value theory (CVT), learning strategies and academic emotions are closely related to learning achievement, and have been considered as important factors influencing student's learning satisfaction and learning performance in the online learning context. However, only a few studies have focused on the influence of learning strategies on academic emotions and the interaction of learning strategies with behavioral engagement and social interaction on learning satisfaction. Methods: The participants were 363 pre-service teachers in China, and we used structural equation modeling (SEM) to analyze the mediating and moderating effects of the data. Results: The main findings of the current study showed that learning strategies influence students' online learning satisfaction through academic emotions. The interaction between learning strategies and behavioral engagement was also an important factor influencing online learning satisfaction. Conclusions: We explored the internal mechanism and boundary conditions of how learning strategies influenced learning satisfaction to provide intellectual guarantee and theoretical support for the online teaching design and online learning platform. This study provides theoretical contributions to the CVT and practical value for massive open online courses (MOOCs), flipped classrooms and blended learning in the future.

Wu Changcheng, Jing Bin, Gong Xue, Mou Ya, Li Junyi

2021

academic emotion, behavioral engagement, learning satisfaction, learning strategy, online learning, social interaction

Public Health Public Health

A deep learning ensemble approach to prioritize candidate drugs against novel coronavirus 2019-nCoV/SARS-CoV-2.

In Applied soft computing

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 havegenerated an utmost need torealize promising therapeutic strategiesto fight the pandemic. Drug repurposing-an efficientdrug discovery technique from approved drugs is an emerging tacticto face the immediate global challenge.It offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus-drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

K Deepthi, A S Jereesh, Liu Yuansheng

2021-Oct-06

COVID-19 drug repurposing, Convolutional neural network, Deep learning, SARS-coV-2, XGBoost

General General

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review.

In Robotics and autonomous systems

The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

Sarker Sujan, Jamal Lafifa, Ahmed Syeda Faiza, Irtisam Niloy

2021-Oct-06

Artificial intelligence, Autonomous system, COVID-19, Healthcare, Robotics, SARS-coV-2

General General

Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic.

In Energy (Oxford, England)

Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak.

Wu Binrong, Wang Lin, Wang Sirui, Zeng Yu-Rong

2021-Jul-01

COVID-19 pandemic, Deep learning, Social media information, Text mining, Time series forecasting

General General

Face Mask Recognition from Audio: The MASC Database and an Overview on the Mask Challenge.

In Pattern recognition

The sudden outbreak of COVID-19 has resulted in tough challenges for the field of biometrics due to its spread via physical contact, and the regulations of wearing face masks. Given these constraints, voice biometrics can offer a suitable contact-less biometric solution; they can benefit from models that classify whether a speaker is wearing a mask or not. This article reviews the Mask Sub-Challenge (MSC) of the INTERSPEECH 2020 COMputational PARalinguistics challengE (ComParE), which focused on the following classification task: Given an audio chunk of a speaker, classify whether the speaker is wearing a mask or not. First, we report the collection of the Mask Augsburg Speech Corpus (MASC) and the baseline approaches used to solve the problem, achieving a performance of 71.8 % Unweighted Average Recall (UAR). We then summarise the methodologies explored in the submitted and accepted papers that mainly used two common patterns: (i) phonetic-based audio features, or (ii) spectrogram representations of audio combined with Convolutional Neural Networks (CNNs) typically used in image processing. Most approaches enhance their models by adapting ensembles of different models and attempting to increase the size of the training data using various techniques. We review and discuss the results of the participants of this sub-challenge, where the winner scored a UAR of 80.1 % . Moreover, we present the results of fusing the approaches, leading to a UAR of 82.6 % . Finally, we present a smartphone app that can be used as a proof of concept demonstration to detect in real-time whether users are wearing a face mask; we also benchmark the run-time of the best models.

Mohamed Mostafa M, Nessiem Mina A, Batliner Anton, Bergler Christian, Hantke Simone, Schmitt Maximilian, Baird Alice, Mallol-Ragolta Adria, Karas Vincent, Amiriparian Shahin, Schuller Björn W

2021-Oct-04

Acoustic modelling, COVID-19, Deep Learning, Masks, Voice biometrics

General General

Mental health and resilience during the coronavirus pandemic: A machine learning approach.

In Journal of clinical psychology

OBJECTIVE : This study explored risk and resilience factors of mental health functioning during the coronavirus disease (COVID-19) pandemic.

METHODS : A sample of 467 adults (M age = 33.14, 63.6% female) reported on mental health (depression, anxiety, posttraumatic stress disorder [PTSD], and somatic symptoms), demands and impacts of COVID-19, resources (e.g., social support, health care access), demographics, and psychosocial resilience factors.

RESULTS : Depression, anxiety, and PTSD rates were 44%, 36%, and 23%, respectively. Supervised machine learning models identified psychosocial factors as the primary significant predictors across outcomes. Greater trauma coping self-efficacy and forward-focused coping, but not trauma-focused coping, were associated with better mental health. When accounting for psychosocial resilience factors, few external resources and demographic variables emerged as significant predictors.

CONCLUSION : With ongoing stressors and traumas, employing coping strategies that emphasize distraction over trauma processing may be warranted. Clinical and community outreach efforts should target trauma coping self-efficacy to bolster resilience during a pandemic.

Samuelson Kristin W, Dixon Kelly, Jordan Joshua T, Powers Tyler, Sonderman Samantha, Brickman Sophie

2021-Oct-11

COVID-19, PTSD, anxiety, coping self-efficacy, depression, trauma

Radiology Radiology

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

In JAMA network open

Importance : Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.

Objectives : To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups.

Design, Setting, and Participants : This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries.

Main Outcomes and Measures : Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated.

Results : In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger.

Conclusions and Relevance : In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.

Yan Yao, Schaffter Thomas, Bergquist Timothy, Yu Thomas, Prosser Justin, Aydin Zafer, Jabeer Amhar, Brugere Ivan, Gao Jifan, Chen Guanhua, Causey Jason, Yao Yuxin, Bryson Kevin, Long Dustin R, Jarvik Jeffrey G, Lee Christoph I, Wilcox Adam, Guinney Justin, Mooney Sean

2021-Oct-01

Public Health Public Health

The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model.

In Work (Reading, Mass.)

BACKGROUND : In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation.

OBJECTIVE : The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran.

METHOD : The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed.

RESULTS : The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing.

CONCLUSION : This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable. Thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

Jamshidnezhad Amir, Hosseini Seyed Ahmad, Ghavamabadi Leila Ibrahimi, Marashi Seyed Mahdi Hossaeini, Mousavi Hediye, Zilae Marzieh, Dehaghi Behzad Fouladi

2021-Oct-05

Artificial neural networks, coronavirus, environmental, relative humidity, temperature

Cardiology Cardiology

Integrated medical resource consumption stratification in hospitalized patients: an Auto Triage Management model based on accurate risk, cost and length of stay prediction.

In Science China. Life sciences

Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden analysis. Although progression risks have been extensively researched for numbers of diseases, other crucial indicators that reflect patients' economic and time costs have not been systematically studied. To address the problems, we developed an automatic deep learning based Auto Triage Management (ATM) Framework capable of accurately modelling patients' disease progression risk and health economic evaluation. Based on them, we can first discover the relationship between disease progression and medical system cost, find potential features that can more precisely aid patient triage in resource allocation, and allow treatment plan searching that has cured patients. Applying ATM in COVID-19, we built a joint model to predict patients' risk, the total length of stay (LoS) and cost when at-admission, and remaining LoS and cost at a given hospitalized time point, with C-index 0.930 and 0.869 for risk prediction, mean absolute error (MAE) of 5.61 and 5.90 days for total LoS prediction in internal and external validation data.

Zhong Qin, Li Zongren, Wang Wenjun, Zhang Lei, He Kunlun

2021-Oct-08

AutoML, electronic medical records, patient triage

General General

Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine Learning.

In Frontiers in medicine

Background: The outbreak of COVID-19 attracted the attention of the whole world. Our study aimed to explore the predictors for the survival of patients with COVID-19 by machine learning. Methods: We conducted a retrospective analysis and used the idea of machine learning to train the data of COVID-19 patients in Leishenshan Hospital through the logical regression algorithm provided by scikit-learn. Results: Of 2010 patients, 42 deaths were recorded until March 29, 2020. The mortality rate was 2.09%. There were 6,812 records after data features combination and data arrangement, 3,025 records with high-quality after deleting incomplete data by manual checking, and 5,738 records after data balancing finally by the method of Borderline-1 Smote. The results of 10 times of data training by logistic regression model showed that albumin, saturation of pulse oxygen at admission, alanine aminotransferase, and percentage of neutrophils were possibly associated with the survival of patients. The results of 10 times of data training including age, sex, and height beyond the laboratory measurements showed that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients. The rates of precision, recall, and f1-score of the two training models were all higher than 0.9 and relatively stable. Conclusions: We demonstrated that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients with COVID-19.

Zhao Yongfeng, Chen Qianjun, Liu Tao, Luo Ping, Zhou Yi, Liu Minghui, Xiong Bei, Zhou Fuling

2021

Borderline-Smote, COVID-19, SARS-CoV-2, machine learning, survival

General General

Corrigendum: Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada.

In Frontiers in public health

[This corrects the article DOI: 10.3389/fpubh.2021.675766.].

Snider Brett, McBean Edward A, Yawney John, Gadsden S Andrew, Patel Bhumi

2021

COVID-19, SHapley, XGBoost, artificial intelligence, mortality

General General

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

bioRxiv Preprint

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Palukuri, M. V.; Marcotte, E. M.

2021-10-11

General General

BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning

ArXiv Preprint

Social distancing, an essential public health measure to limit the spread of contagious diseases, has gained significant attention since the outbreak of the COVID-19 pandemic. In this work, the problem of visual social distancing compliance assessment in busy public areas, with wide field-of-view cameras, is considered. A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced, and several measures for the evaluation of social distance detection systems are proposed. A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated. BEV-Net combines detection of head and feet locations, camera pose estimation, a differentiable homography module to map image into BEV coordinates, and geometric reasoning to produce a BEV map of the people locations in the scene. Experiments on complex crowded scenes demonstrate the power of the approach and show superior performance over baselines derived from methods in the literature. Applications of interest for public health decision makers are finally discussed. Datasets, code and pretrained models are publicly available at GitHub.

Zhirui Dai, Yuepeng Jiang, Yi Li, Bo Liu, Antoni B. Chan, Nuno Vasconcelos

2021-10-10

General General

COVID-19 screening using breath-borne volatile organic compounds.

In Journal of breath research

Rapid screening of COVID-19 is key to controlling the pandemic. However, current nucleic acid amplification involves lengthy procedures in addition to discomfort of taking throat/nasal swabs. Here we describe potential breath-borne volatile organic compound (VOC) biomarkers together with machine learning that can be used for point-of-care screening of COVID-19. Using a commercial gas chromatograph-ion mobility spectrometer (GC-IMS), higher levels of propanol were detected in exhaled breath of COVID-19 patients (N=74) and non-COVID-19 respiratory infections (RI) (N=30) than those of non-COVID-19 controls (NC) / health care workers (HCW) (N=87), and backgrounds (N=87). In contrast, breath-borne acetone was found to be significantly lower for COVID-19 patients than other subjects. Twelve key endogenous VOC species using supervised machine learning models (Support Vector Machines (SVM), Gradient Boosting Machines (GBM) and Random Forests) were shown to exhibit strong capabilities in discriminating COVID-19 from (HCW+NC) and RI with a precision ranging from 91% to 100%. GBM and Random Forests models can also discriminate RI patients from healthy subjects with a precision of 100%. In addition, the developed models using breath-borne VOC could also detect a confirmed COVID-19 patient but with a false negative throat swab PCR test. It takes ten minutes to allow an entire breath test to finish, including analysis of the 12 key VOC species. The developed technology provides a novel concept for non-invasive rapid point-of-care-test (POCT) screening for COVID-19 in various scenarios. Keywords: COVID-19, Exhaled Breath, Biomarkers, Volatile Organic Compounds (VOCs), Propanol, Acetone, Machine Learning.

Chen Haoxuan, Qi Xiao, Zhang Lu, Li Xinyue, Ma Jianxin, Zhang Chunyang, Feng Huasong, Yao Maosheng

2021-Oct-08

Acetone, Biomarkers, COVID-19, Exhaled Breath, Machine Learning, Propanol, Volatile Organic Compounds (VOCs)

Radiology Radiology

The performance of artificial intelligence supported Thoracic CT to evaluate the radiologic improvement in patients with COVID-19 pneumonia: comparision pirfenidon vs. corticosteroid.

In International journal of clinical practice

AIM : We aimed to investigate the effect of short-term pirfenidone treatment on prolonged COVID-19 pneumonia.

METHOD : Hospital files of patients hospitalized with a diagnosis of critical COVID-19 pneumonia between November 2020 and March 2021 were retrospectively reviewed. Chest computed tomography images taken both before treatment and 2 months after treatment, demographic characteristics and laboratory parameters of patients receiving pirfenidone+methylprednisolone (n=13) and only methylprednisolones (n=9) were recorded. Pulmonary function tests were performed after the second month of the treatment. CT involvement rates were determined by machine learning.

RESULTS : A total of 22 patients, 13 of whom (59.1%) were using methylprednisolone + pirfenidone and 9 of whom (40.9%) were using only methylprednisolone were included. When the blood gas parameters and pulmonary function tests of the patients were compared at the end of the second month, it was found that the FEV1, FEV1%, FVC, and FVC% values were statistically significantly higher in the methylprednisolone + pirfenidone group compared to the methylprednisolone group (p=0.025, p=0.012, p=0.026, and p=0.017, respectively). When the rates of change in CT scans at diagnosis and second month of treatment were examined, it was found that the involvement rates in the methylprednisolone + pirfenidone group were statistically significantly decreased (p<0.001).

CONCLUSION : Antifibrotic agents can reduce fibrosis that may develop in the future. These can also help dose reduction and/or non-use strategy for methylprednisolone therapy, which has many side effects. Further large series and randomized controlled studies are needed on this subject.

Acat Murat, Yildiz Gulhan Pinar, Oner Serkan, Turan Muhammed Kamil

2021-Oct-08

COVID-19, computed tomography, fibrosis, machine learning algorithms, pirfenidone

Pathology Pathology

Silent SARS-CoV-2 Infections, Waning Immunity, Serology Testing, and COVID-19 Vaccination: A Perspective.

In Frontiers in immunology ; h5-index 100.0

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus causes a spectrum of clinical manifestations, ranging from asymptomatic to mild, moderate, or severe illness with multi-organ failure and death. Using a new machine learning algorithm developed by us, we have reported a significantly higher number of predicted COVID-19 cases than the documented counts across the world. The sole reliance on confirmed symptomatic cases overlooking the symptomless COVID-19 infections and the dynamics of waning immunity may not provide 'true' spectrum of infection proportion, a key element for an effective planning and implementation of protection and prevention strategies. We and others have previously shown that strategic orthogonal testing and leveraging systematic data-driven modeling approach to account for asymptomatics and waning cases may situationally have a compelling role in informing efficient vaccination strategies beyond prevalence reporting. However, currently Centers for Disease Control and Prevention (CDC) does not recommend serological testing either before or after vaccination to assess immune status. Given the 27% occurrence of breakthrough infections in fully vaccinated (FV) group with many being asymptomatics and still a larger fraction of the general mass remaining unvaccinated, the relaxed mask mandate and distancing by CDC can drive resurgence. Thus, we believe it is a key time to focus on asymptomatics (no symptoms) and oligosymptomatics (so mild that the symptoms remain unrecognized) as they can be silent reservoirs to propagate the infection. This perspective thus highlights the need for proactive efforts to reevaluate the current variables/strategies in accounting for symptomless and waning fractions.

Narasimhan Madhusudhanan, Mahimainathan Lenin, Noh Jungsik, Muthukumar Alagarraju

2021

COVID-19, asymptomatic, serology testing, vaccination, waning

Public Health Public Health

Electricity-consumption data reveals the economic impact and industry recovery during the pandemic.

In Scientific reports ; h5-index 158.0

Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.

Wang Xinlei, Si Caomingzhe, Gu Jinjin, Liu Guolong, Liu Wenxuan, Qiu Jing, Zhao Junhua

2021-Oct-07

General General

Intelligent health system for investigation and consenting COVID-19 patients and precision medicine.

In Personalized medicine

Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.

Ahmed Zeeshan

2021-Oct-08

COVID-19, artificial intelligence, data analytics, machine learning, patients recruitment

Radiology Radiology

Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images.

In PeerJ. Computer science

Coronavirus Disease 2019 (COVID-19) pandemic has been ferociously destroying global health and economics. According to World Health Organisation (WHO), until May 2021, more than one hundred million infected cases and 3.2 million deaths have been reported in over 200 countries. Unfortunately, the numbers are still on the rise. Therefore, scientists are making a significant effort in researching accurate, efficient diagnoses. Several studies advocating artificial intelligence proposed COVID diagnosis methods on lung images with high accuracy. Furthermore, some affected areas in the lung images can be detected accurately by segmentation methods. This work has considered state-of-the-art Convolutional Neural Network architectures, combined with the Unet family and Feature Pyramid Network (FPN) for COVID segmentation tasks on Computed Tomography (CT) scanner samples from the Italian Society of Medical and Interventional Radiology dataset. The experiments show that the decoder-based Unet family has reached the best (a mean Intersection Over Union (mIoU) of 0.9234, 0.9032 in dice score, and a recall of 0.9349) with a combination between SE ResNeXt and Unet++. The decoder with the Unet family obtained better COVID segmentation performance in comparison with Feature Pyramid Network. Furthermore, the proposed method outperforms recent segmentation state-of-the-art approaches such as the SegNet-based network, ADID-UNET, and A-SegNet + FTL. Therefore, it is expected to provide good segmentation visualizations of medical images.

Nguyen Hai Thanh, Bao Tran Toan, Luong Huong Hoang, Nguyen Huynh Tuan Khoi

2021

Computed tomography scanner, Covid diagnosis, Covid segmentation, Segmentation visualization

General General

A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations.

In PeerJ. Computer science

The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.

Kumar Swarn Avinash, Nasralla Moustafa M, García-Magariño Iván, Kumar Harsh

2021

Business intelligence, COVID-19, Decision support system, Machine learning, Pandemics, Sentiment analysis

General General

A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count.

In Computer methods and programs in biomedicine

BACKGROUND : As blood testing is radiation-free, low-cost and simple to operate, some researchers use machine learning to detect COVID-19 from blood test data. However, few studies take into consideration the imbalanced data distribution, which can impair the performance of a classifier.

METHOD : A novel combined dynamic ensemble selection (DES) method is proposed for imbalanced data to detect COVID-19 from complete blood count. This method combines data preprocessing and improved DES. Firstly, we use the hybrid synthetic minority over-sampling technique and edited nearest neighbor (SMOTE-ENN) to balance data and remove noise. Secondly, in order to improve the performance of DES, a novel hybrid multiple clustering and bagging classifier generation (HMCBCG) method is proposed to reinforce the diversity and local regional competence of candidate classifiers.

RESULTS : The experimental results based on three popular DES methods show that the performance of HMCBCG is better than only use bagging. HMCBCG+KNE obtains the best performance for COVID-19 screening with 99.81% accuracy, 99.86% F1, 99.78% G-mean and 99.81% AUC.

CONCLUSION : Compared to other advanced methods, our combined DES model can improve accuracy, G-mean, F1 and AUC of COVID-19 screening.

Wu Jiachao, Shen Jiang, Xu Man, Shao Minglai

2021-Sep-29

COVID-19 screening, Candidate classifier generation, Dynamic ensemble selection, Hybrid multiple clustering and bagging, Imbalanced data

General General

MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

In Structure (London, England : 1993)

The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances, and active-site boundaries are used for the analysis of active sites. The algorithms derived provide model equations that can predict whether changes in distances, such as contraction or expansion, will result in improved binding affinity. The algorithm is confirmed using kinetic studies of dihydrofolate reductase (DHFR), together with two DHFR-TS crystal structures. Empirical analyses of 881 crystal structures involving 180 ligands are used to interpret protein-ligand binding affinities. MANORAA links to major biological databases for web-based analysis of drug design. The frequency of atoms inside the main protease structures, including those from SARS-CoV-2, shows how the rigid part of the ligand can be used as a probe for molecular design (http://manoraa.org).

Tanramluk Duangrudee, Pakotiprapha Danaya, Phoochaijaroen Sakao, Chantravisut Pattra, Thampradid Sirikanya, Vanichtanankul Jarunee, Narupiyakul Lalita, Akavipat Ruj, Yuvaniyama Jirundon

2021-Oct-04

DHFR, SARS-CoV-2, binding affinity prediction from distance, drug design platform, influential distance, main protease, methotrexate, pocket design, structural conservation, trimethoprim

General General

Lessons learned from COVID-19 vaccination in Indonesia: experiences, challenges, and opportunities.

In Human vaccines & immunotherapeutics ; h5-index 43.0

The development of safe and effective COVID-19 vaccines as well as their delivery to people's arms are the best hope for ending the COVID-19 pandemic. However, the implementation of vaccination varies greatly across countries, with the developing countries lagging behind. This study investigates Indonesia's vaccination experiences, challenges, and acceleration over the course of implementation period. This study provides simulations to estimate the vaccination rate using time-series forecasting machine learning. We use Administrative data and Survey results in our analysis. Our findings suggest limited vaccine availability had caused low-coverage vaccination implementation in the early stage of vaccination implementation period. However, following the increased availability of vaccine, the vaccination rate accelerates up to 600% times. The government of Indonesia utilized strategic public places, public and private offices, and engaging private sectors in the phase two implementation to accelerate the vaccination implementation. Indonesia might reach 63.1 million individuals vaccinated at the end of March 2022, or 35% of the targeted population with up to April 2021 vaccination rate. To accelerate, government introduced a number of new strategies including door-to-door persuasion through neighborhood association (RT), educating individuals, and providing transportation from their home to the vaccination facility. We expect new strategies could further improve vaccination speed by around 1.4 million to 3.5 million individuals per day.

Arifin Bondi, Anas Titik

2021-Oct-06

COVID-19, vaccination acceleration, vaccines

Radiology Radiology

Radiology Implementation Considerations for Artificial Intelligence (AI) Applied to COVID-19, From the AJR Special Series on AI Applications.

In AJR. American journal of roentgenology

Hundreds of imaging-based artificial intelligence (AI) models have been developed in response to the COVID-19 pandemic. AI systems that incorporate imaging have shown promise in primary detection, severity grading, and prognostication of outcomes in COVID-19, and have enabled integration of imaging with a broad range of additional clinical and epidemiologic data. However, systematic reviews of AI models applied to COVID-19 medical imaging have highlighted problems in the field, including methodologic issues and problems in real-world deployment. Clinical use of such models should be informed by both the promise and potential pitfalls of implementation. How does a practicing radiologist make sense of this complex topic, and what factors should be considered in the implementation of AI tools for imaging of COVID-19? This critical review aims to help the radiologist understand the nuances that impact the clinical deployment of AI for imaging of COVID-19. We review imaging use cases for AI models in COVID-19 (e.g., diagnosis, severity assessment, and prognostication) and explore considerations for AI model development and testing, deployment infrastructure, clinical user interfaces, quality control, and institutional review board and regulatory approvals, with a practical focus on what a radiologist should consider when implementing an AI tool for COVID-19.

Li Matthew D, Chang Ken, Mei Xueyan, Bernheim Adam, Chung Michael, Steinberger Sharon R, Kalpathy-Cramer Jayashree, Little Brent P

2021-Oct-06

General General

Increased risk for COVID-19 breakthrough infection in fully vaccinated patients with substance use disorders in the United States between December 2020 and August 2021.

In World psychiatry : official journal of the World Psychiatric Association (WPA)

Individuals with substance use disorders (SUDs) are at increased risk for COVID-19 infection and for adverse outcomes of the infection. Though vaccines are highly effective against COVID-19, their effectiveness in individuals with SUDs might be curtailed by compromised immune status and a greater likelihood of exposures, added to the waning vaccine immunity and the new SARS-CoV-2 variants. In a population-based cohort study, we assessed the risk, time trends, outcomes and disparities of COVID-19 breakthrough infection in fully vaccinated SUD patients starting 14 days after completion of vaccination. The study included 579,372 individuals (30,183 with a diagnosis of SUD and 549,189 without such a diagnosis) who were fully vaccinated between December 2020 and August 2021, and had not contracted COVID-19 infection prior to vaccination. We used the TriNetX Analytics network platform to access de-identified electronic health records from 63 health care organizations in the US. Among SUD patients, the risk for breakthrough infection ranged from 6.8% for tobacco use disorder to 7.8% for cannabis use disorder, all significantly higher than the 3.6% in non-SUD population (p<0.001). Breakthrough infection risk remained significantly higher after controlling for demographics (age, gender, ethnicity) and vaccine types for all SUD subtypes, except for tobacco use disorder, and was highest for cocaine and cannabis use disorders (hazard ratio, HR=2.06, 95% CI: 1.30-3.25 for cocaine; HR=1.92, 95% CI: 1.39-2.66 for cannabis). When we matched SUD and non-SUD individuals for lifetime comorbidities and adverse socioeconomic determinants of health, the risk for breakthrough infection no longer differed between these populations, except for patients with cannabis use disorder, who remained at increased risk (HR=1.55, 95% CI: 1.22-1.99). The risk for breakthrough infection was higher in SUD patients who received the Pfizer than the Moderna vaccine (HR=1.49, 95% CI: 1.31-1.69). In the vaccinated SUD population, the risk for hospitalization was 22.5% for the breakthrough cohort and 1.6% for the non-breakthrough cohort (risk ratio, RR=14.4, 95% CI: 10.19-20.42), while the risk for death was 1.7% and 0.5% respectively (RR=3.5, 95% CI: 1.74-7.05). No significant age, gender and ethnic disparities for breakthrough infection were observed in vaccinated SUD patients. These data suggest that fully vaccinated SUD individuals are at higher risk for breakthrough COVID-19 infection, and this is largely due to their higher prevalence of comorbidities and adverse socioeconomic determinants of health compared with non-SUD individuals. The high frequency of comorbidities in SUD patients is also likely to contribute to their high rates of hospitalization and death following breakthrough infection.

Wang Lindsey, Wang QuanQiu, Davis Pamela B, Volkow Nora D, Xu Rong

2021-Oct-05

COVID-19 breakthrough infection, Substance use disorders, cannabis use disorder, cocaine use disorder, comorbidities, socioeconomic determinants of health, vaccination

Surgery Surgery

Effects of surgical masks on aerosol dispersion in professional singing.

In Journal of exposure science & environmental epidemiology ; h5-index 34.0

BACKGROUND : In the CoVID-19 pandemic, singing came into focus as a high-risk activity for the infection with airborne viruses and was therefore forbidden by many governmental administrations.

OBJECTIVE : The aim of this study is to investigate the effectiveness of surgical masks regarding the spatial and temporal dispersion of aerosol and droplets during professional singing.

METHODS : Ten professional singers performed a passage of the Ludwig van Beethoven's "Ode of Joy" in two experimental setups-each with and without surgical masks. First, they sang with previously inhaled vapor of e-cigarettes. The emitted cloud was recorded by three cameras to measure its dispersion dynamics. Secondly, the naturally expelled larger droplets were illuminated by a laser light sheet and recorded by a high-speed camera.

RESULTS : The exhaled vapor aerosols were decelerated and deflected by the mask and stayed in the singer's near-field around and above their heads. In contrast, without mask, the aerosols spread widely reaching distances up to 1.3 m. The larger droplets were reduced by up to 86% with a surgical mask worn.

SIGNIFICANCE : The study shows that surgical masks display an effective tool to reduce the range of aerosol dispersion during singing. In combination with an appropriate aeration strategy for aerosol removal, choir singers could be positioned in a more compact assembly without contaminating neighboring singers all singers.

Kniesburges Stefan, Schlegel Patrick, Peters Gregor, Westphalen Caroline, Jakubaß Bernhard, Veltrup Reinhard, Kist Andreas M, Döllinger Michael, Gantner Sophia, Kuranova Liudmila, Benthaus Tobias, Semmler Marion, Echternach Matthias

2021-Oct-05

Aerosol dispersion, Airborne virus transmission, Choir singing, SARC-CoV-2 pandemic, Surgical mask

General General

Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements.

In NPJ digital medicine

Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.

Sattler Felix, Ma Jackie, Wagner Patrick, Neumann David, Wenzel Markus, Schäfer Ralf, Samek Wojciech, Müller Klaus-Robert, Wiegand Thomas

2020-Oct-06

Public Health Public Health

Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe.

In Nature communications ; h5-index 260.0

European governments use non-pharmaceutical interventions (NPIs) to control resurging waves of COVID-19. However, they only have outdated estimates for how effective individual NPIs were in the first wave. We estimate the effectiveness of 17 NPIs in Europe's second wave from subnational case and death data by introducing a flexible hierarchical Bayesian transmission model and collecting the largest dataset of NPI implementation dates across Europe. Business closures, educational institution closures, and gathering bans reduced transmission, but reduced it less than they did in the first wave. This difference is likely due to organisational safety measures and individual protective behaviours-such as distancing-which made various areas of public life safer and thereby reduced the effect of closing them. Specifically, we find smaller effects for closing educational institutions, suggesting that stringent safety measures made schools safer compared to the first wave. Second-wave estimates outperform previous estimates at predicting transmission in Europe's third wave.

Sharma Mrinank, Mindermann Sören, Rogers-Smith Charlie, Leech Gavin, Snodin Benedict, Ahuja Janvi, Sandbrink Jonas B, Monrad Joshua Teperowski, Altman George, Dhaliwal Gurpreet, Finnveden Lukas, Norman Alexander John, Oehm Sebastian B, Sandkühler Julia Fabienne, Aitchison Laurence, Gavenčiak Tomáš, Mellan Thomas, Kulveit Jan, Chindelevitch Leonid, Flaxman Seth, Gal Yarin, Mishra Swapnil, Bhatt Samir, Brauner Jan Markus

2021-Oct-05

General General

COVID-19: WHAT'S NEXT?

In Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion

Since December 2019, when severe acute respiratory syndrome coronavirus 2 emerged in Wuhan, China, this virus and the resulting disease, coronavirus disease (COVID-19), has spread worldwide. What has occurred in this year and a half goes beyond anything we have dealt with, as humankind, in the past two centuries, perhaps obscured only by war. An incredible number of articles, whether scientific or in the press, have been published, making it impossible to discern between what is biological and what is social in nature. Here, we aim to reflect on the basic structure of the virus and associate its behavior to that of determining factors of the human condition that may be modifiable soon. Needless to say, we find our effort clearly incomplete, and that both scientific and social aspects regarding COVID-19 or any other pandemic encountered in the future, will be constantly changing, from their beginning to their end.

Ponce-de-León Alfredo, Dolores Niembro-Ortega María, González-Lara María F

2021

Artificial intelligence, Coronavirus disease, Nanotechnology, Severe acute respiratory syndrome coronavirus 2 variants, Vaccines

Internal Medicine Internal Medicine

Optimal triage for COVID-19 patients under limited healthcare resources: Development of a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has placed an unprecedented burden on healthcare systems.

OBJECTIVE : To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining healthcare system capacity.

METHODS : A nationwide sample of 5,601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate maintenance of the model performance when clinical and laboratory variables were eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find an optimal threshold within limited resource environments that minimizes mortality rates.

RESULTS : The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model's performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1%, compared to the conventional Youden Index.

CONCLUSIONS : Our adaptive triage model and its threshold optimization capability revealed that COVID-19 management can be achieved via the cooperation of both medical and healthcare management sectors for maximum treatment efficacy. The model is available online for clinical implementation.

CLINICALTRIAL :

Kim Jeong Min, Lim Hwa Kyung, Ahn Jae-Hyeon, Lee Kyoung Hwa, Lee Kwang Suk, Koo Kyo Chul

2021-Oct-03

General General

EXPRESS: Lay, professional, and artificial intelligence perspectives on risky medical decisions and COVID-19: How does the number of lives matter in clinical trials framed as gains versus losses?

In Quarterly journal of experimental psychology (2006)

Outcomes of clinical trials need to be communicated effectively to make decisions that save lives. We investigated whether framing can bias these decisions and if risk preferences shift depending on the number of patients. Hypothetical information about two medicines used in clinical trials having a sure or a risky outcome was presented in either a gain frame (people would be saved) or a loss frame (people would die). The number of patients who signed up for the clinical trials was manipulated in both frames in all the experiments. Using an unnamed disease, lay participants (experiment 1) and would-be medical professionals (experiment 2) were asked to choose which medicine they would have administered. For COVID-19, lay participants were asked which medicine should medical professionals (experiment 3), artificially intelligent software (experiment 4), and they themselves (experiment 5) favor to be administered. Broadly consistent with prospect theory, people were more risk-seeking in the loss frames than the gain frames. Risk-aversion in gain frames was sensitive to the number of lives with risk-neutrality at low magnitudes and risk-aversion at high magnitudes. In the loss frame, participants were mostly risk-seeking. This pattern was consistent across laypersons and medical professionals, further extended to preferences for choices that medical professionals and artificial intelligence programs should make in the context of COVID-19. These results underscore how medical decisions can be impacted by the number of lives at stake and reveal inconsistent risk preferences for clinical trials during a real pandemic.

Mukherjee Sumitava, Reji Divya

2021-Oct-05

Asian Disease problem, COVID-19, Framing effect, Risk-aversion, Valuation of lives, medical decision making

General General

Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants.

In ACS pharmacology & translational science

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Jain Sankalp, Talley Daniel C, Baljinnyam Bolormaa, Choe Jun, Hanson Quinlin, Zhu Wei, Xu Miao, Chen Catherine Z, Zheng Wei, Hu Xin, Shen Min, Rai Ganesha, Hall Matthew D, Simeonov Anton, Zakharov Alexey V

2021-Oct-08

General General

Mental health trajectories of individuals and families following the COVID-19 pandemic: Study protocol of a longitudinal investigation and prevention program.

In Mental health & prevention

Introduction : Many adults, adolescents and children are suffering from persistent stress symptoms in the face of the COVID-19 pandemic. This study aims to characterize long-term trajectories of mental health and to reduce the transition to manifest mental disorders by means of a stepped care program for indicated prevention.

Methods and analysis : Using a prospective-longitudinal design, we will assess the mental strain of the pandemic using the Patient Health Questionnaire, Strength and Difficulties Questionnaire and Spence Child Anxiety Scale. Hair samples will be collected to assess cortisol as a biological stress marker of the previous months. Additionally, we will implement a stepped-care program with online- and face-to-face-interventions for adults, adolescents, and children. After that we will assess long-term trajectories of mental health at 6, 12, and 24 months follow-up. The primary outcome will be psychological distress (depression, anxiety and somatoform symptoms). Data will be analyzed with general linear model and machine learning. This study will contribute to the understanding of the impact of the COVID-19 pandemic on mental health. The evaluation of the stepped-care program and longitudinal investigation will inform clinicians and mental health stakeholders on populations at risk, disease trajectories and the sufficiency of indicated prevention to ameliorate the mental strain of the pandemic.

Ethics and dissemination : The study is performed according to the Declaration of Helsinki and was approved by the Ethics Committee of the Department of Psychology at the Humboldt Universität zu Berlin (no. 2020-35).

Trial registration number : DRKS00023220.

Langhammer Till, Hilbert Kevin, Praxl Berit, Kirschbaum Clemens, Ertle Andrea, Asbrand Julia, Lueken Ulrike

2021-Sep-30

COVID-19, cortisol, family transmission, prediction, stepped-care

General General

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images.

In Applied soft computing

Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

Naeem Hamad, Bin-Salem Ali Abdulqader

2021-Dec

Convolutional Neural Network, Coronavirus, Covid-19 Severity Classification, Deep learning, Long Short-Term Memory, Multi-level feature extraction

Public Health Public Health

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran.

In Stochastic environmental research and risk assessment : research journal

As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg-Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.

Shaibani Mohammad Javad, Emamgholipour Sara, Moazeni Samira Sadate

2021-Sep-30

Artificial bee colony, Artificial neural network, COVID-19, Firefly algorithm, Hybrid model

General General

Detection and analysis of COVID-19 in medical images using deep learning techniques.

In Scientific reports ; h5-index 158.0

The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.

Yang Dandi, Martinez Cristhian, Visuña Lara, Khandhar Hardev, Bhatt Chintan, Carretero Jesus

2021-Oct-04

General General

Artificial intelligence against COVID-19 Pandemic: A Comprehensive Insight.

In Current medical imaging

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.

Equbal Azhar, Masood Sarfaraz, Equbal Iftekhar, Ahmad Shafi, Khan Noor Zaman, Khan Zahid A

2021-Oct-04

Artificial intelligence, COVID-19, Diagnosis, Imaging, Pandemic, Pathogens

General General

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs.

In Physical and engineering sciences in medicine

According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.

Arora Ridhi, Bansal Vipul, Buckchash Himanshu, Kumar Rahul, Sahayasheela Vinodh J, Narayanan Narayanan, Pandian Ganesh N, Raman Balasubramanian

2021-Oct-05

COVID-19, Classification, Deep learning, Feature extraction, Image processing, Machine learning, X-ray

Public Health Public Health

Association between human coronaviruses' epidemic and environmental factors on a global scale.

In Environmental science and pollution research international

Environmental factors could influence the epidemic of virus in human; however, the association remains intricate, and the evidence is still not clear in human coronaviruses (HCoVs). We aimed to explore and compare the associations between HCoVs' epidemic and environmental factors globally. Four common HCoVs' data were collected by a systematic literature review, and data of MERS, SARS, and COVID-19 were collected from the World Health Organization's reports. Monthly positive rates of common HCoVs and incidence rates of MERS, SARS, and COVID-19 were calculated. Geographical coordinates were used to link virus data and environmental data. Generalized additive models (GAMs) were used to quantitatively estimate the association of environmental factors with HCoVs' epidemic. We found that there are wide associations between HCoVs and environmental factors on a global scale, and some of the associations were nonlinear. In addition, COVID-19 has the most similarities in associations' direction with common HCoVs, especially for HCoV-HKU1 in four environmental factors including the significantly negative associations with average temperature, precipitation, vegetation coverage (p<0.05), and the U-shaped association with temperature range. This study strengthened the relevant research evidences and provided significant insights into the epidemic rules of HCoVs in general. The similarities between COVID-19 and common HCoVs indicated that it is critically important to strengthen surveillance on common HCoVs and pay more attention to environmental factors' role in surveillance and early warning of HCoVs' epidemic.

Yan Xiangyu, Wang Zekun, Wang Xuechun, Zhang Xiangyu, Wang Lianhao, Lu Zuhong, Jia Zhongwei

2021-Oct-05

Environmental factor, Epidemic, Global health, Human coronavirus, Meteorological factor, One health, Vegetation coverage

General General

[Digitalization in rheumatological practice].

In Zeitschrift fur Rheumatologie

Digitalization in medicine is of major interest since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. This article tries to present the induced changes and technical solutions with respect to the different parts in the patient journey. Symptom checkers, new health applications, digital appointment management etc. are described. Apart from the technical and digital possibilities, the changes in the quality of communication additionally have to be mentioned. There is an urgent need for further technical standardization including the interfaces. In many cases further studies must confirm the equivalence of digital applications in comparison to analogue techniques.

Welcker M, Mühlensiepen F, Knitza J, Popp F, Aries P

2021-Oct-04

Applications, Artificial intelligence, Communication, Digital devices, Telemedicine

General General

The potential and challenges of Health 4.0 to face COVID-19 pandemic: a rapid review.

In Health and technology

The COVID-19 pandemic has generated the need to evolve health services to reduce the risk of contagion and promote a collaborative environment even remotely. Advances in Industry 4.0, including the internet of things, mobile networks, cloud computing, and artificial intelligence make Health 4.0 possible to connect patients with healthcare professionals. Hence, the focus of this work is analyzing the potentiality, and challenges of state-of-the-art Health 4.0 applications to face the COVID-19 pandemic including augmented environments, diagnosis of the virus, forecasts, medical robotics, and remote clinical services. It is concluded that Health 4.0 can be applied in the prevention of contagion, improve diagnosis, promote virtual learning environments, and offer remote services. However, there are still ethical, technical, security, and legal challenges to be addressed. Additionally, more imaging datasets for COVID-19 detection need to be made available to the scientific community. Working in the areas of opportunity will help to address the new normal. Likewise, Health 4.0 can be applied not only in the COVID-19 pandemic, but also in future global viruses and natural disasters.

Loeza-Mejía Cecilia-Irene, Sánchez-DelaCruz Eddy, Pozos-Parra Pilar, Landero-Hernández Luis-Alfonso

2021-Sep-28

Artificial Intelligence, COVID-19, Communication networks, Health 4.0, Machine learning, Remote clinical services

General General

The prospective of Artificial Intelligence in COVID-19 Pandemic.

In Health and technology

Coronavirus disease 2019 (COVID-19) is a major threat throughout the world. The latest advancements in the field of computational techniques based on Artificial Intelligence (AI), Machine Learning (ML) and Big Data can help in detecting, monitoring and forecasting the severity of the COVID-19 pandemic. We aim to review the detection of the COVID-19 pandemic empowered by AI, major implications, challenges and the future of smart health care at a glance. The AI plays a pioneering role in rapid and improved detection of the disease. It helps in modeling the disease activity and predicting the severity for better decision making and preparedness by healthcare authorities and policymakers. It is a promising technology for automatic and fully transparent monitoring system to track and treat the patients remotely without spreading the virus to others. The future application areas of AI-based healthcare are also identified. The role of AI in tackling the COVID-19 pandemic is reviewed in this paper. AI proves beneficial in early detection with improved results. It also provides solution for contact tracing, prediction, drug development thus reducing the workload of medical industry.

Swayamsiddha Swati, Prashant Kumar, Shaw Devansh, Mohanty Chandana

2021-Sep-28

Artificial Intelligence, COVID-19, Coronavirus, SARS-CoV-2

General General

Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin.

In Frontiers in genetics ; h5-index 62.0

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a "digital twin" of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions. Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability. Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin-angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others). Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.

Barbiero Pietro, Viñas Torné Ramon, Lió Pietro

2021

digital twin, generative adversarial networks, graph representation learning, monitoring, precision medicine

General General

On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers.

In Frontiers in immunology ; h5-index 100.0

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

Vázquez-Jiménez Aarón, Avila-Ponce De León Ugo Enrique, Matadamas-Guzman Meztli, Muciño-Olmos Erick Andrés, Martínez-López Yoscelina E, Escobedo-Tapia Thelma, Resendis-Antonio Osbaldo

2021

COVID-19, cell heterogeneity, immune landscape, immune system, machine-learning, single-cell analysis

General General

What Factors Are Most Closely Associated With Mood Disorders in Adolescents During the COVID-19 Pandemic? A Cross-Sectional Study Based on 1,771 Adolescents in Shandong Province, China.

In Frontiers in psychiatry

Background and Aims: COVID-19 has been proven to harm adolescents' mental health, and several psychological influence factors have been proposed. However, the importance of these factors in the development of mood disorders in adolescents during the pandemic still eludes researchers, and practical strategies for mental health education are limited. Methods: We constructed a sample of 1,771 adolescents from three junior high middle schools, three senior high middle schools, and three independent universities in Shandong province, China. The sample stratification was set as 5:4:3 for adolescent aged from 12 - 15, 15 - 18, 18 - 19. We examined the subjects' anxiety, depression, psychological resilience, perceived social support, coping strategies, subjective social/school status, screen time, and sleep quality with suitable psychological scales. We chose four widely used classification models-k-nearest neighbors, logistic regression, gradient-boosted decision tree (GBDT), and a combination of the GBDT and LR (GBDT + LR)-to construct machine learning models, and we utilized the Shapley additive explanations value (SHAP) to measure how the features affected the dependent variables. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. Results: The current rates of occurrence of symptoms of anxiety and depression were 28.3 and 30.8% among the participants. The descriptive and univariate analyses showed that all of the factors included were statistically related to mood disorders. Among the four machine learning algorithms, the GBDT+LR algorithm achieved the best performance for anxiety and depression with average AUC values of 0.819 and 0.857. We found that the poor sleep quality was the most significant risk factor for mood disorders among Chinese adolescents. In addition, according to the feature importance (SHAP) of the psychological factors, we proposed a five-step mental health education strategy to be used during the COVID-19 pandemic (sleep quality-resilience-coping strategy-social support-perceived social status). Conclusion: In this study, we performed a cross-sectional investigation to examine the psychological impact of COVID-19 on adolescents. We applied machine learning algorithms to quantify the importance of each factor. In addition, we proposed a five-step mental health education strategy for school psychologists.

Ren Ziyuan, Xin Yaodong, Wang Zhonglin, Liu Dexiang, Ho Roger C M, Ho Cyrus S H

2021

COVID-19, GBDT, SHAP value, adolescents, mood disorders, resilience, sleep quality

General General

Feature selection for proximity estimation in COVID-19 contact tracing apps based on Bluetooth Low Energy (BLE).

In Pervasive and mobile computing

During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLE's debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of today's solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment. Results show that a better accuracy can be obtained in outdoor locations with respect to indoor ones, and that indoor proximity estimation can benefit more from the introduction of more features with respect to the outdoor estimation case. Accuracy can be increased about 10% when multiple features are considered if the device is aware of its environment, reaching a performance of up to 83% in indoor spaces and up to 91% in outdoor ones. These results encourage future contact tracing apps to integrate this awareness not only to better assess the associated risk of a given environment but also to improve the proximity accuracy for detecting close contacts.

Madoery Pablo G, Detke Ramiro, Blanco Lucas, Comerci Sandro, Fraire Juan, Gonzalez Montoro Aldana, Bellassai Juan Carlos, Britos Grisel, Ojeda Silvia, Finochietto Jorge M

2021-Oct

Bluetooth, COVID-19, Contact tracing, Feature selection, Machine learning, Proximity estimation

Surgery Surgery

Diagnosing COVID-19 from CT Image of Lung Segmentation & Classification with Deep Learning Based on Convolutional Neural Networks.

In Wireless personal communications

Early-stage exposure and analysis of diseases are life-threatening causes for controlling the spread of COVID-19. Recently, Deep Learning (DL) centered approaches have projected intended for COVID-19 during the initial stage through the Computed Tomography (CT) mechanism is to simplify and aid with the analysis. However, these methodologiesundergocommencing one of the following issues: each CT scan slice treated separately and train and evaluate from the same dataset the strategies for image collections. Independent slice therapy is the identical patient involved in the preparation and set the tests at the same time, which can yield inaccurate outcomes. It also poses the issue of whether or not an individual should compare the scans of the same patient. This paper aims to establish image classifiers to determine whether a patient tested positive or negative for COVID-19 centered on lung CT scan imageries. In doing so, a Visual Geometry Group-16 (VGG-16) and a Convolutional Neural Network (CNN) 3-layer model used for marking. The images are first segmented using K-means Clustering before the classification to increase classification efficiency. Then, the VGG-16 model and the 3-layer CNN model implemented on the raw and segmented data. The impact of the segmentation of the image and two versions are explored and compared, respectively. Various tuning techniques were performed and tested to improve the VGG-16 model's performance, including increasing epochs, optimizer adjustment, and decreasing the learning rate. Moreover, pre-trained weights of the VGG-16 the model added to enhance the algorithm.

Kumari K Sita, Samal Sarita, Mishra Ruby, Madiraju Gunashekhar, Mahabob M Nazargi, Shivappa Anil Bangalore

2021-Sep-25

3-layer convolutional neural network, COVID-19, CT images, Deep learning, Machine learning, Visual geometry group-16

General General

Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index.

In Data & knowledge engineering

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial-temporal model and graph spatial-temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks).

Lv Zhiqiang, Li Jianbo, Dong Chuanhao, Li Haoran, Xu Zhihao

2021-Sep

COVID-19, Data mining, Data models, Mining methods and algorithms, Traffic revitalization index

General General

Broad-Spectrum Antiviral Peptides and Polymers.

In Advanced healthcare materials

As the human cost of the pandemic caused by the SARS-CoV-2 is still being witnessed worldwide, the development of broad-spectrum antiviral agents against emerging and re-emerging viruses is seen as a necessity to hamper the spread of infections. Various targets during the viral life-cycle can be considered to inhibit viral infection, from viral attachment to viral fusion or replication. Macromolecules represent a particularly attractive class of therapeutics due to their multivalency and versatility. Although several antiviral macromolecules hold great promise in clinical applications, the emergence of resistance after prolonged exposure urges the need for improved solutions. In the present article, we review the recent advancement in the discovery of antiviral peptides and polymers with diverse structural features and antiviral mechanisms. Future perspectives, such as the development of virucidal peptides/polymers and their coatings against SARS-CoV-2 infection, standardization of antiviral testing protocols, and use of artificial intelligence or machine learning as a tool to accelerate the discovery of antiviral macromolecules, are discussed. This article is protected by copyright. All rights reserved.

Kuroki Agnès, Tay Joyce, Lee Guan Huei, Yang Yi Yan

2021-Oct-02

Antivirals, antiviral mechanism, chemical structure, peptides, polymers

General General

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

In Scientific reports ; h5-index 158.0

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Nguyen Sam, Chan Ryan, Cadena Jose, Soper Braden, Kiszka Paul, Womack Lucas, Work Mark, Duggan Joan M, Haller Steven T, Hanrahan Jennifer A, Kennedy David J, Mukundan Deepa, Ray Priyadip

2021-Oct-01

General General

Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters.

In The Journal of the Acoustical Society of America

This study aimed to develop an artificial intelligence (AI)-based tool for screening COVID-19 patients based on the acoustic parameters of their voices. Twenty-five acoustic parameters were extracted from voice samples of 203 COVID-19 patients and 171 healthy individuals who produced a sustained vowel, i.e., /a/, as long as they could after a deep breath. The selected acoustic parameters were from different categories including fundamental frequency and its perturbation, harmonicity, vocal tract function, airflow sufficiency, and periodicity. After the feature extraction, different machine learning methods were tested. A leave-one-subject-out validation scheme was used to tune the hyper-parameters and record the test set results. Then the models were compared based on their accuracy, precision, recall, and F1-score. Based on accuracy (89.71%), recall (91.63%), and F1-score (90.62%), the best model was the feedforward neural network (FFNN). Its precision function (89.63%) was a bit lower than the logistic regression (90.17%). Based on these results and confusion matrices, the FFNN model was employed in the software. This screening tool could be practically used at home and public places to ensure the health of each individual's respiratory system. If there are any related abnormalities in the test taker's voice, the tool recommends that they seek a medical consultant.

Vahedian-Azimi Amir, Keramatfar Abdalsamad, Asiaee Maral, Atashi Seyed Shahab, Nourbakhsh Mandana

2021-Sep

General General

Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.

In Medical image analysis

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

Garcia Santa Cruz Beatriz, Bossa Matías Nicolás, Sölter Jan, Husch Andreas Dominik

2021-Sep-27

Bias, COVID-19, Confounding, Datasets, Imaging, Machine learning, Review, X-Ray

Radiology Radiology

COVID‑19 pathology imaging: A one-year perspective.

In Dental and medical problems

The first cases of coronavirus disease 2019 (COVID‑19) were reported in Wuhan, China, in December 2019. Five months later, the World Health Organization (WHO) announced a pandemic. The symptoms are nonspecific, and include breathing difficulties, cough, fever, and the loss of smell and taste. The diagnosis is confirmed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) testing. Medical imaging has been mainly used to estimate the range of disease or potential complications.The aim of this study was to present the radiographic features of COVID‑19 reported in published papers. This investigation includes the scientific work concerning chest radiography (chest X-ray - CXR) and computed tomography (CT) in COVID‑19 patients. The most common pathologies are described, and the classification of COVID‑19 appearance in CT and other radiology reports is summarized. The usage of lung ultrasound (LUS) was taken into consideration. This study emphasizes the role of artificial intelligence (AI) in the COVID‑19 pandemic. The algorithms developed to detect the disease are discussed. The role of medical imaging is not limited to the respiratory system; it can also be used in searching for and monitoring complications (cardiac, vascular or brain damage). Due to the significant role of radiology in the current pandemic, a review of the latest medical literature was performed to help clarify the upcoming data.

Hajac Martyna, Olchowy Cyprian, Poręba Rafał, Gać Paweł

COVID‑19, artificial intelligence, computed tomography, radiography, ultrasound

Public Health Public Health

Global Research on Coronaviruses: A Metadata-Based Analysis for Public Health Policies.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Within the context of the COVID-19 pandemic, this article suggests a data science strategy for analyzing global research on coronaviruses. The application of reproducible research principles founded on text-as-data, open science, the dissemination of scientific data, and easy access to scientific production may aid public health in the fight against the virus.

OBJECTIVE : The primary goal of this article is to use global research on coronaviruses to identify critical elements that can help inform public health policy decisions. We present a data science framework to assist policymakers in implementing cutting-edge data science techniques for the purpose of developing evidence-based public health policies.

METHODS : We use the EpiBibR package to gain access to coronavirus research documents worldwide (n = 121,231) and their associated metadata. To analyze these data, we first employ a theoretical framework to group the findings into three categories: conceptual, intellectual, and social. Second, we map the results of our analysis in these three dimensions using machine learning techniques (natural language processing) and social network analysis.

RESULTS : Our findings are first methodological in nature. They demonstrate the potential for the proposed data science framework to be applied to public health policies. Additionally, our findings indicate that the United States and China are the primary contributors to global coronavirus research. They also demonstrate that India and Europe are significant contributors, albeit in a secondary position. University collaborations in this domain are strong between the United States, Canada, and the United Kingdom, confirming the country-level findings.

CONCLUSIONS : Our findings argue for a data-driven approach to public health policy, particularly when efficient and relevant research is required. Text mining techniques can assist policymakers in calculating evidence-based indices and informing their decision-making process regarding specific actions necessary for effective health responses.

CLINICALTRIAL :

Warin Thierry

2021-Sep-27

Public Health Public Health

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.

In Briefings in bioinformatics

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

Manavalan Balachandran, Basith Shaherin, Lee Gwang

2021-Sep-30

IL-6 inducing peptides, SARS-CoV-2, antiviral peptides, bioinformatics, coronavirus, machine learning, performance assessment

General General

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate.

In Frontiers in public health

This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

Kaliappan Jayakumar, Srinivasan Kathiravan, Mian Qaisar Saeed, Sundararajan Karpagam, Chang Chuan-Yu, C Suganthan

2021

COVID-19, feature selection, machine learning, prediction error, regression, reproduction rate prediction

General General

Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 - China, 2020.

In China CDC weekly

Introduction : New information technology (IT) has been applied to prevent and control coronavirus disease 2019 (COVID-19) in many regions of China since the outbreak of COVID-19. This study aims to illustrate the current status and key areas of the application of new IT in the prevention and control of COVID-19.

Methods : Literature related to the prevention and control of COVID-19 with new IT since 2020 was retrieved from China National Knowledge Internet and Wanfang Literature databases, the two most authoritative databases in China. CiteSpace 5.7.R2 was used to analyze the institutions, authors, and keywords of the articles. The application of new IT is determined by keywords and highly cited documents.

Results : A total of 1,711 articles were included, as the number of publications has been continually increasing over the investigation period. The six hot topics of new IT applied in COVID-19 were big data, artificial intelligence, Internet+, blockchain, Internet of Things, and 5G. The most productive institution is University of Chinese Academy of Sciences, and the most productive author in this field is Tao Pei, whose article, "Multi-level Spatial Distribution Estimation Model of the Inter-Regional Migrant Population Using Multi-Source Spatio-Temporal Big Data: A Case Study of Migrants from Wuhan During the Spread of COVID-19," was highly cited.

Discussion : This study could help medical professionals understand the application status and research trends of new IT in the prevention and control of COVID-19. This paper also helps researchers find potential co-operative institutions and partners.

Li Dan, Wang Songwang, Su Xuemei

2021-Feb-19

Radiology Radiology

Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome.

In Frontiers in physiology

Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5-10 s vs. 1-2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias -9.8 ml [CI: +56.0/-75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/-5.5%] and -0.5% [CI: +2.3/-3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically.

Herrmann Peter, Busana Mattia, Cressoni Massimo, Lotz Joachim, Moerer Onnen, Saager Leif, Meissner Konrad, Quintel Michael, Gattinoni Luciano

2021

ARDS, DeepLTK, LabVIEW, Maluna, U-Net, deep learning, fully automatic lung segmentation, mechanical ventilation

General General

Cardiac involvement in hospitalized patients with COVID-19 and its incremental value in outcomes prediction.

In Scientific reports ; h5-index 158.0

Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF < 50% occurred in 3.4%, abnormal LV global longitudinal strain (< 16%) in 24%, and diastolic dysfunction in 20%. Right ventricular systolic dysfunction (RV free wall strain < 20%) was noted in 18%. Moderate and large pericardial effusion were uncommon with an incidence of 0.4% for each category. Forty patients received ECMO support, and 79 died (10.9%). A stepwise increase in AUC was observed with addition of vital signs and laboratory measurements to baseline clinical characteristics, and a further significant increase (AUC 0.91) was observed when echocardiographic measurements were added. The performance of an optimized prediction model was similar to the model including baseline characteristics + vital signs and laboratory results + echocardiographic measurements.

Pournazari Payam, Spangler Alison L, Ameer Fawzi, Hagan Kobina K, Tano Mauricio E, Chamsi-Pasha Mohammed, Chebrolu Lakshmi H, Zoghbi William A, Nasir Khurram, Nagueh Sherif F

2021-Sep-30

General General

Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics.

In Nature communications ; h5-index 260.0

Viral reproduction of SARS-CoV-2 provides opportunities for the acquisition of advantageous mutations, altering viral transmissibility, disease severity, and/or allowing escape from natural or vaccine-derived immunity. We use three mathematical models: a parsimonious deterministic model with homogeneous mixing; an age-structured model; and a stochastic importation model to investigate the effect of potential variants of concern (VOCs). Calibrating to the situation in England in May 2021, we find epidemiological trajectories for putative VOCs are wide-ranging and dependent on their transmissibility, immune escape capability, and the introduction timing of a postulated VOC-targeted vaccine. We demonstrate that a VOC with a substantial transmission advantage over resident variants, or with immune escape properties, can generate a wave of infections and hospitalisations comparable to the winter 2020-2021 wave. Moreover, a variant that is less transmissible, but shows partial immune-escape could provoke a wave of infection that would not be revealed until control measures are further relaxed.

Dyson Louise, Hill Edward M, Moore Sam, Curran-Sebastian Jacob, Tildesley Michael J, Lythgoe Katrina A, House Thomas, Pellis Lorenzo, Keeling Matt J

2021-Sep-30

Radiology Radiology

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net.

In Clinical imaging

PURPOSE : The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT).

MATERIALS AND METHODS : In this retrospective study, a novel hybrid model (MTU-COVNet) was developed to extract visual features from volumetric thoracic CT scans for the detection of COVID-19. The collected dataset consisted of 3210 CT scans from 953 patients. Of the total 3210 scans in the final dataset, 1327 (41%) were obtained from the COVID-19 group, 929 (29%) from the CAP group, and 954 (30%) from the Normal CT group. Diagnostic performance was assessed with the area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity.

RESULTS : The proposed approach with the optimized features from concatenated layers reached an overall accuracy of 97.7% for the CT-MTU dataset. The rest of the total performance metrics, such as; specificity, sensitivity, precision, F1 score, and Matthew Correlation Coefficient were 98.8%, 97.6%, 97.8%, 97.7%, and 96.5%, respectively. This model showed high diagnostic performance in detecting COVID-19 pneumonia (specificity: 98.0% and sensitivity: 98.2%) and CAP (specificity: 99.1% and sensitivity: 97.1%). The areas under the ROC curves for COVID-19 and CAP were 0.997 and 0.996, respectively.

CONCLUSION : A deep learning-based AI system built on the CT imaging can detect COVID-19 pneumonia with high diagnostic efficiency and distinguish it from CAP and normal CT. AI applications can have beneficial effects in the fight against COVID-19.

Kavuran Gürkan, İn Erdal, Geçkil Ayşegül Altıntop, Şahin Mahmut, Berber Nurcan Kırıcı

2021-Sep-27

Artificial intelligence (AI), COVID-19, Computed tomography (CT), Deep learning, Pneumonia

Public Health Public Health

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach.

In JMIR public health and surveillance

BACKGROUND : COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance.

OBJECTIVE : Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved.

METHODS : We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes.

RESULTS : A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC-ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the "lite" models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM.

CONCLUSIONS : We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.

Wong Kenneth Chi-Yin, Xiang Yong, Yin Liangying, So Hon-Cheong

2021-Sep-30

COVID-19, biobank, machine learning, medical informatics, pandemic, prediction, prediction models, public health, risk factors

General General

Determining Top Fully Connected Layer's Hidden Neuron Count for Transfer Learning, Using Knowledge Distillation: a Case Study on Chest X-Ray Classification of Pneumonia and COVID-19.

In Journal of digital imaging

Deep convolutional neural network (CNN)-assisted classification of images is one of the most discussed topics in recent years. Continuously innovation of neural network architectures is making it more correct and efficient every day. But training a neural network from scratch is very time-consuming and requires a lot of sophisticated computational equipment and power. So, using some pre-trained neural network as feature extractor for any image classification task or "transfer learning" is a very popular approach that saves time and computational power for practical use of CNNs. In this paper, an efficient way of building full model from any pre-trained model with high accuracy and low memory is proposed using knowledge distillation. Using the distilled knowledge of the last layer of pre-trained networks passes through fully connected layers with different hidden layers, followed by Softmax layer. The accuracies of student networks are mildly lesser than the whole models, but accuracy of student models clearly indicates the accuracy of the real network. In this way, the best number of hidden layers for dense layer for that pre-trained network with best accuracy and no-overfitting can be found with less time. Here, VGG16 and VGG19 (pre-trained upon "ImageNet" dataset) is tested upon chest X-rays (pneumonia and COVID-19). For finding the best total number of hidden layers, it saves nearly 44 min for VGG19 and 36 min and 37 s for VGG16 feature extractor.

Ghosh Ritwick

2021-Sep-29

COVID-19, Chest X-ray, Deep learning, Image classification, Knowledge distillation, Medical imaging, Pneumonia, Transfer learning

General General

Corrigendum: Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada.

In Frontiers in artificial intelligence

[This corrects the article DOI: 10.3389/frai.2021.684609.].

Snider Brett, Patel Bhumi, McBean Edward

2021

COVID-19, SHAP (shapley additive explanation), XGBoost (extreme gradient boosting), artificial intelligence, co-morbidity, mortality

General General

Predicting COVID-19 Patient Shielding: A Comprehensive Study

The 2021 Australasian Joint Conference on Artificial Intelligence (AJCAI 2021)

There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding -- identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a multi-label classification problem. We use publicly available, de-identified ICU medical text data for our experiments. The labels are derived from the published COVID-19 patient shielding data. We present an extensive comparison across 12 multi-label classifiers from the simple binary relevance to neural networks and the most recent transformers. To the best of our knowledge this is the first comprehensive study, where such a range of multi-label classifiers for medical text are considered. We highlight the benefits of various approaches, and argue that, for the task at hand, both predictive accuracy and processing time are essential.

Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer

2021-10-01

General General

Characteristics of Adaptation in Undergraduate University Students Suddenly Exposed to Fully Online Education During the COVID-19 Pandemic.

In Frontiers in psychiatry

This study aimed to clarify the adaptation features of University students exposed to fully online education during the novel coronavirus disease 2019 (COVID-19) pandemic and to identify accompanying mental health problems and predictors of school adaptation. The pandemic has forced many universities to transition rapidly to delivering online education. However, little is known about the impact of this drastic change on students' school adaptation. This cross-sectional study used an online questionnaire, including assessments of impressions of online education, study engagement, mental health, and lifestyle habits. In total, 1,259 students were assessed. The characteristics of school adaptation were analyzed by a two-step cluster analysis. The proportion of mental health problems was compared among different groups based on a cluster analysis. A logistic regression analysis was used to identify predictors of cluster membership. P-values < 0.05 were considered statistically significant. The two-step cluster analysis determined three clusters: school adaptation group, school maladaptation group, and school over-adaptation group. The last group significantly exhibited the most mental health problems. Membership of this group was significantly associated with being female (OR = 1.42; 95% CI 1.06-1.91), being older (OR = 1.21; 95% CI 1.01-1.44), those who considered online education to be less beneficial (OR = 2.17; 95% CI 1.64-2.88), shorter sleep time on weekdays (OR = 0.826; 95% CI 0.683-.998), longer sleep time on holidays (OR = 1.21; 95% CI 1.03-1.43), and worse restorative sleep (OR = 2.27; 95% CI 1.81-2.86). The results suggest that academic staff should understand distinctive features of school adaptation owing to the rapid transition of the educational system and should develop support systems to improve students' mental health. They should consider ways to incorporate online classes with their lectures to improve students' perceived benefits of online education. Additionally, educational guidance on lifestyle, such as sleep hygiene, may be necessary.

Ishimaru Daiki, Adachi Hiroyoshi, Nagahara Hajime, Shirai Shizuka, Takemura Haruo, Takemura Noriko, Mehrasa Alizadeh, Higashino Teruo, Yagi Yasushi, Ikeda Manabu

2021

COVID-19, University students, mental health, online learning, school adaptation

General General

Empirical Quantitative Analysis of COVID-19 Forecasting Models

ArXiv Preprint

COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large number of different forecasting models have been proposed, mainly including statistical models, compartmental models, and deep learning models. However, due to various uncertain factors across different regions such as economics and government policy, no forecasting model appears to be the best for all scenarios. In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training. We find that if a dimension brings about higher performance gains, if not well-tuned, it may also lead to harsher performance penalties. Furthermore, model selection is the dominant factor in determining the predictive performance. It is responsible for both the largest improvement and the largest variation in performance in all prediction tasks across different regions. While practitioners may perform more complicated time series analysis in practice, they should be able to achieve reasonable results if they have adequate insight into key decisions like model selection.

Yun Zhao, Yuqing Wang, Junfeng Liu, Haotian Xia, Zhenni Xu, Qinghang Hong, Zhiyang Zhou, Linda Petzold

2021-10-01

General General

COVID-view: Diagnosis of COVID-19 using Chest CT.

In IEEE transactions on visualization and computer graphics

Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis. COVID-view incorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist using COVID-view and provides the attention heatmap as an explainable DL for the model output. We designed and evaluated COVID-view through suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases.

Jadhav Shreeraj, Deng Gaofeng, Zawin Marlene, Kaufman Arie E

2021-Sep-29

General General

An attempt to construct the individual model of daily facial skin temperature using variational autoencoder.

In Artificial life and robotics

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

Masaki Ayaka, Nagumo Kent, Iwashita Yuki, Oiwa Kosuke, Nozawa Akio

2021-Sep-24

Anomaly detection, Deep learning, Facial skin temperature, Hotelling’s theory, Infrared thermography, Variational autoencoder

Public Health Public Health

The Role of Digital Technology in Responding to COVID-19 Pandemic: Saudi Arabia's Experience.

In Risk management and healthcare policy

Introduction : The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-chain ribonucleic acid (RNA) virus. As of March 25, 2021, the total number of positive cases and fatalities in the Kingdom of Saudi Arabia (KSA) had reached 386,300 and 6624, respectively, with a case fatality rate of 1.71%. The KSA was among the leading nations to heed the advice of WHO officials and put strict precautionary and preventive measures in place to curb the early spread of COVID-19 before it was declared a global pandemic.

Methodology : This was an uncontrolled before-after study following a mixed-method approach for data collection. National and regional data were extracted from the Health Electronic Surveillance Network (HESN), a centralized public health collection system for quantitative and statistical data. Quantitative and qualitative methods have been utilized in studying data derived from tech media.

Results : The Saudi authorities utilized different technological tools to aid in managing and combating the COVID-19 pandemic. In the case of Al Madinah Al Mounawarah, after the implementation of several technologies, the most important being Tawakkalna, the number of active daily cases decreased by 61%.

Conclusion : The use of the Tawakkalna application was proven to be a successful method in fighting the COVID-19 pandemic in the KSA. This vital and essential experience warrants the use of different digital technology that offers a personalized profile displaying the person's status (affected, vaccinated, or no history of infection). This application played and will continue to play a crucial and effective role in pandemic containment in Saudi Arabia.

Khan Anas, Alahmari Ahmed, Almuzaini Yasir, Alturki Nada, Aburas Alhanouf, Alamri Fahad A, Albagami Mohammed, Alzaid Mashael, Alharbi Turki, Alomar Rahaf, Abu Tayli Muath, Assiri Abdullah M, Jokhdar Hani A

2021

Saudi Arabia, applications, digital technology, risk management

General General

Assessment of the influence of features on a classification problem: an application to COVID-19 patients.

In European journal of operational research

This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease.

Davila-Pena Laura, García-Jurado Ignacio, Casas-Méndez Balbina

2021-Sep-24

62H30, 91A80, 97R40, COVID-19, Classification, Influence of features, Machine learning, Shapley value

General General

Classification of COVID-19 in X-ray images with Genetic Fine-tuning.

In Computers & electrical engineering : an international journal

New and more transmissible SARS-COV-2 variants aggravated the SARS-COV-2 emergence. Lung X-ray images stand out as an alternative to support case screening. The latest computer-aided diagnosis systems have been using Deep Learning (DL) to detect pulmonary diseases. In this context, our work investigates different types of pneumonia detection, including COVID-19, based on X-ray image processing and DL techniques. Our methodology comprehends a pre-processing step including data-augmentation, contrast enhancement, and resizing method to overcome the challenge of heterogeneous and few samples of public datasets. Additionally, we propose a new Genetic Fine-Tuning method to automatically define an optimal set of hyper-parameters of ResNet50 and VGG16 architectures. Our results are encouraging; we achieve an accuracy of 97% considering three classes: COVID-19, other pneumonia, and healthy. Thus, our methodology could assist in classifying COVID-19 pneumonia, which could reduce costs by making the process faster and more efficient.

Vieira Pablo A, Magalhães Deborah M V, Carvalho-Filho Antonio O, Veras Rodrigo M S, Rabêlo Ricardo A L, Silva Romuere R V

2021-Sep-24

Convolutional neural networks, Evolutionary genetic systems, Fine-tuning, Pneumonia, SARS-COV-2, X-ray

General General

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

bioRxiv Preprint

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima's D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

Mostefai, F.; Gamache, I.; Huang, J.; N’Guessan, A.; Pelletier, J.; Pesaranghader, A.; Hamelin, D.; Murall, C. L.; Poujol, R.; Grenier, J.-C.; Smith, M.; Caron, E.; Craig, M.; Shapiro, J.; Wolf, G.; Krishnaswamy, S.; Hussin, J.

2021-09-29

General General

COVID19-HPSMP : COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction.

In Expert systems with applications

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction ( COVID19 PRIMO ) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction ( COVID19-HPSMP ), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion centre that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

Ronaghi Farnoush, Salimibeni Mohammad, Naderkhani Farnoosh, Mohammadi Arash

2021-Sep-20

COVID-19 pandemic, Deep Neural Networks, Hybrid models, Information fusion, Stock movement prediction

Public Health Public Health

ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India.

In Neural computing & applications

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40-60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10-3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Kumar Rajagopal, Al-Turjman Fadi, Srinivas L N B, Braveen M, Ramakrishnan Jothilakshmi

2021-Sep-21

Cloud data, Corona-virus disease 19 (COVID-19), Edge artificial intelligence, Machine learning

General General

6G Networks for Next Generation of Digital TV Beyond 2030.

In Wireless personal communications

This paper prosed a novel 6G QoS over the future 6G wireless architecture to offer excellent Quality of Service (QoS) for the next generation of digital TV beyond 2030. During the last 20 years, the way society used to watch and consume TV and Cinema has changed radically. The creation of the Over The Top content platforms based on Cloud Services followed by its commercial video consumption model, offering flexibility for subscribers such as n Video on Demand. Besides the new business model created, the network infrastructure and wireless technologies also permitted the streaming of high-quality TV and film formats such as High Definition, followed by the latest widespread TV standardization Ultra-High- Definition TV. Mobile Broadband services onset the possibility for consumers to watch TV or Video content anywhere at any time. However, the network infrastructure needs continuous improvement, primarily when crises, like the coronavirus disease (COVID-19) and the worldwide pandemic, creates immense network traffic congestions. The outcome of that congestion was the decrease of QoS for such multimedia services, impacting the user's experience. More power-hungry video applications are commencing to test the networks' resilience and future roadmap of 5G and Beyond 5G (B5G). For this, 6G architecture planning must be focused on offering the ultimate QoS for prosumers beyond 2030.

Rufino Henrique Paulo Sergio, Prasad Ramjee

2021-Sep-18

4K, 6G, 8K, B5G, Broadcasting, Holographic communications, Live TV, Multicasting, NextGen TV, OTT, Prosumers, QoE, QoS, Quantum computing, Quantum machine learning, Satellite TV, UHD TV

General General

COVID-19's U.S. Temperature Response Profile.

In Environmental & resource economics

** : We estimate the U.S. temperature response profile (TRP) for COVID-19 and show it is highly sensitive to temperature variation. Replacing the erratic daily death counts U.S. states initially reported with counts based on death certificate date, we build a week-ahead statistical forecasting model that explains most of their daily variation (R2 = 0.97) and isolates COVID-19's TRP (p < 0.001). These counts, normalized at 31 °C (U.S. mid-summer average), scale up to 160% at 5 °C in the static case where the infection pool is held constant. Positive case counts are substantially more temperature sensitive. When temperatures are declining, dynamic feedback through a growing infection pool can substantially amplify these temperature effects. Our estimated TRP can be incorporated into COVID-related planning exercises and used as an input to SEIR models employed for longer run forecasting. For the former, we show how our TRP is predictive of the realized pattern of growth rates in per capita positive cases across states five months after the end of our sample period. For the latter, we show the variation in herd immunity levels implied by temperature-driven, time-varying R0 series for the Alpha and Delta variants of COVID-19 for several representative states.

Supplementary Information : The online version contains supplementary material available at 10.1007/s10640-021-00603-8.

Carson Richard T, Carson Samuel L, Dye Thayne K, Mayfield Samuel A, Moyer Daniel C, Yu Chu A

2021-Sep-20

Data temporal alignment, Epidemiology, Forecasting, Temperature sensitivity

General General

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.

In Pattern recognition letters

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.

Mansour Romany F, Escorcia-Gutierrez José, Gamarra Margarita, Gupta Deepak, Castillo Oscar, Kumar Sachin

2021-Nov

COVID-19, Deep learning, Image classification, Unsupervised learning, Variational autoencoder

General General

Deep Transfer Learning Based Classification Model for Covid-19 using Chest CT-scans.

In Pattern recognition letters

COVID-19 is an infectious and contagious virus. As of this writing, more than 160 million people have been infected since its emergence, including more than 125,000 in Algeria. In this work, We first collected a dataset of 4,986 COVID and non-COVID images confirmed by RT-PCR tests at Tlemcen hospital in Algeria. Then we performed a transfer learning on deep learning models that got the best results on the ImageNet dataset, such as DenseNet121, DenseNet201, VGG16, VGG19, Inception Resnet-V2, and Xception, in order to conduct a comparative study. Therefore, We have proposed an explainable model based on the DenseNet201 architecture and the GradCam explanation algorithm to detect COVID-19 in chest CT images and explain the output decision. Experiments have shown promising results and proven that the introduced model can be beneficial for diagnosing and following up patients with COVID-19.

Lahsaini Ilyas, Habib Daho Mostafa El, Chikh Mohamed Amine

2021-Sep-22

COVID-19, Deep Learning, DenseNet-121, DenseNet-201, ImageNet, Transfer Learning, VGG16, VGG19,, Xception

General General

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images.

In Pattern recognition

Segmentation of infections from CT scans is important for accurate diagnosis and follow-up in tackling the COVID-19. Although the convolutional neural network has great potential to automate the segmentation task, most existing deep learning-based infection segmentation methods require fully annotated ground-truth labels for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised segmentation method for COVID-19 infections in CT slices, which only requires scribble supervision and is enhanced with the uncertainty-aware self-ensembling and transformation-consistent techniques. Specifically, to deal with the difficulty caused by the shortage of supervision, an uncertainty-aware mean teacher is incorporated into the scribble-based segmentation method, encouraging the segmentation predictions to be consistent under different perturbations for an input image. This mean teacher model can guide the student model to be trained using information in images without requiring manual annotations. On the other hand, considering the output of the mean teacher contains both correct and unreliable predictions, equally treating each prediction in the teacher model may degrade the performance of the student network. To alleviate this problem, the pixel level uncertainty measure on the predictions of the teacher model is calculated, and then the student model is only guided by reliable predictions from the teacher model. To further regularize the network, a transformation-consistent strategy is also incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed method has been evaluated on two public datasets and one local dataset. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.

Liu Xiaoming, Yuan Quan, Gao Yaozong, He Kelei, Wang Shuo, Tang Xiao, Tang Jinshan, Shen Dinggang

2021-Sep-20

COVID-19, infection segmentation, transformation consistency, uncertainty, weakly supervised learning

General General

AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome.

In Genes & genetic systems

In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel knowledge from big data without prior knowledge or particular models is highly desirable for analyses of genome sequences, particularly for obtaining unexpected insights. We have developed a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions that can reveal various novel genome characteristics. Here, we explain the data mining by the BLSOM: an unsupervised AI. As a specific target, we first selected SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) because a large number of viral genome sequences have been accumulated via worldwide efforts. We analyzed more than 0.6 million sequences collected primarily in the first year of the pandemic. BLSOMs for short oligonucleotides (e.g., 4-6-mers) allowed separation into known clades, but longer oligonucleotides further increased the separation ability and revealed subgrouping within known clades. In the case of 15-mers, there is mostly one copy in the genome; thus, 15-mers that appeared after the epidemic started could be connected to mutations, and the BLSOM for 15-mers revealed the mutations that contributed to separation into known clades and their subgroups. After introducing the detailed methodological strategies, we explain BLSOMs for various topics, such as the tetranucleotide BLSOM for over 5 million 5-kb fragment sequences derived from almost all microorganisms currently available and its use in metagenome studies. We also explain BLSOMs for various eukaryotes, including fishes, frogs and Drosophila species, and found a high separation ability among closely related species. When analyzing the human genome, we found enrichments in transcription factor-binding sequences in centromeric and pericentromeric heterochromatin regions. The tDNAs (tRNA genes) could be separated according to their corresponding amino acid.

Ikemura Toshimichi, Iwasaki Yuki, Wada Kennosuke, Wada Yoshiko, Abe Takashi

2021-Sep-27

COVID-19, artificial intelligence, metagenome, oligonucleotide composition, self-organizing map

Dermatology Dermatology

Evolving phenotypes of non-hospitalized patients that indicate long COVID.

In BMC medicine ; h5-index 89.0

BACKGROUND : For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection.

METHODS : In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3-6 and 6-9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized.

RESULTS : We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients' medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94-3.46]), alopecia (OR 3.09, 95% CI [2.53-3.76]), chest pain (OR 1.27, 95% CI [1.09-1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22-2.10]), shortness of breath (OR 1.41, 95% CI [1.22-1.64]), pneumonia (OR 1.66, 95% CI [1.28-2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22-1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65.

CONCLUSIONS : The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.

Estiri Hossein, Strasser Zachary H, Brat Gabriel A, Semenov Yevgeniy R, Patel Chirag J, Murphy Shawn N

2021-Sep-27

Electronic health records, Machine learning, Phenotypes, Post-acute sequelae of SARS-CoV-2

General General

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate.

MATERIALS AND METHODS : Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively.

RESULTS : Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes.

DISCUSSION : DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps.

CONCLUSION : DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

Huang Yufang, Liu Yifan, Steel Peter A D, Axsom Kelly M, Lee John R, Tummalapalli Sri Lekha, Wang Fei, Pathak Jyotishman, Subramanian Lakshminarayanan, Zhang Yiye

2021-Sep-27

machine learning, predictive clustering, risk stratification

General General

Covid-19 risk factors: statistical learning from German healthcare claims data.

In Infectious diseases (London, England)

BACKGROUND : Precise individual risk quantification of severe courses of Covid-19 is needed to prioritize protective measures and to assess population risks in a phase of increased immunization. So far, results for the German population are lacking. Furthermore, existing studies pre-specify comorbidity risks by broad categories rather than deriving them from the data using statistical learning techniques.

METHODS : Risk factors for severe, critical and lethal courses of Covid-19 are identified from a large German claims dataset covering more than 4 million individuals. To avoid prior grouping and pre-selection of risk factors, fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures are used, resulting in more than 33,000 covariates. These are processed using a LASSO approach.

RESULTS : We identify relevant risk factors, among which hypertensive diseases, heart disease and the corresponding medications are most relevant at population level. Prior use of diuretics is the strongest single medical predictor for severe course (e.g. Torasemide, odds ratio (OR) 1.801), but also for a critical course (OR 2.304) and death (OR 2.523). To assess risk profiles at the individual level, our approach sums up many such factors and has better predictive ability than using pre-specified morbidity groups (AUC for predicting critical course 0.875 versus AUC ≤ 0.865).

CONCLUSIONS : The proposed method can help to identify risk factors and assess risk at the individual level for other infectious diseases. The results can be used by administrative data holders to guide protective policies, while a risk index can be applied in clinical studies with a narrower focus.

Jucknewitz Roland, Weidinger Oliver, Schramm Anja

2021-Sep-26

SARS-CoV-2, machine learning, prediction, prioritization, routine data

General General

Explaining Causal Influence of External Factors on Incidence Rate of Covid-19.

In SN computer science

Classical susceptible-infected-removed model with constant transmission rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread and spatio-temporal variation of transmission rate. Also, explainability of a model is of prime necessity to understand the influence of multiple factors on transmission rate. Thus, we modified discrete global susceptible-infected-removed model with time-varying transmission rate, recovery rate and multiple spatially local models. We have derived the criteria for disease-free equilibrium within a specific time period. A convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection cases in each locality in a region in USA for a 10-day prediction period. Comparison with current state of the art methods reveals performance superiority of the proposed method. A perturbation-based spatio-temporal model interpretation method is proposed which generates spatio-temporal local interpretations. Global interpretations are generated by statistically accumulating the local interpretations. Global interpretations of transmission rate for a region in USA shows consistent positive influence of population density, whereas, temperature and humidity have very minor influence. An experiment with what-if scenario reveals locality specific quick identification of positive cases, rapid isolation and improving healthcare facilities are keys to rapid convergence to disease-free equilibrium. A long-term forecasting of 160 days predicts new infection cases in a region in USA with a median error of 455 cases.

Paul Swarna Kamal, Jana Saikat, Bhaumik Parama

2021

Artificial intelligence, Discrete mathematics, Modeling and prediction, Neural nets

General General

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images.

In Journal of ambient intelligence and humanized computing

The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16, Xception and InceptionV3, where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3. The source code of this work is available at https://github.com/subhankar01/covidfs-aihc.

Bhowal Pratik, Sen Subhankar, Sarkar Ram

2021-Sep-22

COVID-19, CUR decomposition, Chest-X-Ray images, Coalition game theory, Deep learning, Feature selection, Nystrom sampling

General General

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques.

In Journal of ambient intelligence and humanized computing

Since the arrival of the novel Covid-19, several types of researches have been initiated for its accurate prediction across the world. The earlier lung disease pneumonia is closely related to Covid-19, as several patients died due to high chest congestion (pneumonic condition). It is challenging to differentiate Covid-19 and pneumonia lung diseases for medical experts. The chest X-ray imaging is the most reliable method for lung disease prediction. In this paper, we propose a novel framework for the lung disease predictions like pneumonia and Covid-19 from the chest X-ray images of patients. The framework consists of dataset acquisition, image quality enhancement, adaptive and accurate region of interest (ROI) estimation, features extraction, and disease anticipation. In dataset acquisition, we have used two publically available chest X-ray image datasets. As the image quality degraded while taking X-ray, we have applied the image quality enhancement using median filtering followed by histogram equalization. For accurate ROI extraction of chest regions, we have designed a modified region growing technique that consists of dynamic region selection based on pixel intensity values and morphological operations. For accurate detection of diseases, robust set of features plays a vital role. We have extracted visual, shape, texture, and intensity features from each ROI image followed by normalization. For normalization, we formulated a robust technique to enhance the detection and classification results. Soft computing methods such as artificial neural network (ANN), support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier, and deep learning classifier are used for classification. For accurate detection of lung disease, deep learning architecture has been proposed using recurrent neural network (RNN) with long short-term memory (LSTM). Experimental results show the robustness and efficiency of the proposed model in comparison to the existing state-of-the-art methods.

Goyal Shimpy, Singh Rajiv

2021-Sep-18

Covid-19, Deep learning, Lung disease prediction, Machine learning, Soft computing

General General

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning.

In Biomedical signal processing and control

Background and objective : In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals.

Methods : The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming.

Results : Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs.

Conclusions : Comparison of the model output is undertaken with physician's prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.

Radhakrishnan Sita, Nair Suresh G, Isaac Johney

2022-Jan

Blood oxygen saturation, Inspired oxygen, Mechanical ventilation, Multilayer perceptron, Pairwise analysis

General General

The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion.

In Frontiers in psychology ; h5-index 92.0

More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams - such as reduced informal communication - with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable - but not limited to - being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.

Peifer Corinna, Pollak Anita, Flak Olaf, Pyszka Adrian, Nisar Muhammad Adeel, Irshad Muhammad Tausif, Grzegorzek Marcin, Kordyaka Bastian, Kożusznik Barbara

2021

collective communication, machine learning, team effectiveness, team flow, virtual teams

General General

COVID-19: A Comprehensive Review of Learning Models.

In Archives of computational methods in engineering : state of the art reviews

Coronavirus disease is communicable and inhibits the infected person's immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end.

Chahar Shivam, Roy Pradeep Kumar

2021-Sep-18

Dermatology Dermatology

Reforms, Errors, and Dermatopathology Malpractice: Then and Now: A Comprehensive Retrospective.

In Advances in anatomic pathology

Medical malpractice occurs when a hospital, doctor, or other health care professional, through a negligent act or omission, causes an injury to a patient. The negligence might be the result of errors in diagnosis, treatment, aftercare, or health management. To be considered medical malpractice under the law, the claim must violate the standard of care, the injury must be caused by the negligence and, last but most certainly not least, the injury must result in significant damages. This review is an overview of medicolegal issues specific to the practice of Dermatopathology with the caveat that most are likely pertinent to other specialties of pathology as well. The safety of patients remains the priority in pathology as it does in any medical undertaking, and this is no different in the practice of Dermatopathology. The review is broadly divided in 2 parts-we begin with an overview of tort reforms, advocated by physicians to reduce costs associated with malpractice defense. In the second part we address practical issues specific to the practice of pathology and dermatopathology. These include among others, errors-related to the biopsy type, inadequacy of clinical information regarding the lesion that is biopsied, role of interstate dermatopathology as well as examples of select entities commonly misdiagnosed in dermatopathology. In the last decade, artificial intelligence (AI) has moved to the forefront of technology. While research into the uses of AI in pathology is promising, the use of AI in diagnostic practice is still somewhat uncommon. Given that AI is not fully integrated routinely as a diagnostic adjunct, its' impact on pathology-specific medicolegal issues cannot, as yet at least, be defined. Restriction of medical malpractice is of particular relevance in the COVID-19 era, a period that is anything but normal. The response of states with specific pandemic-related guidelines is addressed with the caveat that this particular issue is only covered in select states. Furthermore, given that the COVID pandemic is only a year old, while it does not appear to have had an immediate impact on pathology-specific medicolegal matters, it is possible that the role of COVID on this issue, if any at all, will and can only be fully defined a few years down the line.

Mahalingam Meera

2021-Sep-27

Public Health Public Health

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).

METHODS : We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.

RESULTS : Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.

CONCLUSION : Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.

Khalid Sara, Yang Cynthia, Blacketer Clair, Duarte-Salles Talita, Fernández-Bertolín Sergio, Kim Chungsoo, Park Rae Woong, Park Jimyung, Schuemie Martijn J, Sena Anthony G, Suchard Marc A, You Seng Chan, Rijnbeek Peter R, Reps Jenna M

2021-Sep-06

COVID-19, Data harmonization, Data quality control, Distributed data network, Machine learning, Risk prediction

General General

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

In PloS one ; h5-index 176.0

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

Singhal Lakshya, Garg Yash, Yang Philip, Tabaie Azade, Wong A Ian, Mohammed Akram, Chinthala Lokesh, Kadaria Dipen, Sodhi Amik, Holder Andre L, Esper Annette, Blum James M, Davis Robert L, Clifford Gari D, Martin Greg S, Kamaleswaran Rishikesan

2021

General General

Analyses of Original and Computationally-Derived Electronic Health Record Data: The National COVID Cohort Collaborative.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Computationally-derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record (EHR) data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic.

OBJECTIVE : We aimed to (1) compare the results from analyses of synthetic data to those from original data, and (2) assess the strengths and limitations of leveraging computationally-derived data for research purposes.

METHODS : We used the National COVID Cohort Collaborative's (N3C) instance of MDClone, a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel). We downloaded EHR data from 34 N3C institutional partners, and tested three use cases, including (1) exploring the distributions of key features of the COVID-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-related measures and outcomes, and constructing their respective epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data.

RESULTS : For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. While the synthetic and original data yielded overall nearly the same results, there were exceptions which included an odds ratio on either side of the null in multivariable analyses (0.97 versus 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts.

CONCLUSIONS : This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.

CLINICALTRIAL :

Foraker Randi, Guo Aixia, Thomas Jason, Zamstein Noa, Payne Philip R O, Wilcox Adam

2021-Sep-12

General General

Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study.

In Environmental science & technology ; h5-index 132.0

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO2, O3, PM2.5, PM10, SO2, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.

Kang Qiao, Song Xing, Xin Xiaying, Chen Bing, Chen Yuanzhu, Ye Xudong, Zhang Baiyu

2021-Sep-24

COVID-19, air pollutant, causal inference, machine learning, meteorological factor, structural causal model

Public Health Public Health

Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.

In Frontiers in public health

Mental health prediction is one of the most essential parts of reducing the probability of serious mental illness. Meanwhile, mental health prediction can provide a theoretical basis for public health department to work out psychological intervention plans for medical workers. The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. Besides, we use stepwise logistic regression, binary bat algorithm, hybrid improved dragonfly algorithm and the proposed prediction model to predict mental health of medical workers. The results show that the prediction accuracy of the proposed model is 92.55%, which is better than the existing algorithms. This method can be used to predict mental health of global medical worker. In addition, the method proposed in this paper can also play a role in the appropriate work plan for medical worker.

Wang Xiaofeng, Li Hu, Sun Chuanyong, Zhang Xiumin, Wang Tan, Dong Chenyu, Guo Dongyang

2021

COVID-19, artificial intelligence, machine learning, mental health, neural network, prediction, public health

General General

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.

Lorenzen Stephan Sloth, Nielsen Mads, Jimenez-Solem Espen, Petersen Tonny Studsgaard, Perner Anders, Thorsen-Meyer Hans-Christian, Igel Christian, Sillesen Martin

2021-Sep-23

General General

Network neighbors of viral targets and differentially expressed genes in COVID-19 are drug target candidates.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic is raging. It revealed the importance of rapid scientific advancement towards understanding and treating new diseases. To address this challenge, we adapt an explainable artificial intelligence algorithm for data fusion and utilize it on new omics data on viral-host interactions, human protein interactions, and drugs to better understand SARS-CoV-2 infection mechanisms and predict new drug-target interactions for COVID-19. We discover that in the human interactome, the human proteins targeted by SARS-CoV-2 proteins and the genes that are differentially expressed after the infection have common neighbors central in the interactome that may be key to the disease mechanisms. We uncover 185 new drug-target interactions targeting 49 of these key genes and suggest re-purposing of 149 FDA-approved drugs, including drugs targeting VEGF and nitric oxide signaling, whose pathways coincide with the observed COVID-19 symptoms. Our integrative methodology is universal and can enable insight into this and other serious diseases.

Zambrana Carme, Xenos Alexandros, Böttcher René, Malod-Dognin Noël, Pržulj Nataša

2021-Sep-23

General General

LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

bioRxiv Preprint

Although numerous promising therapeutic targets for human diseases have been discovered, most have not been successfully translated into clinical practice. A bottleneck in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Recent advances in 3D docking simulations have not solved this problem, given that 3D protein structures with sufficient resolution are not always available and that they are computationally expensive to obtain. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins, and we present its validation by identifying potential therapeutic compounds for various human diseases. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified novel therapeutics for cancer, lifestyle-related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. Given that ethoxzolamide is already approved for several diseases, it could be rapidly deployed for the treatment of patients with COVID-19. We envision that LIGHTHOUSE will bring about a paradigm shift in translational medicine, providing a bridge from bench side to bedside.

Shimizu, H.; Kodama, M.; Matsumoto, M.; Orba, Y.; Sasaki, M.; Sato, A.; Sawa, H.; Nakayama, K. I.

2021-09-25

General General

ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.

In Briefings in bioinformatics

MOTIVATION : The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius $r$ of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex.

RESULTS : We benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices.

AVAILABILITY : http://zoomQA.renzhitech.com.

Hippe Kyle, Lilley Cade, William Berkenpas Joshua, Chandana Pocha Ciri, Kishaba Kiyomi, Ding Hui, Hou Jie, Si Dong, Cao Renzhi

2021-Sep-22

Surgery Surgery

SARS-CoV-2 in Solid Organ Transplant Recipients: A Structured Review of 2020.

In Transplantation proceedings ; h5-index 25.0

BACKGROUND : The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is challenging health systems all over the world. Particularly high-risk groups show considerable mortality rates after infection. In 2020, a huge number of case reports, case series, and consecutively various systematic reviews have been published reporting on morbidity and mortality risk connected with SARS-CoV-2 in solid organ transplant (SOT) recipients. However, this vast array of publications resulted in an increasing complexity of the field, overwhelming even for the expert reader.

METHODS : We performed a structured literature review comprising electronic databases, transplant journals, and literature from previous systematic reviews covering the entire year 2020. From 164 included articles, we identified 3451 cases of SARS-CoV-2-infected SOT recipients.

RESULTS : Infections resulted in a hospitalization rate of 84% and 24% intensive care unit admissions in the included patients. Whereas 53.6% of patients were reported to have recovered, cross-sectional overall mortality reported after coronavirus disease 2019 (COVID-19) was at 21.1%. Synoptic data concerning immunosuppressive medication attested to the reduction or withdrawal of antimetabolites (81.9%) and calcineurin inhibitors (48.9%) as a frequent adjustment. In contrast, steroids were reported to be increased in 46.8% of SOT recipients.

CONCLUSIONS : COVID-19 in SOT recipients is associated with high morbidity and mortality worldwide. Conforming with current guidelines, modifications of immunosuppressive therapies mostly comprised a reduction or withdrawal of antimetabolites and calcineurin inhibitors, while frequently maintaining or even increasing steroids. Here, we provide an accessible overview to the topic and synoptic estimates of expectable outcomes regarding in-hospital mortality of SOT recipients with COVID-19.

Quante Markus, Brake Linda, Tolios Alexander, Della Penna Andrea, Steidle Christoph, Gruendl Magdalena, Grishina Anna, Haeberle Helene, Guthoff Martina, Tullius Stefan G, Königsrainer Alfred, Nadalin Silvio, Löffler Markus W

2021-Aug-16

Internal Medicine Internal Medicine

Delirium occurrence and association with outcomes in hospitalized COVID-19 patients.

In International psychogeriatrics

Delirium is reported to be one of the manifestations of coronavirus infectious disease 2019 (COVID-19) infection. COVID-19 hospitalized patients are at a higher risk of delirium. Pathophysiology behind the association of delirium and COVID-19 is uncertain. We analyzed the association of delirium occurrence with outcomes in hospitalized COVID-19 patients, across all age groups, at Mayo Clinic hospitals.A retrospective study of all hospitalized COVID-19 patients at Mayo Clinic between March 1, 2020 and December 31, 2020 was performed. Occurrence of delirium and outcomes of mortality, length of stay, readmission, and 30-day mortality after hospital discharge were measured. Chi-square test, student t-test, survival analysis, and logistic regression analysis were performed to measure and compare outcomes of delirium group adjusted for age, sex, Charlson comorbidity score, and COVID-19 severity with no-delirium group.A total of 4351 COVID-19 patients were included in the study. Delirium occurrence in the overall study population was noted to be 22.4%. The highest occurrence of delirium was also noted in patients with critical COVID-19 illness severity. A statistically significant OR 4.35 (3.27-5.83) for in-hospital mortality and an OR 4.54 (3.25-6.38) for 30-day mortality after discharge in the delirium group were noted. Increased hospital length of stay, 30-day readmission, and need for skilled nursing facility on discharge were noted in the delirium group. Delirium in hospitalized COVID-19 patients is a marker for increased mortality and morbidity. In this group, outcomes appear to be much worse when patients are older and have a critical severity of COVID-19 illness.

Pagali Sandeep, Fu Sunyang, Lindroth Heidi, Sohn Sunghwan, Burton M Caroline, Lapid Maria

2021-Sep-23

COVID-19 severity, delirium, hospitalized COVID-19, outcomes

Public Health Public Health

The State of Mind of Healthcare Professionals in the Light of the COVID-19: Insights from Text Analysis of Twitter's Online Discourses.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic has affected populations worldwide, with extreme health, economic, social, and political implications. Healthcare professionals (HCPs) are at the core of pandemic response and are among the most crucial factors in maintaining coping capacities. Yet, they are also vulnerable to mental health effects, managing a long-lasting emergency under lack of resources and complicated personal concerns. However, there is a lack of longitudinal studies that investigate the HCP population.

OBJECTIVE : To analyse the state of mind of HCPs as expressed in online discussions published on Twitter in light of COVID-19, from the pandemic onset until the end of 2020.

METHODS : The population for this study was selected from followers of a few hundred Twitter accounts of healthcare organizations and common HCP points of interest. We used active learning, a process that iteratively uses machine learning and manual data labeling, to select the large-scale population of Twitter accounts maintained by English-speaking HCPs focusing on individuals rather than official organizations. We analyzed the topics and emotions in their discourse during 2020. The topic distributions were obtained using the Latent Dirichlet Allocation (LDA) algorithm. We defined a measure of topic cohesion and described the most cohesive topics. The emotions expressed in tweets during 2020 were compared to 2019. Finally, the emotion intensities were cross-correlated with the pandemic waves to explore possible associations between the pandemic development and emotional response.

RESULTS : We analyzed timelines of 53,063 Twitter profiles, 90% of which are maintained by individual HCPs. Professional topics account for 44.5% of tweets by HCPs from Jan. 1st to Dec. 6th, 2020. Events such as the pandemic waves, U.S. elections, or the George Floyd case affect the HCPs' discourse. The levels of joy and sadness exceed their minimal and maximal values yesteryear, respectively, 80% of the time, P= .001. Most interestingly, fear precedes the pandemic waves (in terms of the differences in confirmed cases) by two weeks with a Spearman correlations coefficient of ρ(47)= .34, P= .026.

CONCLUSIONS : Analyses of longitudinal data over the 2020 year reveal that a large fraction of HCP discourse is related directly to professional content, including the increase in the volume of discussions following the pandemic waves. The changes in emotional patterns (decrease of joy, an increase of sadness, fear, and disgust) during the year 2020 may indicate the utmost importance in providing emotional support for HCPs to prevent fatigue, burnout, and mental health disorders postpandemic period. The increase of fear two weeks in advance of pandemic waves indicates that HCPs are in a position and with adequate qualification to anticipate the pandemic development, and could serve as a bottom-up pathway for expressing the morbidity and clinical situation to health agencies.

CLINICALTRIAL :

Elyashar Aviad, Plochotnikov Ilia, Cohen Idan-Chaim, Puzis Rami, Cohen Odeya

2021-Jul-23

General General

The Efficiency of U.S. Public Space Utilization During the COVID-19 Pandemic.

In Risk analysis : an official publication of the Society for Risk Analysis

The COVID-19 pandemic has called for and generated massive novel government regulations to increase social distancing for the purpose of reducing disease transmission. A number of studies have attempted to guide and measure the effectiveness of these policies, but there has been less focus on the overall efficiency of these policies. Efficient social distancing requires implementing stricter restrictions during periods of high viral prevalence and rationing social contact to disproportionately preserve gatherings that produce a good ratio of benefits to transmission risk. To evaluate whether U.S. social distancing policy actually produced an efficient social distancing regime, we tracked consumer preferences for, visits to, and crowding in public locations of 26 different types. We show that the United States' rationing of public spaces, postspring 2020, has failed to achieve efficiency along either dimension. In April 2020, the United States did achieve notable decreases in visits to public spaces and focused these reductions at locations that offer poor benefit-to-risk tradeoffs. However, this achievement was marred by an increase, from March to April, in crowding at remaining locations due to fewer locations remaining open. In December 2020, at the height of the pandemic so far, crowding in and total visits to locations were higher than in February, before the U.S. pandemic, and these increases were concentrated in locations with the worst value-to-risk tradeoff.

Benzell Seth G, Collis Avinash, Nicolaides Christos

2021-Sep-22

COVID-19, nonpharmaceutical interventions, social contact, social welfare, transmission risk

General General

COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning.

In Informatics in medicine unlocked

Coronavirus (COVID-19) has been one of the most dangerous and acute deadly diseases across the world recently. Researchers are trying to develop automated and feasible COVID-19 detection systems with the help of deep neural networks, machine learning techniques, etc. In this paper, a deep learning-based COVID-19 detection system called COV-VGX is proposed that contributes to detecting coronavirus disease automatically using chest X-ray images. The system introduces two types of classifiers, namely, a multiclass classifier that automatically predicts coronavirus, pneumonia, and normal classes and a binary classifier that predicts coronavirus and pneumonia classes. Using transfer learning, a deep CNN model is proposed to extract distinct and high-level features from X-ray images in collaboration with the pretrained model VGG-16. Despite the limitation of the COVID-19 dataset, the model is evaluated with sufficient COVID-19 images. Extensive experiments for multiclass classifier have achieved 98.91% accuracy, 97.31% precision, 99.50% recall, 98.39% F1-score, while 99.37% accuracy, 98.76% precision, 100% recall, 99.38% F1-score for binary classifier. The proposed system can contribute a lot in diagnosing COVID-19 effectively in the medical field.

Saha Prottoy, Sadi Muhammad Sheikh, Aranya O F M Riaz Rahman, Jahan Sadia, Islam Ferdib-Al

2021

COVID-19, Deep learning, Transfer learning, VGG-16, X-ray images

Public Health Public Health

Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model.

In Remote sensing

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018-30 April 2019) and a pandemic semester (1 November 2019-30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).

Li Qiulun, Zhu Qingyang, Xu Muwu, Zhao Yu, Narayan K M Venkat, Liu Yang

2021-Apr

COVID-19, China, MAIAC AOD, PM2.5, air pollution, machine learning, random forest, remote sensing

General General

Loan default prediction of Chinese P2P market: a machine learning methodology.

In Scientific reports ; h5-index 158.0

Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements.

Xu Junhui, Lu Zekai, Xie Ying

2021-Sep-21

Public Health Public Health

Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression.

In Computers in biology and medicine

BACKGROUND AND OBJECTIVES : Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary.

METHODS : We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID-19.

RESULTS : The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition.

CONCLUSIONS : The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.

Blagojević Anđela, Šušteršič Tijana, Lorencin Ivan, Šegota Sandi Baressi, Anđelić Nikola, Milovanović Dragan, Baskić Danijela, Baskić Dejan, Petrović Nataša Zdravković, Sazdanović Predrag, Car Zlatan, Filipović Nenad

2021-Sep-14

COVID-19, Clinical condition assessment, Personalized model, Predictive blood biomarkers, Rule-based machine learning

General General

New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.

In IEEE transactions on neural networks and learning systems

The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies have been implemented to find potential anti-COVID-19 drugs in a short span of time. Motivated by this critical global challenge, in this review, we detail approaches that have been used for drug repurposing for COVID-19 and suggest improvements to the existing deep learning (DL) approach to identify and repurpose drugs to treat this complex disease. By optimizing hyperparameter settings, deploying suitable activation functions, and designing optimization algorithms, the improved DL approach will be able to perform feature extraction from quality big data, turning the traditional DL approach, referred to as a ``black box,'' which generalizes and learns the transmitted data, into a ``glass box'' that will have the interpretability of its rationale while maintaining a high level of prediction accuracy. When adopted for drug repurposing for COVID-19, this improved approach will create a new generation of DL approaches that can establish a cause and effect relationship as to why the repurposed drugs are suitable for treating COVID-19. Its ability can also be extended to repurpose drugs for other complex diseases, develop appropriate treatment strategies for new diseases, and provide precision medical treatment to patients, thus paving the way to discover new drugs that can potentially be effective for treating COVID-19.

Lee Chun Yen, Chen Yi-Ping Phoebe

2021-Sep-21

General General

Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.

In Journal of cancer research and clinical oncology

PURPOSE : Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by telemedicine photos, which are analogous to visual inspection, displaced part of in-person visits. This study evaluated by a dual convolutional neural network (CNN) performance metrics in dermoscopic (DI) versus smartphone-captured images (SI) and tested if artificial intelligence narrows the proclaimed gap in diagnostic accuracy.

METHODS : A CNN that receives a raw image and predicts malignancy, overlaid by a second independent CNN which processes a sonification (image-to-sound mapping) of the original image, were combined into a unified malignancy classifier. All images were histopathology-verified in a comparison between NMSC and benign skin lesions excised as suspected NMSCs. Study criteria outcomes were sensitivity and specificity for the unified output.

RESULTS : Images acquired by DI (n = 132 NMSC, n = 33 benign) were compared to SI (n = 170 NMSC, n = 28 benign). DI and SI analysis metrics resulted in an area under the curve (AUC) of the receiver operator characteristic curve of 0.911 and 0.821, respectively. Accuracy was increased by DI (0.88; CI 81.9-92.4) as compared to SI (0.75; CI 68.1-80.6, p < 0.005). Sensitivity of DI was higher than SI (95.3%, CI 90.4-98.3 vs 75.3%, CI 68.1-81.6, p < 0.001), but not specificity (p = NS).

CONCLUSION : Telemedicine use of smartphone images might result in a substantial decrease in diagnostic performance as compared to dermoscopy, which needs to be considered by both healthcare providers and patients.

Dascalu A, Walker B N, Oron Y, David E O

2021-Sep-21

Deep learning, Dermoscopy, Non-melanoma skin cancer, Preventive medicine, Sonification, Telemedicine

Radiology Radiology

Immune and cellular damage biomarkers to predict COVID-19 mortality in hospitalized patients.

In Current research in immunology

Early prediction of COVID-19 in-hospital mortality relies usually on patients' preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p < 0.05), international-normalized-ratio (p < 0.01), platelets, alanine-aminotransferase, creatinine (all p < 0.001). The primary outcome (60-day mortality) was 29.10% (n = 87) in the development dataset, and 39.55% (n = 159) in the external validation dataset. The performance of the 8 tested models on the external validation dataset were similar to that of the holdout test dataset, indicating that the models capture the key predictors of mortality. The shap analysis in both datasets showed that age, immune features (%lymphocytes, platelets) and LDH substantially impacted on all models' predictions, while creatinine and CRP varied among the different models. The model with the better performance was model 8 (60-day mortality AUROC 0.83 ± 0.06 in holdout test set, 0.79 ± 0.02 in external validation dataset). The features that had the greatest impact on this model's prediction were age, LDH, platelets, and %lymphocytes, more than comorbidities or inflammation markers, and these findings were highly consistent in both datasets, likely reflecting the virus effect at the very beginning of the disease.

Lombardi Carlo, Roca Elena, Bigni Barbara, Bertozzi Bruno, Ferrandina Camillo, Franzin Alberto, Vivaldi Oscar, Cottini Marcello, D’Alessio Andrea, Del Poggio Paolo, Conte Gian Marco, Berti Alvise

2021-Sep-16

COVID-19, CRP, Coronavirus, In-hospital death, LDH, Lymphocytes, Platelets, SARS-CoV-2

General General

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

RANLP-2021

While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis of Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweets express doubt, and 1/5 use loaded language, while English tweets are abundant in loaded language, exaggeration, fear, name-calling, doubt, and flag-waving. Finally, in terms of framing, Arabic tweets adopt a health and safety perspective, while in English economic concerns dominate.

Preslav Nakov, Firoj Alam, Shaden Shaar, Giovanni Da San Martino, Yifan Zhang

2021-09-22

Public Health Public Health

Using informative features in machine learning based method for COVID-19 drug repurposing.

In Journal of cheminformatics

Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

Aghdam Rosa, Habibi Mahnaz, Taheri Golnaz

2021-Sep-20

Clustering method, Coronavirus disease 2019, Protein−protein interaction, SARS-CoV-2

General General

The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation

ArXiv Preprint

Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of non-invasively estimating blood volume variations in the microvascular tissue from video - would be well suited for these situations. Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the 1st Vision for Vitals Challenge (V4V) presented a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. In this paper, we outline the evaluation protocol, the data used, and the results. V4V is to be held in conjunction with the 2021 International Conference on Computer Vision.

Ambareesh Revanur, Zhihua Li, Umur A. Ciftci, Lijun Yin, Laszlo A. Jeni

2021-09-22

General General

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study.

In Frontiers in immunology ; h5-index 100.0

Background : Prediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality.

Objective : To study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods.

Methods : Sixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease.

Results : On admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83-87% whether the patient will develop severe disease.

Conclusion : This study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide.

Krysko Olga, Kondakova Elena, Vershinina Olga, Galova Elena, Blagonravova Anna, Gorshkova Ekaterina, Bachert Claus, Ivanchenko Mikhail, Krysko Dmitri V, Vedunova Maria

2021

COVID-19, IL-6, artificial intelligence, macrophage derived cytokine, prediction models

Dermatology Dermatology

Identifying Silver Linings During the Pandemic Through Natural Language Processing.

In Frontiers in psychology ; h5-index 92.0

COVID-19 has presented an unprecedented challenge to human welfare. Indeed, we have witnessed people experiencing a rise of depression, acute stress disorder, and worsening levels of subclinical psychological distress. Finding ways to support individuals' mental health has been particularly difficult during this pandemic. An opportunity for intervention to protect individuals' health & well-being is to identify the existing sources of consolation and hope that have helped people persevere through the early days of the pandemic. In this paper, we identified positive aspects, or "silver linings," that people experienced during the COVID-19 crisis using computational natural language processing methods and qualitative thematic content analysis. These silver linings revealed sources of strength that included finding a sense of community, closeness, gratitude, and a belief that the pandemic may spur positive social change. People's abilities to engage in benefit-finding and leverage protective factors can be bolstered and reinforced by public health policy to improve society's resilience to the distress of this pandemic and potential future health crises.

Lossio-Ventura Juan Antonio, Lee Angela Yuson, Hancock Jeffrey T, Linos Natalia, Linos Eleni

2021

COVID-19, natural language processing, protective factors, sentiment analysis, silver linings, topic modeling

General General

Automatic deep learning system for COVID-19 infection quantification in chest CT.

In Multimedia tools and applications

The paper proposes an automatic deep learning system for COVID-19 infection areas segmentation in chest CT scans. CT imaging proved its ability to detect the COVID-19 disease even for asymptotic patients, which make it a trustworthy alternative for PCR. Coronavirus disease spread globally and PCR screening is the adopted diagnostic testing method for COVID-19 detection. However, PCR is criticized due its low sensitivity ratios, also, it is time-consuming and manual complicated process. The proposed framework includes different steps; it starts to prepare the region of interest by segmenting the lung organ, which then undergoes edge enhancing diffusion filtering (EED) to improve the infection areas contrast and intensity homogeneity. The proposed FCN is implemented using U-net architecture with modified residual block to include concatenation skip connection. The block improves the learning of gradient values by forwarding the infection area features through the network. The proposed system is evaluated using different measures and achieved dice overlapping score of 0.961 and 0.780 for lung and infection areas segmentation, respectively. The proposed system is trained and tested using many 2D CT slices extracted from diverse datasets from different sources, which demonstrate the system generalization and effectiveness. The use of more datasets from different sources helps to enhance the system accuracy and generalization, which can be accomplished based on the data availability in in the future.

Alirr Omar Ibrahim

2021-Sep-13

COVID-19 infection, Chest CT, Deep learning, Segmentation

General General

COVID-19: Government subsidy models for sustainable energy supply with disruption risks.

In Renewable & sustainable energy reviews

The outbreak of the COVID-19 pandemic poses great challenges to the current government subsidy models in the renewable energy sector for recovering in the post-pandemic economy. Although, many subsidy models have been applied to accelerate renewable energy investment decisions. However, it is important to develop a new model to ensure the sustainability of the renewable energy supply network under disruptions on both the supply and demand sides due to hazardous events. This study investigates different subsidy models (renewable credit, supplier subsidy, and retailer subsidy) to find a win-win subsidy model for sustainable energy supply under disruption risks. The objective is to determine the optimal capacity of renewable energy added to the grid, the optimal wholesale price of the power plant, and the optimal retail price of the aggregator under different subsidy models to maximize the economic, social, and environmental benefits of the whole network. A novel scenario-based robust fuzzy optimization approach is proposed to capture the uncertainties of business-as-usual operations (e.g., some relevant costs and demand) and hazardous events (e.g., COVID-19 pandemic). The proposed model is tested in a case study of the Vietnamese energy market. The results show that for a high negative impact level of hazardous events on the supply side, the renewable credit and supplier subsidy models should be considered to recovery the renewable energy market. Further, the proposed approach has a better performance in improving the power plant's robust profit for most of the hazard scenarios than the robust optimization model.

Tsao Yu-Chung, Thanh Vo-Van, Chang Yi-Ying, Wei Hsi-Hsien

2021-Oct

COVID-19, Government subsidy, Hazardous scenario, Renewable energy, Robust fuzzy model, Sustainable supply

General General

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images.

In Knowledge-based systems

Aim : By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor.

Method : To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks.

Results : Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models.

Conclusion : Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.

Sun Junding, Li Xiang, Tang Chaosheng, Wang Shui-Hua, Zhang Yu-Dong

2021-Sep-15

Biogeography-based optimization, COVID-19, Convolutional neural network, Deep learning, Pneumonia

General General

Adoption of Improved Neural Network Blade Pattern Recognition in Prevention and Control of Corona Virus Disease-19 Pandemic.

In Pattern recognition letters

To explore the adoption effect of improved neural network blade pattern in corona virus disease (COVID)-19, comparative analysis is implemented. First, the following hypotheses are proposed. I: in addition to the confirmed cases and deaths, people suspected of being infected are also involved in the spread of the epidemic. II: patients who have been cured may also develop secondary infections, so it is considered that there is still a link between cured cases and the spread of the epidemic. III: only the relevant data of the previous day is used to predict the epidemic prevention and control of the next day. Then, the epidemic data from February 1st to February 15th in X province were selected as the control. The combined neural network model is used for prevention and control prediction, and the prediction results of the traditional neural network model are compared. The results show that the predictions of the daily new cases by the five neural network models have little difference with the actual value, and the trend is basically consistent. However, there are still differences in some time nodes. The errors of neural network 1 on the 6th and network 3 on the 13th are large. The accuracy of the combined neural network prediction model is high, and there is little difference between the result and the actual value at each time node. The prediction of the cumulative number of diagnoses per day of the five neural network models is also analyzed, and the results are relatively ideal. In addition, the accuracy of the combined neural network prediction model is high, and the difference between the result and the actual value at each time node is relatively small. It is found that the standard deviations of neural networks 2 and 3 are relatively high through the comparison of the deviations. The deviation means of the five models were all relatively low, and the mean deviation and standard deviation of the combined neural network model are the lowest. It is found that the accuracy of prediction on the epidemic spread in this province is good by comparing the performance of each neural network model. Regarding various indicators, the prediction accuracy of the combined neural network model is higher than that of the other four models, and its performance is also the best. Finally, the MSE of the improved neural network model is lower compared with the traditional neural network model. Moreover, with the change of learning times, the change trend of MSE is constant (P <0.05 for all). In short, the improved neural network blade model has better performance compared with that of the traditional neural network blade model. The prediction results of the epidemic situation are accurate, and the application effect is remarkable, so the proposed model is worthy of further promotion and application in the medical field.

Ma Yanli, Li Zhonghua, Gou Jixiang, Ding Lihua, Yang Dong, Feng Guiliang

2021-Sep-15

artificial intelligence, corona virus disease (COVID)-19, improved neural network blade model, neural network model

General General

Software system to predict the infection in COVID-19 patients using deep learning and web of things.

In Software: practice & experience

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time-consuming Reverse Transcriptase polymerase chain reaction (RT-PCR) test; a new coronavirus 2019 (COVID-19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT-PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID-19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U-Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F-score of 0.96, which is best among state-of-the-art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice-coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

Singh Ashima, Kaur Amrita, Dhillon Arwinder, Ahuja Sahil, Vohra Harpreet

2021-Jun-24

COVID‐19, EfficientNet B0, SARSCoV‐2, U‐net, WoT, deep learning, segmentation

Public Health Public Health

Microplanning for designing vaccination campaigns in low-resource settings: A geospatial artificial intelligence-based framework.

In Vaccine ; h5-index 70.0

Existing campaign-based healthcare delivery programs used for immunization often fall short of established health coverage targets due to a lack of accurate estimates for population size and location. A microplan, an integrated set of detailed planning components, can be used to identify this information to support programs such as equitable vaccination efforts. Here, we presents a series of steps necessary to create an artificial intelligence-based framework for automated microplanning, and our pilot implementation of this analysis tool across 29 countries of the Americas. Further, we describe our processes for generating a conceptual framework, creating customized catchment areas, and estimating up-to-date populations to support microplanning for health campaigns. Through our application of the present framework, we found that 68 million individuals across the 29 countries are within 5 km of a health facility. The number of health facilities analyzed ranged from 2 in Peru to 789 in Argentina, while the total population within 5 km ranged from 1,233 in Peru to 15,304,439 in Mexico. Our results demonstrate the feasibility of using this methodological framework to support the development of customized microplans for health campaigns using open-source data in multiple countries. The pandemic is demanding an improved capacity to generate successful, efficient immunization campaigns; we believe that the steps described here can increase the automation of microplans in low resource settings.

Augusto Hernandes Rocha Thiago, Grapiuna de Almeida Dante, Shankar Kozhumam Arthi, Cristina da Silva Núbia, Bárbara Abreu Fonseca Thomaz Erika, Christine de Sousa Queiroz Rejane, de Andrade Luciano, Staton Catherine, Ricardo Nickenig Vissoci João

2021-Sep-15

COVID-19, Coronavirus, Health campaign, Microplan, Vaccination, Vaccine

General General

Latent Class Analysis Reveals COVID-19-related ARDS Subgroups with Differential Responses to Corticosteroids.

In American journal of respiratory and critical care medicine ; h5-index 108.0

Rationale Two distinct subphenotypes have been identified in acute respiratory distress syndrome (ARDS), but the presence of subgroups in ARDS associated with COVID-19 is unknown. The objective of this study was to identify clinically relevant, novel subgroups in COVID-19-related ARDS, and compare them to previously described ARDS subphenotypes. Methods Eligible participants were adults with COVID-19 and ARDS at Columbia University Irving Medical Center. Latent class analysis (LCA) was used to identify subgroups with baseline clinical, respiratory, and laboratory data serving as partitioning variables. A previously-developed machine learning model was used to classify patients as the hypoinflammatory and hyperinflammatory subphenotypes. Baseline characteristics and clinical outcomes were compared between subgroups. Heterogeneity of treatment effect (HTE) for corticosteroid-use in subgroups was tested. Measurements and Main Results From 3/2-4/30/2020, 483 patients with COVID-19-related ARDS met study criteria. A two-class LCA model best fit the population (p=0.0075). Class 2 (23%) had higher pro-inflammatory markers, troponin, creatinine and lactate, lower bicarbonate and lower blood pressure than Class 1 (77%). 90-day mortality was higher in Class 2 versus Class 1 (75% vs 48%; p<0.0001). Considerable overlap was observed between these subgroups and ARDS subphenotypes. SARS-CoV-2 RT-PCR cycle threshold was associated with mortality in the hypoinflammatory but not the hyperinflammatory phenotype. HTE to corticosteroids was observed (p=0.0295), with improved mortality in the hyperinflammatory phenotype and worse mortality in the hypoinflammatory phenotype, with the caveat that corticosteroid treatment was not randomized. Conclusions We identified two COVID-19-related ARDS subgroups with differential outcomes, similar to previously described ARDS subphenotypes. SARS-CoV-2 PCR cycle threshold had differential value for predicting mortality in the subphenotypes. The subphenotypes had differential treatment responses to corticosteroids. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Sinha Pratik, Furfaro David, Cummings Matthew J, Abrams Darryl, Delucchi Kevin, Maddali Manoj V, He June, Thompson Alison, Murn Michael, Fountain John, Rosen Amanda, Robbins-Juarez Shelief Y, Adan Matthew A, Satish Tejus, Madhavan Mahesh, Gupta Aakriti, Lyashchenko Alexander K, Agerstrand Cara, Yip Natalie H, Burkart Kristin M, Beitler Jeremy R, Baldwin Matthew R, Calfee Carolyn S, Brodie Daniel, O’Donnell Max R

2021-Sep-20

ARDS, COVID-19, Latent class analysis, Phenotyping

Public Health Public Health

Identification of high-risk COVID-19 patients using machine learning.

In PloS one ; h5-index 176.0

The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.

Quiroz-Juárez Mario A, Torres-Gómez Armando, Hoyo-Ulloa Irma, León-Montiel Roberto de J, U’Ren Alfred B

2021

General General

Ethics, Integrity and Retributions of Digital Detection Surveillance Systems on Infectious Diseases: Systematic literature review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic has raised the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases (ID). These opportunities create a "double-edge sword" as the ethical governance of such approaches often lag behind technological achievements.

OBJECTIVE : The aim was to investigate ethical issues identified from utilizing AI-augmented surveillance or early warning systems to monitor and detect common or novel ID outbreaks.

METHODS : We searched relevant articles in a number of databases that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems and/or big data analytics technology for detecting, monitoring, or tracing ID according to PRISMA guidelines, and further identified and analysed them with a theoretical framework.

RESULTS : This systematic review identified 29 articles presented in six major themes clustered under individual, organizational and societal levels, including: awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. Whilst these measures were understandable during a pandemic, the public were concerned about receiving inadequate information, unclear governance frameworks, and lack of privacy protection, data integrity and autonomy when utilizing ID digital surveillance. The barriers to engagement could widen existing healthcare disparities or digital divides by underrepresenting vulnerable and at-risk populations, and expose patients' highly sensitive data such as their movements and contacts to outside sources, impinging significantly upon basic human and civil rights.

CONCLUSIONS : Our findings inform ethical considerations for service delivery models for medical practitioners and policymakers implicated in the use of digital surveillance for ID spread and a basis for the global governance structure.

CLINICALTRIAL :

Zhao Yan Ivy, Ma Ye Xuan, Yu Man Wai Cecilia, Liu Jia, Dong Wei Nan, Pang Qin, Lu Xiao Qin, Molassiotis Alex, Holroyd Eleanor, Wong Chi Wai William

2021-Sep-14

Ophthalmology Ophthalmology

[Attitude of patients to possible telemedicine in ophthalmology : Survey by questionnaire in patients with glaucoma].

In Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft

BACKGROUND : The COVID-19 pandemic in 2020 and 2021 severely restricted the care of ophthalmology patients. Teleophthalmological services, such as video consultation or medical telephone advice could, at least partially, compensate for the lack of necessary controls in the case of chronic diseases; however, teleophthalmological options are currently still significantly underrepresented in Germany.

OBJECTIVE : In order to determine the willingness of patients to use telemedicine and the virtual clinic, we conducted a survey using a questionnaire on the subject of teleophthalmology in university medicine patients with known glaucoma as a chronic disease during the first wave of the COVID-19 pandemic.

METHODS : A total of100 patients were interviewed. The questionnaire contained 22 questions with multiple choice possible answers. The inclusion criterion was the presence of glaucoma as a chronic disease, age over 18 years, and sufficient linguistic understanding to answer the questions. The data were collected, analyzed and anonymously evaluated.

RESULTS : In the patient survey it could be shown that the respondents with glaucoma are very willing to do teleophthalmology and that this would be utilized. Of the patients surveyed 74.0% would accept telemedicine and virtual clinics. Of the ophthalmological patients surveyed 54.0% stated that their visit to the doctor/clinic could not take place due to SARS-CoV‑2 and 17.0% of the patients stated that the SARS-CoV‑2 pandemic had changed their opinion of telemedicine.

DISCUSSION : The acceptance of telemedicine in patients with chronic open-angle glaucoma seems surprisingly high. This has been increased even further by the SARS-CoV‑2 pandemic. These results reflect a general willingness of patients with chronic eye disease but do not reflect the applicability and acceptance and applicability from a medical point of view; however, this form of virtual consultation is accepted by the majority of patients with glaucoma and could be considered for certain clinical pictures.

Zwingelberg Sarah B, Mercieca Karl, Elksne Eva, Scheffler Stephanie, Prokosch Verena

2021-Sep-20

Artificial intelligence, Glaucoma, Ophthalmology, SARS-CoV‑2, Telemedicine

General General

Using Different Machine Learning Models to Classify Patients with Mild and Severe Cases of COVID-19 Based on Multivariate Blood Testing.

In Journal of medical virology

BACKGROUND : COVID19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system.

METHOD : Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from January 2020 to June 2021) to construct different machine learning models to classify patients with either mild or severe cases of COVID-19.

RESULTS : All models show good performance in the classification between COVID-19 patients with mild and severe disease. The AUC of the random forest model is 0.89, the AUC of the naive Bayes model is 0.90, the AUC of the support vector machine model is 0.86, and the AUC of the KNN model is 0.78, the AUC of the Logistic regression model is 0.84, and the AUC of the artificial neural network model is 0.87, among which the Naive Bayes model has the best performance.

CONCLUSION : Different machine learning models can classify patients with mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19. This article is protected by copyright. All rights reserved.

Zhang Rui-Kun, Xiao Qi, Zhu Sheng-Lang, Lin Hai-Yan, Tang Ming

2021-Sep-20

Artificial intelligence < Biostatistics & Bioinformatics, Coronavirus < Virus classification, Infection

Public Health Public Health

The future of zoonotic risk prediction.

In Philosophical transactions of the Royal Society of London. Series B, Biological sciences

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Carlson Colin J, Farrell Maxwell J, Grange Zoe, Han Barbara A, Mollentze Nardus, Phelan Alexandra L, Rasmussen Angela L, Albery Gregory F, Bett Bernard, Brett-Major David M, Cohen Lily E, Dallas Tad, Eskew Evan A, Fagre Anna C, Forbes Kristian M, Gibb Rory, Halabi Sam, Hammer Charlotte C, Katz Rebecca, Kindrachuk Jason, Muylaert Renata L, Nutter Felicia B, Ogola Joseph, Olival Kevin J, Rourke Michelle, Ryan Sadie J, Ross Noam, Seifert Stephanie N, Sironen Tarja, Standley Claire J, Taylor Kishana, Venter Marietjie, Webala Paul W

2021-Nov-08

access and benefit sharing, epidemic risk, global health, machine learning, viral ecology, zoonotic risk

General General

IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides for managing COVID-19 severity.

bioRxiv Preprint

Interleukin 13 (IL-13) is an immunoregulatory cytokine that is primarily released by activated T-helper 2 cells. It induces the pathogenesis of many allergic diseases, such as airway hyperresponsiveness, glycoprotein hypersecretion and goblet cell hyperplasia. IL-13 also inhibits tumor immunosurveillance, which leads to carcinogenesis. In recent studies, elevated IL-13 serum levels have been shown in severe COVID-19 patients. Thus it is important to predict IL-13 inducing peptides or regions in a protein for designing safe protein therapeutics particularly immunotherapeutic. This paper describes a method developed for predicting, designing and scanning IL-13 inducing peptides. The dataset used in this study contain experimentally validated 313 IL-13 inducing peptides and 2908 non-inducing homo-sapiens peptides extracted from the immune epitope database (IEDB). We have extracted 95 key features using SVC-L1 technique from the originally generated 9165 features using Pfeature. Further, these key features were ranked based on their prediction ability, and top 10 features were used for building machine learning prediction models. In this study, we have deployed various machine learning techniques to develop models for predicting IL-13 inducing peptides. These models were trained, test and evaluated using five-fold cross-validation techniques; best model were evaluated on independent dataset. Our best model based on XGBoost achieves a maximum AUC of 0.83 and 0.80 on the training and independent dataset, respectively. Our analysis indicate that certain SARS-COV2 variants are more prone to induce IL-13 in COVID-19 patients. A standalone package as well as a web server named IL-13Pred has been developed for predicting IL-13 inducing peptides (https://webs.iiitd.edu.in/raghava/il13pred/).

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

2021-09-21

General General

Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

In DIS. Designing Interactive Systems (Conference)

Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first co-analyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learning-based structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by co-researchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed.

Chapko Dorota, Rothstein Pedro, Emeh Lizzie, Frumiento Pino, Kennedy Donald, Mcnicholas David, Orjiekwe Ifeoma, Overton Michaela, Snead Mark, Steward Robyn, Sutton Jenny, Bradshaw Melissa, Jeffreys Evie, Gallia Will, Ewans Sarah, Williams Mark, Grierson Mick

2021-Jun

Human-centered computing → Human computer interaction (HCI), Learning disability, co-design, survey, topic model

Internal Medicine Internal Medicine

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany.

In Frontiers in artificial intelligence

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

Bai Tao, Zhu Xue, Zhou Xiang, Grathwohl Denise, Yang Pengshuo, Zha Yuguo, Jin Yu, Chong Hui, Yu Qingyang, Isberner Nora, Wang Dongke, Zhang Lei, Kortüm K Martin, Song Jun, Rasche Leo, Einsele Hermann, Ning Kang, Hou Xiaohua

2021

COVID-19, Wuhan cohort, Würzburg cohort, foresight, interpretability, mortality prediction model, reliability

General General

MirrorME: implementation of an IoT based smart mirror through facial recognition and personalized information recommendation algorithm.

In International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

We are living in the era of the fourth industrial revolution, which also treated as 4IR or Industry 4.0. Generally, 4IR considered as the mixture of robotics, artificial intelligence (AI), quantum computing, the Internet of Things (IoT) and other frontier technologies. It is obvious that nowadays a plethora of smart devices is providing services to make the daily life of humans easier. However, in the morning most people around the globe use a traditional mirror while preparing themselves for daily tasks. The aim is to build a low-cost intelligent mirror system that can display a variety of details based on user recommendations. Therefore, in this article, Internet of Things (IoT) and AI-based smart mirror is introduced that will support the users to receive the necessary daily update of weather information, date, time, calendar, to-do list, updated news headlines, traffic updates, COVID-19 cases status and so on. Moreover, a face detection method also implemented with the smart mirror to construct the architecture more secure. Our proposed MirrorME application provides a success rate of nearly 87% in interacting with the features of face recognition and voice input. The mirror is capable of delivering multimedia facilities while maintaining high levels of security within the device.

Uddin Khandaker Mohammad Mohi, Dey Samrat Kumar, Parvez Gias Uddin, Mukta Ayesha Siddika, Acharjee Uzzal Kumar

2021-Sep-12

4IR, Artificial intelligence, Authentication, Face detection, IoT, Smart mirror

General General

Experimental Technologies in the Diagnosis and Treatment of COVID-19 in Patients with Comorbidities.

In Journal of healthcare informatics research

The COVID-19 pandemic has impacted the whole world and raised concerns about its effects on different human organ systems. Early detection of COVID-19 may significantly increase the rate of survival; thus, it is critical that the disease is detected early. Emerging technologies have been used to prevent, diagnose, and manage COVID-19 among the populace in the USA and globally. Numerous studies have revealed the growing implementation of novel engineered systems during the intervention at various points of the disease's pathogenesis, especially as it relates to comorbidities and complications related to cardiovascular and respiratory organ systems. In this review, we provide a succinct, but extensive, review of the pathogenesis of COVID-19, particularly as it relates to angiotensin-converting enzyme 2 (ACE2) as a viral entry point. This is followed by a comprehensive analysis of cardiovascular and respiratory comorbidities of COVID-19 and novel technologies that are used to diagnose and manage hospitalized patients. Continuous cardiorespiratory monitoring systems, novel machine learning algorithms for rapidly triaging patients, various imaging modalities, wearable immunosensors, hotspot tracking systems, and other emerging technologies are reviewed. COVID-19 effects on the immune system, associated inflammatory biomarkers, and innovative therapies are also assessed. Finally, with emphasis on the impact of wearable and non-wearable systems, this review highlights future technologies that could help diagnose, monitor, and mitigate disease progression. Technologies that account for an individual's health conditions, comorbidities, and even socioeconomic factors can drastically reduce the high mortality seen among many COVID-19 patients, primarily via disease prevention, early detection, and pertinent management.

Amin Md Shahnoor, Wozniak Marcin, Barbaric Lidija, Pickard Shanel, Yerrabelli Rahul S, Christensen Anton, Coiado Olivia C

2021-Sep-15

Cardiovascular, Immune system, Innovation, Non-wearables, Pulmonary, Wearables

Public Health Public Health

Impact of COVID-19 on city-scale transportation and safety: An early experience from Detroit.

In Smart health (Amsterdam, Netherlands)

The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City---Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus and the rising number of COVID-19 confirmed cases and deaths. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goals of this work are: i) figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the City of Detroit, ii) determining whether each type of data (e.g. traffic volume data) could be a useful factor in the confirmed-cases prediction, and iii) providing an early future prediction method for COVID-19 rates, which can be a vital contributor to life-saving advanced preventative and preparatory responses. In addressing these problems, the prediction results of six feature groups are presented and analyzed to quantify the prediction effectiveness of each type of data. Then, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next week. The model demonstrated a promising prediction result with a coefficient of determination ( R 2 ) of up to approximately 0.91. Furthermore, six essential observations with supporting evidence are presented, which will be helpful for decision-makers to take specific measures that aid in preventing the spread of COVID-19 and protecting public health and safety. The proposed approaches could be applied, customized, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.

Yao Yongtao, Geara Tony G, Shi Weisong

2021-Nov

COVID-19, Daily cases Detroit, Data analysis, Prediction, Quarantine, Social distancing weather, Traffic volume crashes, Transportation networks

General General

Determination of COVID-19 Vaccine Hesitancy Among University Students.

In Cureus

Introduction With the sudden outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), vaccines appear to be the most efficient measure in combating spread. However, vaccines are only effective if a community collectively uptakes vaccination. This approach is growing increasingly difficult with the emergence of 'Vaccine Hesitancy.' This paper aims to determine the association between university curricula and the degree of hesitancy for the COVID-19 vaccine. Methods The online questionnaire assessed demographic data, prior knowledge of vaccines, attitude towards COVID-19 vaccines using an adapted version of the WHO Strategic Advisory Group of Experts (SAGE) Working Group's Vaccine Hesitancy Survey (VHS) and factors likely to motivate vaccine uptake. By using binary scoring, the degree of hesitancy among students was determined. Exploratory Factor Analysis (EFA) on VHS revealed underlying causes of hesitancy. To analyze the dependence between hesitancy and curriculum, a chi-squared test was conducted. Results Medical students scored higher for prior knowledge of vaccines (M = 3.54) as opposed to non-medical students (M = 3.49). Medical students responded favorably to COVID-19 vaccines with only 1.37% showing hesitancy for all nine items of VHS, compared to 2.55% of non-medical students. EFA produced three subscales within the VHS: lack of confidence, risk factor concern, and misinformation. The lack of confidence factor accounted for 65% of the data obtained. The chi-square test solidified that vaccine hesitancy is dependent on curriculum. Conclusion The majority of non-medical students showed hesitancy towards obtaining COVID-19 vaccines compared to medical students who were more willing, largely owing to their knowledge and understanding of vaccines.

Sadaqat Waliya, Habib Shanzay, Tauseef Ambreen, Akhtar Sheharyar, Hayat Meryum, Shujaat Syeda A, Mahmood Amina

2021-Aug

covid-19, curriculum, hesitancy, university students, vaccine

General General

Accuracy of health-related information regarding COVID-19 on Twitter during a global pandemic.

In World medical & health policy

This study was performed to analyze the accuracy of health-related information on Twitter during the coronavirus disease 2019 (COVID-19) pandemic. Authors queried Twitter on three dates for information regarding COVID-19 and five terms (cure, emergency or emergency room, prevent or prevention, treat or treatments, vitamins or supplements) assessing the first 25 results with health-related information. Tweets were authoritative if written by governments, hospitals, or physicians. Two physicians assessed each tweet for accuracy. Metrics were compared between accurate and inaccurate tweets using χ 2 analysis and Mann-Whitney U. A total of 25.4% of tweets were inaccurate. Accurate tweets were more likely written by Twitter authenticated authors (49.8% vs. 20.9%, 28.9% difference, 95% confidence interval [CI]: 17.7-38.2) with accurate tweet authors having more followers (19,491 vs. 7346; 3446 difference, 95% CI: 234-14,054) versus inaccurate tweet authors. Likes, retweets, tweet length, botometer scores, writing grade level, and rank order did not differ between accurate and inaccurate tweets. We found 1/4 of health-related COVID-19 tweets inaccurate indicating that the public should not rely on COVID-19 health information written on Twitter. Ideally, improved government regulatory authority, public/private industry oversight, independent fact-checking, and artificial intelligence algorithms are needed to ensure inaccurate information on Twitter is removed.

Swetland Sarah B, Rothrock Ava N, Andris Halle, Davis Bennett, Nguyen Linh, Davis Phil, Rothrock Steven G

2021-Jul-29

COVID‐19, pandemic, social media

General General

Data-driven operation of the resilient electric grid: A case of COVID-19.

In Journal of engineering (Stevenage, England)

Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID-19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi-fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML-driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID-19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID-19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID-19 pandemic.

Noorazar H, Srivastava A, Pannala S, K Sadanandan Sajan

2021-Aug-09

oncology Oncology

All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine.

In The EPMA journal

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

Wang Wei, Yan Yuxiang, Guo Zheng, Hou Haifeng, Garcia Monique, Tan Xuerui, Anto Enoch Odame, Mahara Gehendra, Zheng Yulu, Li Bo, Kang Timothy, Zhong Zhaohua, Wang Youxin, Guo Xiuhua, Golubnitschaja Olga

2021-Sep-13

Adolescence, Artificial intelligence (AI), Behavioural patterns, Big data management, Body mass index (BMI), COVID-19, Cancers, Cardiovascular disease, Communicable, Dietary habits, Epidemics, Glycan, Health economy, Health policy, Individualised patient profile, Lifestyle, Liquid biopsy, Medical ethics, Microbiome, Modifiable preventable risks, Mood disorders, Multi-level diagnostics, Multi-parametric analysis, Natural substances, Neurologic diseases, Non-communicable diseases, Omics, Pandemics, Periodontal health, Predictive preventive personalised medicine (PPPM/3PM), Risk assessment, Sleep medicine, Stress overload, Suboptimal health status (SHS), Traditional medicine

General General

Automated Detection of COVID-19 Cough.

In Biomedical signal processing and control

Easy detection of COVID-19 is a challenge. Quick biological tests do not give enough accuracy. Success in the fight against new outbreaks depends not only on the efficiency of the tests used, but also on the cost, time elapsed and the number of tests that can be done massively. Our proposal provides a solution to this challenge. The main objective is to design a freely available, quick and efficient methodology for the automatic detection of COVID-19 in raw audio files. Our proposal is based on automated extraction of time-frequency cough features and selection of the more significant ones to be used to diagnose COVID-19 using a supervised machine-learning algorithm. Random Forest has performed better than the other models analysed in this study. An accuracy close to 90% was obtained. This study demonstrates the feasibility of the automatic diagnose of COVID-19 from coughs, and its applicability to detecting new outbreaks.

Tena Alberto, Clarià Francesc, Solsona Francesc

2021-Sep-13

COVID-19, automated cough detection, diagnosis, signal processing, time-frequency.

General General

Utility and usability of an automated COVID-19 symptom monitoring system (CoSMoS) in primary care during COVID-19 pandemic: A qualitative feasibility study.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : COVID-19 telemonitoring applications have been developed and used in primary care to monitor patients quarantined at home. There is a lack of evidence on the utility and usability of telemonitoring applications from end-users' perspective.

OBJECTIVES : This study aimed to evaluate the feasibility of a COVID-19 symptom monitoring system (CoSMoS) by exploring its utility and usability with end-users.

METHODS : This was a qualitative study using in-depth interviews. Patients with suspected COVID-19 infection who used CoSMoS Telegram bot to monitor their COVID-19 symptoms and doctors who conducted the telemonitoring via CoSMoS dashboard were recruited. Universal sampling was used in this study. We stopped the recruitment when data saturation was reached. Patients and doctors shared their experiences using CoSMoS, its utility and usability for COVID-19 symptoms monitoring. Data were coded and analysed using thematic analysis.

RESULTS : A total of 11 patients and 4 doctors were recruited into this study. For utility, CoSMoS was useful in providing close monitoring and continuity of care, supporting patients' decision making, ensuring adherence to reporting, and reducing healthcare workers' burden during the pandemic. In terms of usability, patients expressed that CoSMoS was convenient and easy to use. The use of the existing social media application for symptom monitoring was acceptable for the patients. The content in the Telegram bot was easy to understand, although revision was needed to keep the content updated. Doctors preferred to integrate CoSMoS into the electronic medical record.

CONCLUSION : CoSMoS is feasible and useful to patients and doctors in providing remote monitoring and teleconsultation during the COVID-19 pandemic. The utility and usability evaluation enables the refinement of CoSMoS to be a patient-centred monitoring system.

Lim Hooi Min, Abdullah Adina, Ng Chirk Jenn, Teo Chin Hai, Valliyappan Indra Gayatri, Abdul Hadi Haireen, Ng Wei Leik, Noor Azhar Abdul Muhaimin, Chiew Thiam Kian, Liew Chee Sun, Chan Chee Seng

2021-Sep-06

COVID-19, Digital health, Monitoring system, Telemonitoring, Usability, Utility

Cardiology Cardiology

Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)

The American Journal of Cardiology, Volume 123, Issue 10, 15 May 2019, Pages 1681-1689

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index of 0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample t test and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and under-sampling strategies.We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naive Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes.

Moumita Bhattacharya, Dai-Yin Lu, Shibani M Kudchadkar, Gabriela Villarreal Greenland, Prasanth Lingamaneni, Celia P Corona-Villalobos, Yufan Guan, Joseph E Marine, Jeffrey E Olgin, Stefan Zimmerman, Theodore P Abraham, Hagit Shatkay, Maria Roselle Abraham

2021-09-19

Radiology Radiology

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial.

METHODS : We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models.

RESULTS : DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p<0.001). As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation.

CONCLUSIONS : DR-MIL can effectively represent the deep characteristics of COVID-19 lesions in CT images and accurately distinguish COVID-19 from CAP in a weakly supervised manner. The resulting DRIS is a useful supplement to visual interpretation of the spatial pattern of lesions when screening for COVID-19.

Qi Shouliang, Xu Caiwen, Li Chen, Tian Bin, Xia Shuyue, Ren Jigang, Yang Liming, Wang Hanlin, Yu Hui

2021-Sep-09

COVID-19, Community-acquired pneumonia, Convolutional neural network, Deep learning, Lung CT image, Multiple instance learning

General General

Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model

CJC Open, Volume 3, Issue 6, June 2021, Pages 801-813

Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low risk of congestive heart failure, hypertension, age, diabetes, previous stroke/transient ischemic attack scores. Hence, there is a need to understand the pathophysiology of AF and stroke in HCM. In this retrospective study, we develop and apply a data-driven, machine learning based method to identify AF cases, and clinical and imaging features associated with AF, using electronic health record data. HCM patients with documented paroxysmal/persistent/permanent AF (n = 191) were considered AF cases, and the remaining patients in sinus rhythm (n = 640) were tagged as No-AF. We evaluated 93 clinical variables and the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample t test and the information gain criterion. We identified 18 highly informative variables that are positively (n = 11) and negatively (n = 7) correlated with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of oversampling and under-sampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naive Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80). Our model is the first machine learning based method for identification of AF cases in HCM. This model demonstrates good performance, addresses data imbalance, and suggests that AF is associated with a more severe cardiac HCM phenotype.

Moumita Bhattacharya, Dai-Yin Lu, Ioannis Ventoulis, Gabriela V. Greenland, Hulya Yalcin, Yufan Guan, Joseph E. Marine, Jeffrey E. Olgin, Stefan L. Zimmerman, Theodore P. Abraham, M. Roselle Abraham, Hagit Shatkay

2021-09-19

General General

Generating insights in uncharted territories: real-time learning from data in critically ill patients-an implementer report.

In BMJ health & care informatics

Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data.Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure.Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future.

van de Sande Davy, Van Genderen Michel E, Huiskens Joost, Veen Robert E R, Meijerink Yvonne, Gommers Diederik, van Bommel Jasper

2021-Sep

COVID-19, artificial intelligence, critical care outcomes, data science, machine learning

General General

Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes

Journal of Biomedical Informatics Volume 82, June 2018, Pages 31-40

Patients associated with multiple co-occurring health conditions often face aggravated complications and less favorable outcomes. Co-occurring conditions are especially prevalent among individuals suffering from kidney disease, an increasingly widespread condition affecting 13% of the general population in the US. This study aims to identify and characterize patterns of co-occurring medical conditions in patients employing a probabilistic framework. Specifically, we apply topic modeling in a non-traditional way to find associations across SNOMEDCT codes assigned and recorded in the EHRs of>13,000 patients diagnosed with kidney disease. Unlike most prior work on topic modeling, we apply the method to codes rather than to natural language. Moreover, we quantitatively evaluate the topics, assessing their tightness and distinctiveness, and also assess the medical validity of our results. Our experiments show that each topic is succinctly characterized by a few highly probable and unique disease codes, indicating that the topics are tight. Furthermore, inter-topic distance between each pair of topics is typically high, illustrating distinctiveness. Last, most coded conditions grouped together within a topic, are indeed reported to co-occur in the medical literature. Notably, our results uncover a few indirect associations among conditions that have hitherto not been reported as correlated in the medical literature.

Moumita Bhattacharya, Claudine Jurkovitz, Hagit Shatkay

2021-09-19

Cardiology Cardiology

Application of artificial intelligence to the electrocardiogram.

In European heart journal ; h5-index 154.0

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Attia Zachi I, Harmon David M, Behr Elijah R, Friedman Paul A

2021-Sep-17

Artificial intelligence, Digital health, Electrocardiograms, Machine learning

General General

Applications of blockchain in the medical field: A narrative review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analysed blockchain applications from the perspectives of technology, business or patient care, few studies have focused on actual use-case scenarios of blockchain in healthcare. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice.

OBJECTIVE : This paper aims to provide a systematic review of the current and projected uses of blockchain technology in healthcare, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in healthcare is discussed.

METHODS : We searched databases such as PubMed, EMBASE, Scopus, IEEE and Springer using a combination of terms related to blockchain and healthcare. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarized the key medical scenarios using blockchain technology.

RESULTS : We found 1,647 relevant studies, of which 60 unique studies were included in this review. These studies report a variety of uses for blockchain, and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues and regulatory policy.

CONCLUSIONS : Blockchain technology can improve healthcare services in a decentralized, tamper-proof, transparent and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term health benefits. Not only can it be a mechanism to secure electronic health records (EHRs), but it provides a powerful tool that can empower users to control their own health data, enabling a fool-proof health data history and establishing medical responsibility.

Xie Yi, Zhang Jiayao, Wang Honglin, Liu Pengran, Liu Songxiang, Huo Tongtong, Duan Yu-Yu, Dong Zhe, Lu Lin, Ye Zhewei

2021-Sep-10

Radiology Radiology

COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet.

In Scientific reports ; h5-index 158.0

With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.

Javidi Malihe, Abbaasi Saeid, Naybandi Atashi Sara, Jampour Mahdi

2021-Sep-16

General General

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms.

In Artificial intelligence in medicine ; h5-index 34.0

COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diagnosis for fast detection of that disease. The study suggests a hybrid COVID-19 framework (named HMB-HCF) based on deep learning (DL), genetic algorithm (GA), weighted sum (WS), and majority voting principles in nine phases. Its segmentation phase suggests a lung segmentation algorithm using X-Ray images (named HMB-LSAXI) for extracting lungs. Its classification phase is built from a hybrid convolutional neural network (CNN) architecture using an abstractly-designed CNN (named HMB1-COVID19) and transfer learning (TL) pre-trained models (VGG16, VGG19, ResNet50, ResNet101, Xception, DenseNet121, DenseNet169, MobileNet, and MobileNetV2). The hybrid CNN architecture is used for learning, classification, and parameters optimization while GA is used to optimize the hyperparameters. This hybrid working mechanism is combined in an overall algorithm named HMB-DLGA. The study experiments implemented the WS approach to evaluate the models' performance using the loss, accuracy, F1-score, precision, recall, and area under curve (AUC) metrics with different pre-defined ratios. A collected, combined, and unified X-Ray dataset from 8 different public datasets was used alongside the regularization, dropout, and data augmentation techniques to limit the overall overfitting. The applied experiments reported state-of-the-art metrics. VGG16 reported 100% WS metric (i.e., 0.0097, 99.78%, 0.9984, 99.89%, 99.78%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the highest WS. It also reported a 99.92% WS metric (i.e., 0.0099, 99.84%, 0.9984, 99.84%, 99.84%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the last reported WS result. HMB-HCF was validated on 13 different public datasets to verify its generalization. The best-achieved metrics were compared with 13 related studies. These extensive experiments' target was the applicability verification and generalization.

Balaha Hossam Magdy, Balaha Magdy Hassan, Ali Hesham Arafat

2021-Sep

COVID-19, Classification, Convolutional neural network (CNN), Data augmentation (DA), Deep learning (DL), Genetic algorithms (GA), Optimization, Transfer learning (TL)

General General

3D virtual Histopathology of Cardiac Tissue from Covid-19 Patients based on Phase-Contrast X-ray Tomography

bioRxiv Preprint

For the first time, we have used phase-contrast x-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopatholocigal examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross section of 3:5mm were scanned at the synchrotron in a parallel beam configuration. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by scanning electron microscopy. Further, we implemented a fully automated analysis of the tissue structure in form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis.

Reichardt, M.; Moller Jensen, P.; Andersen Dahl, V.; Bjorholm Dahl, A.; Ackermann, M.; Shah, H.; Länger, F.; Werlein, C.; Kuehnel, M. P.; Jonigk, D.; Salditt, T.

2021-09-18

Public Health Public Health

Early Warning Diagnostics for Emerging Infectious Diseases in Developing into Late-Stage Pandemics.

In Accounts of chemical research ; h5-index 162.0

ConspectusThe spread of infectious diseases due to travel and trade can be seen throughout history, whether from early settlers or traveling businessmen. Increased globalization has allowed infectious diseases to quickly spread to different parts of the world and cause widespread infection. Posthoc analysis of more recent outbreaks-SARS, MERS, swine flu, and COVID-19-has demonstrated that the causative viruses were circulating through populations for days or weeks before they were first detected, allowing disease to spread before quarantines, contact tracing, and travel restrictions could be implemented. Earlier detection of future novel pathogens could decrease the time before countermeasures are enacted. In this Account, we examined a variety of novel technologies from the past 10 years that may allow for earlier detection of infectious diseases. We have arranged these technologies chronologically from prehuman predictive technologies to population-level screening tools. The earliest detection methods utilize artificial intelligence to analyze factors such as climate variation and zoonotic spillover as well as specific species and geographies to identify where the infection risk is high. Artificial intelligence can also be used to monitor health records, social media, and various publicly available data to identify disease outbreaks faster than traditional epidemiology. Secondary to predictive measures is monitoring infection in specific sentinel animal species, where domestic animals or wildlife are indicators of potential disease hotspots. These hotspots inform public health officials about geographic areas where infection risk in humans is high. Further along the timeline, once the disease has begun to infect humans, wastewater epidemiology can be used for unbiased sampling of large populations. This method has already been shown to precede spikes in COVID-19 diagnoses by 1 to 2 weeks. As total infections increase in humans, bioaerosol sampling in high-traffic areas can be used for disease monitoring, such as within an airport. Finally, as disease spreads more quickly between humans, rapid diagnostic technologies such as lateral flow assays and nucleic acid amplification become very important. Minimally invasive point-of-care methods can allow for quick adoption and use within a population. These individual diagnostic methods then transfer to higher-throughput methods for more intensive population screening as an infection spreads. There are many promising early warning technologies being developed. However, no single technology listed herein will prevent every future outbreak. A combination of technologies from across our infection timeline would offer the most benefit in preventing future widespread disease outbreaks and pandemics.

Oeschger Taylor M, McCloskey Duncan S, Buchmann Rose M, Choubal Aakash M, Boza Juan M, Mehta Saurabh, Erickson David

2021-Sep-15

General General

[Digitization of the healthcare system: the BfArM's contribution to the development of potential].

In Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz

Digitalization is a clear megatrend of our time, also in the health sector, which is currently experiencing enormous acceleration due to the COVID-19 pandemic in addition to paving the way due to changes in the legal framework. Looking to the future, this trend will contribute to further digitalization and the merging of individual digital products, including medicinal products and medical devices, into a digital ecosystem. This will be supported by ever-shorter development cycles and technological progress. Digitization will not only strengthen patient sovereignty, but also enable more patient-centered medicine; artificial intelligence will improve and accelerate diagnoses and will contribute to a better understanding of disease patterns and underlying mechanisms or causes.In order to continue to enable innovations in the future, to focus on emerging trends, and, above all, to further improve patient safety, the BfArM is contributing in many places to transforming the opportunities associated with digitalization into possibilities - without losing sight of the risks. The following is an overview of how, for example, the expansion of the Research Data Center, activities addressing interoperability, research projects using artificial intelligence, (inter-)national cooperation, the utilization and inclusion of "Real World Data" in our benefit/risk assessments, and the evaluation of digital health and digital care applications among other activities of the BfArM contribute to "digital readiness" in Germany and Europe.

Broich Karl, Löbker Wiebke, Lauer Wolfgang

2021-Sep-15

Artificial intelligence, BfArM, Big data, DiPA, Interoperability, Research data center

Public Health Public Health

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.

In JMIR mental health

BACKGROUND : Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored.

OBJECTIVE : We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up.

METHODS : The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning-based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status.

RESULTS : Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group.

CONCLUSIONS : Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers-passive-sensing of shifts in category-based social media app usage during the lockdown-can identify individuals at risk for psychiatric sequelae.

Ryu Jihan, Sükei Emese, Norbury Agnes, H Liu Shelley, Campaña-Montes Juan José, Baca-Garcia Enrique, Artés Antonio, Perez-Rodriguez M Mercedes

2021-Sep-15

COVID-19, anxiety disorder, digital phenotype, ecological momentary assessment, hidden Markov model, machine learning, public health, smartphone, social media

General General

Predicting vaccine hesitancy from area-level indicators: A machine learning approach.

In Health economics

Vaccine hesitancy (VH) might represent a serious threat to the next COVID-19 mass immunization campaign. We use machine learning algorithms to predict communities at a high risk of VH relying on area-level indicators easily available to policymakers. We illustrate our approach on data from child immunization campaigns for seven nonmandatory vaccines carried out in 6062 Italian municipalities in 2016. A battery of machine learning models is compared in terms of area under the receiver operating characteristics curve. We find that the Random Forest algorithm best predicts areas with a high risk of VH improving the unpredictable baseline level by 24% in terms of accuracy. Among the area-level indicators, the proportion of waste recycling and the employment rate are found to be the most powerful predictors of high VH. This can support policymakers to target area-level provaccine awareness campaigns.

Carrieri Vincenzo, Lagravinese Raffele, Resce Giuliano

2021-Sep-14

area-level indicators, machine learning, vaccine hesitancy

Public Health Public Health

Dataset of COVID-19 outbreak and potential predictive features in the USA.

In Data in brief

This dataset provides information related to the outbreak of COVID-19 disease in the United States, including data from each of 3142 US counties from the beginning of the outbreak (January 2020) until June 2021. This data is collected from many public online databases and includes the daily number of COVID-19 confirmed cases and deaths, as well as 46 features that may be relevant to the pandemic dynamics: demographic, geographic, climatic, traffic, public-health, social-distancing-policy adherence, and political characteristics of each county. We anticipate many researchers will use this dataset to train models that can predict the spread of COVID-19 and to identify the key driving factors.

Haratian Arezoo, Fazelinia Hadi, Maleki Zeinab, Ramazi Pouria, Wang Hao, Lewis Mark A, Greiner Russell, Wishart David

2021-Sep-10

COVID-19, Epidemiology, Machine learning, Predictive features

General General

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model.

In Cognitive neurodynamics

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Shankar K, Mohanty Sachi Nandan, Yadav Kusum, Gopalakrishnan T, Elmisery Ahmed M

2021-Sep-10

COVID-19, Classification, Deep learning, Fusion model, Optimal feature selection

General General

A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing.

In Neural computing & applications

Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.

Nasser Nidal, Emad-Ul-Haq Qazi, Imran Muhammad, Ali Asmaa, Razzak Imran, Al-Helali Abdulaziz

2021-Sep-10

Cloud computing, Coronavirus, Deep learning, Detection, Internet of things, Machine learning

General General

COVID-19 and Networks.

In New generation computing

Ongoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

Murata Tsuyoshi

2021-Sep-10

Epidemics, Influence maximization, Network science, Temporal networks

General General

Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19.

In BMJ health & care informatics

BACKGROUND : The COVID-19 pandemic has necessitated efficient and accurate triaging of patients for more effective allocation of resources and treatment.

OBJECTIVES : The objectives are to investigate parameters and risk stratification tools that can be applied to predict mortality within 90 days of hospital admission in patients with COVID-19.

METHODS : A literature search of original studies assessing systems and parameters predicting mortality of patients with COVID-19 was conducted using MEDLINE and EMBASE.

RESULTS : 589 titles were screened, and 76 studies were found investigating the prognostic ability of 16 existing scoring systems (area under the receiving operator curve (AUROC) range: 0.550-0.966), 38 newly developed COVID-19-specific prognostic systems (AUROC range: 0.6400-0.9940), 15 artificial intelligence (AI) models (AUROC range: 0.840-0.955) and 16 studies on novel blood parameters and imaging.

DISCUSSION : Current scoring systems generally underestimate mortality, with the highest AUROC values found for APACHE II and the lowest for SMART-COP. Systems featuring heavier weighting on respiratory parameters were more predictive than those assessing other systems. Cardiac biomarkers and CT chest scans were the most commonly studied novel parameters and were independently associated with mortality, suggesting potential for implementation into model development. All types of AI modelling systems showed high abilities to predict mortality, although none had notably higher AUROC values than COVID-19-specific prediction models. All models were found to have bias, including lack of prospective studies, small sample sizes, single-centre data collection and lack of external validation.

CONCLUSION : The single parameters established within this review would be useful to look at in future prognostic models in terms of the predictive capacity their combined effect may harness.

Chu Kelly, Alharahsheh Batool, Garg Naveen, Guha Payal

2021-Sep

COVID-19, healthcare sector, information management, medical informatics, patient care

General General

Densely connected attention network for diagnosing COVID-19 based on chest CT.

In Computers in biology and medicine

BACKGROUND : To fully enhance the feature extraction capabilities of deep learning models, so as to accurately diagnose coronavirus disease 2019 (COVID-19) based on chest CT images, a densely connected attention network (DenseANet) was constructed by utilizing the self-attention mechanism in deep learning.

METHODS : During the construction of the DenseANet, we not only densely connected attention features within and between the feature extraction blocks with the same scale, but also densely connected attention features with different scales at the end of the deep model, thereby further enhancing the high-order features. In this way, as the depth of the deep model increases, the spatial attention features generated by different layers can be densely connected and gradually transferred to deeper layers. The DenseANet takes CT images of the lung fields segmented by an improved U-Net as inputs and outputs the probability of the patients suffering from COVID-19.

RESULTS : Compared with exiting attention networks, DenseANet can maximize the utilization of self-attention features at different depths in the model. A five-fold cross-validation experiment was performed on a dataset containing 2993 CT scans of 2121 patients, and experiments showed that the DenseANet can effectively locate the lung lesions of patients infected with SARS-CoV-2, and distinguish COVID-19, common pneumonia and normal controls with an average of 96.06% Acc and 0.989 AUC.

CONCLUSIONS : The DenseANet we proposed can generate strong attention features and achieve the best diagnosis results. In addition, the proposed method of densely connecting attention features can be easily extended to other advanced deep learning methods to improve their performance in related tasks.

Fu Yu, Xue Peng, Dong Enqing

2021-Sep-09

Attention features, Chest CT, Deep learning, Diagnose COVID-19, Self-attention mechanism

General General

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

In The British journal of radiology

OBJECTIVES : For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes.

METHODS : In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis.

RESULTS : Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified.

CONCLUSIONS : Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management.

ADVANCES IN KNOWLEDGE : We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.

Varghese Bino Abel, Shin Heeseop, Desai Bhushan, Gholamrezanezhad Ali, Lei Xiaomeng, Perkins Melissa, Oberai Assad, Nanda Neha, Cen Steven, Duddalwar Vinay

2021-Sep-14

General General

Ethics for integrating emerging technologies to contain COVID-19 in Zimbabwe.

In Human behavior and emerging technologies

Zimbabwe is among the countries affected with the coronavirus disease (COVID-19) and implemented several infection control and measures such as social distancing, contact tracing, regular temperature checking in strategic entry and exit points, face masking among others. The country also implemented recursive national lockdowns and curfews to reduce the virus transmission rate and its catastrophic impact. These large-scale measures are not easy to implement, adhere to and subsequently difficult to practice and maintain which lead to imperfect public compliance, especially if there is a significant impact on social and political norms, economy, and psychological wellbeing of the affected population. Also, emerging COVID-19 variants, porous borders, regular movement of informal traders and sale of fake vaccination certificates continue to threaten impressive progress made towards virus containment. Therefore, several emerging technologies have been adopted to strengthen the health system and health services delivery, improve compliance, adherence and maintain social distancing. These technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation, and quarantine compliance. However, the use of emerging technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which impact their efficacy, adoption and ultimately influence public trust. Therefore, the study proposes ethical framework for using emerging technologies to contain the COVID-19 pandemic. The framework is centered on ethical practices such as security, privacy, justice, human dignity, autonomy, solidarity, beneficence, and non-maleficence.

Mbunge Elliot, Fashoto Stephen G, Akinnuwesi Boluwaji, Metfula Andile, Simelane Sakhile, Ndumiso Nzuza

2021-Aug-11

COVID‐19, artificial intelligence, digital technologies, ethical values, internet of medical things, social distancing monitoring tools

General General

A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19.

In BioMed research international ; h5-index 102.0

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

Ezugwu Absalom E, Hashem Ibrahim Abaker Targio, Oyelade Olaide N, Almutari Mubarak, Al-Garadi Mohammed A, Abdullahi Idris Nasir, Otegbeye Olumuyiwa, Shukla Amit K, Chiroma Haruna

2021

General General

Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning.

In International journal of imaging systems and technology

The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8 + T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models.

Elghamrawy Sally M, Hassanien Aboul Ella, Vasilakos Athanasios V

2021-Aug-13

artificial intelligence, classification algorithms, deep learning, evolutionary computation, genetic algorithms, hybrid intelligent systems, medical diagnostic, predictive model

General General

A novel and efficient deep learning approach for COVID-19 detection using X-ray imaging modality.

In International journal of imaging systems and technology

With the exponential growth of COVID-19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID-19, 2022 pneumonia, and 5863 normal chest X-ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre-processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.

Bhardwaj Prashant, Kaur Amanpreet

2021-Jul-21

Matthews correlation coefficients, deep learning models, simple averaging, weighted averaging

General General

The snoGloBe interaction predictor enables a broader study of box C/D snoRNA functions and mechanisms

bioRxiv Preprint

Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of noncoding RNA known to serve as guides for the site-specific 2'-O-ribose methylation of ribosomal RNAs and the U6 small nuclear RNA, through direct base pairing with the target. In recent years however, several examples of box C/D snoRNAs regulating different levels of gene expression including transcript stability and splicing have been reported. These regulatory interactions typically require direct binding of the target but do not always involve the guide region. Supporting these new box C/D snoRNA functions, high-throughput RNA-RNA interaction datasets detect many interactions between box C/D snoRNAs and messenger RNAs. To facilitate the study of box C/D snoRNA functionality, we created snoGloBe, a box C/D snoRNA machine learning target predictor based on a gradient boosting classifier and considering snoRNA and target sequence and position as well as target type. SnoGloBe convincingly outperforms general RNA duplex predictors and PLEXY, the only box C/D snoRNA-specific target predictor available. The study of snoGloBe human transcriptome-wide predictions identifies enrichment in snoRNA interactions in exons and on exon-intron junctions. Some specific snoRNAs are predicted to target groups of functionally-related transcripts on common regulatory elements and the exact position of the predicted targets strongly overlaps binding sites of RNA-binding proteins involved in relevant molecular functions. SnoGloBe was also applied to predicting interactions between human box C/D snoRNAs and the SARS-CoV-2 transcriptome, identifying known and novel interactions. Overall, snoGloBe is a timely new tool that will accelerate our understanding of C/D snoRNA targets and function.

Deschamps-Francoeur, G.; Couture, S.; Abou Elela, S.; Scott, M. S.

2021-09-15

General General

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning.

In Expert systems with applications

Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies.

Balaha Hossam Magdy, El-Gendy Eman M, Saafan Mahmoud M

2021-Dec-30

COVID-19, Computed Tomography (CT), Convolutional Neural Network (CNN), Data Augmentation (DA), Harris Hawks Optimization (HHO), Transfer Learning (TL)

General General

Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare.

In Multimedia systems

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread.

Qureshi Kashif Naseer, Alhudhaif Adi, Ali Moazam, Qureshi Maria Ahmed, Jeon Gwanggil

2021-Sep-07

Application, Challenges, Coronavirus, Deep learning, Detection, Diagnosis, Disease, Healthcare, Systems, Technologies

General General

COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus.

In Computers & industrial engineering

This paper proposes an efficient and accurate method to predict coronavirus disease 19 (COVID-19) based on the genome similarity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a bat SARS-CoV-like coronavirus. We introduce similarity features to distinguish COVID-19 from other human coronaviruses by comparing human coronaviruses with a bat SARS-CoV-like coronavirus. In the proposed method each human coronavirus sequence is assigned to three similarity scores considering nucleotide similarities and mutations that lead to the strong absence of cytosine and guanine nucleotides. Next the proposed features are integrated with CpG island features of the genome sequences to improve COVID-19 prediction. Thus each genome sequence is represented by five real numbers. We exhibit the effectiveness of the proposed features using six machine learning classifiers on a dataset including the genome sequences of human coronaviruses similar to SARS-CoV-2. The performances of the machine learning classifiers are close to each other and k-nearest neighbor classifier with similarity features achieves the best results with an accuracy of 99.2%. Moreover, k-nearest neighbor classifier with the integration of CpG based and similarity features has an admirable performance and achieves an accuracy of 99.8%. Experimental results demonstrate that similarity features remarkably decreases the number of false negatives and significantly improve the overall performance. The superiority of the proposed method is also highlighted by comparing with the state-of-the-art studies detecting COVID-19 from genome sequences.

Arslan Hilal

2021-Sep-08

Covid-19, CpG islands, Feature extraction, Machine learning methods, SARS-CoV-2, Similarity

General General

A comprehensive survey of recent trends in deep learning for digital images augmentation.

In Artificial intelligence review

Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.

Khalifa Nour Eldeen, Loey Mohamed, Mirjalili Seyedali

2021-Sep-04

Artificial Intelligence, Data augmentation, Deep learning, GAN, Image augmentation, Machine Learning

General General

Blockchain technology: A DNN token-based approach in healthcare and COVID-19 to generate extracted data.

In Expert systems

The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.

Mallikarjuna Basetty, Shrivastava Gulshan, Sharma Meenakshi

2021-Jul-23

Blockchain, COVID‐19 pandemic, deep neural network, electronic health records, feature extracted data

General General

COVID-19 diagnosis system by deep learning approaches.

In Expert systems

The novel coronavirus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest X-Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID-19 patient dataset. The evaluation results are generated using a fourfold cross-validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1-score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.

Bhuyan Hemanta Kumar, Chakraborty Chinmay, Shelke Yogesh, Pani Suvendu Kumar

2021-Jul-29

COVID‐19, X‐Rays or CT images, quantitative evaluation, respiratory diagnosis

General General

Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.

In Expert systems

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

Alyasseri Zaid Abdi Alkareem, Al-Betar Mohammed Azmi, Doush Iyad Abu, Awadallah Mohammed A, Abasi Ammar Kamal, Makhadmeh Sharif Naser, Alomari Osama Ahmad, Abdulkareem Karrar Hameed, Adam Afzan, Damasevicius Robertas, Mohammed Mazin Abed, Zitar Raed Abu

2021-Jul-28

2019‐nCoV, COVID‐19, COVID‐19 dataset, deep learning, machine learning

General General

Nature-Inspired Solution for Coronavirus Disease Detection and its Impact on Existing Healthcare Systems.

In Computers & electrical engineering : an international journal

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing.  Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy.

Qureshi Kashif Naseer, Alhudhaif Adi, Qureshi Maria Ahmed, Jeon Gwanggil

2021-Sep-06

Challenges, Coronavirus, Disease, Healthcare, Nature Inspired Solutions, Systems, Technologies

General General

Application of machine learning in CT images and X-rays of COVID-19 pneumonia.

In Medicine

Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-ray and CT images, assist doctors in improving diagnosis efficiency, and facilitate the subsequent assessment of the severity of the patient infection. The medical assistant platform based on machine learning can help radiologists make clinical decisions and helper in screening, diagnosis, and treatment. By providing scientific methods for image recognition, segmentation, and evaluation, we summarized the latest developments in the application of artificial intelligence in COVID-19 lung imaging, and provided guidance and inspiration to researchers and doctors who are fighting the COVID-19 virus.

Zhang Fengjun

2021-Sep-10

General General

Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals.

In Health informatics journal ; h5-index 25.0

The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.

Carmo Diedre, Campiotti Israel, Rodrigues Lívia, Fantini Irene, Pinheiro Gustavo, Moraes Daniel, Nogueira Rodrigo, Rittner Leticia, Lotufo Roberto

COVID-19, decision-support systems, deployment, machine learning, medical imaging

Surgery Surgery

From Other Journals: A Review of Recent Articles by Our Editorial Team.

In Pediatric cardiology

In this review we provide a brief description of recently published articles addressing topics relevant to pediatric cardiologists. Our hope is to provide a summary of the latest articles published recently in other journals in our field. The articles address (1) a summary of a scientific statement of the American Heart Association for diagnosis and treatment of myocarditis, (2) development of a perioperative risk score for in-hospital mortality after cardiac surgery in adults with congenital heart disease, (3) using a machine learning algorithm to predict cardiopulmonary deterioration in patients in the interstage period 1-2 h in advance using hospital monitor generated data, (4) risk factors for reoperation after the arterial switch operation, (5) the effect of mitochondrial transplantation for cardiogenic shock in pediatric patients, (6) comparing outcomes of primary or staged repair in tetralogy of Fallot with pulmonary atresia.

Alsaied Tarek, Ashfaq Awais

2021-Sep-13

Atrioventricular canal, Atrioventricular septal defect, COVID-19, Collaterals, Fontan, Heart transplantation, Norwood, Tetralogy of Fallot

General General

A Tale of Two Cities: COVID-19 and the Emotional Well-Being of Student-Athletes Using Natural Language Processing.

In Frontiers in sports and active living

Student-athletes at the Division I institutions face a slew of challenges and stressors that can have negative impacts in eliciting different emotional responses during the COVID-19 pandemic. We employed machine-learning-based natural language processing techniques to analyze the user-generated content posted on Twitter of Atlantic Coast Conference (ACC) student-athletes to study changes in their sentiment as it relates to the COVID-19 crisis, major societal events, and policy decisions. Our analysis found that positive sentiment slightly outweighed negative sentiment overall, but that there was a noticeable uptick in negative sentiment in May and June 2020 in conjunction with the Black Lives Matter protests. The most commonly expressed emotions by these athletes were joy, trust, anticipation, and fear, suggesting that they used social media as an outlet to share primarily optimistic sentiments, while still publicly expressing strong negative sentiments like fear and trepidation about the pandemic and other important contemporary events. Athletic administrators, ACC coaches, support staff, and other professionals can use findings like these to guide sound, evidence-based decision-making and to better track and promote the emotional wellness of student-athletes.

Floyd Carter, Gulavani Susmit S, Du James, Kim Amy C H, Pappas Jason

2021

COVID-19, emotional well-being, machine learning, natural language processing, sentiment analysis, student-athletes

General General

Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data.

In Frontiers in artificial intelligence

The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672, 000 confirmed cases and at least 36, 000 reported deaths by June 2020. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 8, 443 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a Receiver Operating Characteristic AUC of 0.92, Sensitivity of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.

De Souza Fernanda Sumika Hojo, Hojo-Souza Natália Satchiko, Dos Santos Edimilson Batista, Da Silva Cristiano Maciel, Guidoni Daniel Ludovico

2021

Brazil, COVID-19, disease outcome, machine learning, prediction model

General General

Clinical Risk Factors for Mortality Among Critically Ill Mexican Patients With COVID-19.

In Frontiers in medicine

Little literature exists about critically ill patients with coronavirus disease 2019 (COVID-19) from Latin America. Here, we aimed to describe the clinical characteristics and mortality risk factors in mechanically ventilated COVID-19 patients from Mexico. For this purpose, we recruited 67 consecutive mechanically ventilated COVID-19 patients which were grouped according to their clinical outcome (survival vs. death). Clinical risk factors for mortality were identified by machine-learning and logistic regression models. The median age of participants was 42 years and 65% were men. The most common comorbidity observed was obesity (49.2%). Fever was the most frequent symptom of illness (88%), followed by dyspnea (84%). Multilobe ground-glass opacities were observed in 76% of patients by thoracic computed tomography (CT) scan. Fifty-two percent of study participants were ventilated in prone position, and 59% required cardiovascular support with norepinephrine. Furthermore, 49% of participants were coinfected with a second pathogen. Two-thirds of COVID-19 patients developed acute kidney injury (AKIN). The mortality of our cohort was 44.7%. AKIN, uric acid, lactate dehydrogenase (LDH), and a longitudinal increase in the ventilatory ratio were associated with mortality. Baseline PaO2/FiO2 values and a longitudinal recovery of lymphocytes were protective factors against mortality. Our study provides reference data about the clinical phenotype and risk factors for mortality in mechanically ventilated Mexican patients with COVID-19.

Hernández-Cárdenas Carmen M, Choreño-Parra José Alberto, Torruco-Sotelo Carlos, Jurado Felipe, Serna-Secundino Héctor, Aguilar Cristina, García-Olazarán José G, Hernández-García Diana, Choreño-Parra Eduardo M, Zúñiga Joaquín, Lugo-Goytia Gustavo

2021

ARDS, COVID-19, SARS-CoV-2, mortality, risk factors

Public Health Public Health

Classification of Lung Disease in Children by Using Lung Ultrasound Images and Deep Convolutional Neural Network.

In Frontiers in physiology

Bronchiolitis is the most common cause of hospitalization of children in the first year of life and pneumonia is the leading cause of infant mortality worldwide. Lung ultrasound technology (LUS) is a novel imaging diagnostic tool for the early detection of respiratory distress and offers several advantages due to its low-cost, relative safety, portability, and easy repeatability. More precise and efficient diagnostic and therapeutic strategies are needed. Deep-learning-based computer-aided diagnosis (CADx) systems, using chest X-ray images, have recently demonstrated their potential as a screening tool for pulmonary disease (such as COVID-19 pneumonia). We present the first computer-aided diagnostic scheme for LUS images of pulmonary diseases in children. In this study, we trained from scratch four state-of-the-art deep-learning models (VGG19, Xception, Inception-v3 and Inception-ResNet-v2) for detecting children with bronchiolitis and pneumonia. In our experiments we used a data set consisting of 5,907 images from 33 healthy infants, 3,286 images from 22 infants with bronchiolitis, and 4,769 images from 7 children suffering from bacterial pneumonia. Using four-fold cross-validation, we implemented one binary classification (healthy vs. bronchiolitis) and one three-class classification (healthy vs. bronchiolitis vs. bacterial pneumonia) out of three classes. Affine transformations were applied for data augmentation. Hyperparameters were optimized for the learning rate, dropout regularization, batch size, and epoch iteration. The Inception-ResNet-v2 model provides the highest classification performance, when compared with the other models used on test sets: for healthy vs. bronchiolitis, it provides 97.75% accuracy, 97.75% sensitivity, and 97% specificity whereas for healthy vs. bronchiolitis vs. bacterial pneumonia, the Inception-v3 model provides the best results with 91.5% accuracy, 91.5% sensitivity, and 95.86% specificity. We performed a gradient-weighted class activation mapping (Grad-CAM) visualization and the results were qualitatively evaluated by a pediatrician expert in LUS imaging: heatmaps highlight areas containing diagnostic-relevant LUS imaging-artifacts, e.g., A-, B-, pleural-lines, and consolidations. These complex patterns are automatically learnt from the data, thus avoiding hand-crafted features usage. By using LUS imaging, the proposed framework might aid in the development of an accessible and rapid decision support-method for diagnosing pulmonary diseases in children using LUS imaging.

Magrelli Silvia, Valentini Piero, De Rose Cristina, Morello Rosa, Buonsenso Danilo

2021

bronchiolitis, children, deep-learning CNN, lung ultrasonography, pneumonia

General General

Home-Use and Real-Time Sleep-Staging System Based on Eye Masks and Mobile Devices with a Deep Learning Model.

In Journal of medical and biological engineering

Purpose : Sleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID‑19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper.

Methods : We developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing.

Results : The averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements.

Conclusion : These experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.

Hsieh Tsung-Hao, Liu Meng-Hsuan, Kuo Chin-En, Wang Yung-Hung, Liang Sheng-Fu

2021-Sep-04

Automatic sleep-staging method, EEG, EOG, Eye mask, Home use, Mobile platform, MobileNetV2, Real time

General General

An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images.

In Applied soft computing

In recent times,COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, image pre-processing is applied to enhance the quality of the image. Next, the CRNN model is applied as feature extraction and the hyperparameter tuning of CRNN takes place via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.

Shankar K, Perumal Eswaran, Díaz Vicente García, Tiwari Prayag, Gupta Deepak, Saudagar Abdul Khader Jilani, Muhammad Khan

2021-Sep-08

COVID-19, Decision making, Deep learning, Evolutionary computing, Hyperparameter tuning, Intelligent systems, Soft computing

Public Health Public Health

An integrated framework for COVID-19 classification based on classical and quantum transfer learning from a chest radiograph.

In Concurrency and computation : practice & experience

COVID-19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID-19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre-trained models like AlexNet and MobileNet in phase-I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase-II, quantum transfer learning model is utilized, in which a pre-trained ResNet-18 model is applied for DF collection and then these features are supplied as an input to the 4-qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x-ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus-positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

Umer Muhammad Junaid, Amin Javeria, Sharif Muhammad, Anjum Muhammad Almas, Azam Faisal, Shah Jamal Hussain

2021-Jun-29

COVID‐19, SVM, classification, deep features, feature selection, fusion, quantum

Public Health Public Health

Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models.

In Transactions in GIS : TG

In order to find useful intervention strategies for the novel coronavirus (COVID-19), it is vital to understand how the disease spreads. In this study, we address the modeling of COVID-19 spread across space and time, which facilitates understanding of the pandemic. We propose a hybrid data-driven learning approach to capture the mobility-related spreading mechanism of infectious diseases, utilizing multi-sourced mobility and attributed data. This study develops a visual analytic approach that identifies and depicts the strength of the transmission pathways of COVID-19 between areal units by integrating data-driven deep learning and compartmental epidemic models, thereby engaging stakeholders (e.g., public health officials, managers from transportation agencies) to make informed intervention decisions and enable public messaging. A case study in the state of Colorado, USA was performed to demonstrate the applicability of the proposed transmission modeling approach in understanding the spatio-temporal spread of COVID-19 at the neighborhood level. Transmission path maps are presented and analyzed, demonstrating their utility in evaluating the effects of mitigation measures. In addition, integrated embeddings also support daily prediction of infected cases and role analysis of each area unit during the transmission of the virus.

Zhang Tong, Li Jing

2021-Jul-16

General General

Dynamic activity chain pattern estimation under mobility demand changes during COVID-19.

In Transportation research. Part C, Emerging technologies

During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases.

Liu Yan, Tong Lu Carol, Zhu Xi, Du Wenbo

2021-Oct

Activity chain, Discrete choice model, Machine learning, Pandemic, Travel behaviour

General General

Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images.

In Computers in biology and medicine

The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application.

Pathan Sameena, Siddalingaswamy P C, Kumar Preetham, Pai M M Manohara, Ali Tanweer, Acharya U Rajendra

2021-Sep-06

BGWO, CNN, Covid-19, DST, Ensemble, GWO, Hyperparameters, WOA

General General

ULNet for the detection of coronavirus (COVID-19) from chest X-ray images.

In Computers in biology and medicine

Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem of a shortage of testing kits and resources, and it is necessary to use other diagnostic methods as an alternative to these test kits. In this paper, we propose a convolutional neural network (CNN) model (ULNet) to detect COVID-19 using chest X-ray images. The proposed architecture is constructed by adding a new downsampling side, skip connections and fully connected layers on the basis of U-net. Because the shape of the network is similar to UL, it is named ULNet. This model is trained and tested on a publicly available Kaggle dataset (consisting of a combination of 219 COVID-19, 1314 normal and 1345 viral pneumonia chest X-ray images), including binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Normal vs. Viral Pneumonia). The accuracy of the proposed model in the detection of COVID-19 in the binary-class and multiclass tasks is 99.53% and 95.35%, respectively. Based on these promising results, this method is expected to help doctors diagnose and detect COVID-19. Overall, our ULNet provides a quick method for identifying patients with COVID-19, which is conducive to the control of the COVID-19 pandemic.

Wu Tianbo, Tang Chen, Xu Min, Hong Nian, Lei Zhenkun

2021-Sep-04

COVID-19, Chest X-ray images, Classification, Deep learning, ULNet

General General

A randomized controlled trial of a therapeutic relational agent for reducing substance misuse during the COVID-19 pandemic.

In Drug and alcohol dependence ; h5-index 64.0

BACKGROUND : The COVID-19 pandemic disrupted access to treatment for substance use disorders (SUDs), while alcohol and cannabis retail sales increased. During the pandemic, we tested a tailored digital health solution, Woebot-SUDs (W-SUDs), for reducing substance misuse.

METHODS : In a randomized controlled trial, we compared W-SUDs for 8 weeks to a waitlist control. U.S. adults (N = 180) who screened positive for substance misuse (CAGE-AID>1) were enrolled June-August 2020. The primary outcome was the change in past-month substance use occasions from baseline to end-of-treatment (EOT). Study retention was 84%. General linear models tested group differences in baseline-to-EOT change scores, adjusting for baseline differences and attrition.

RESULTS : At baseline, the sample (age M = 40, SD = 12, 65% female, 68% non-Hispanic white) averaged 30.2 (SD = 18.6) substance occasions in the past month. Most (77%) reported alcohol problems, 28% cannabis, and 45% multiple substances; 46% reported moderate-to-severe depressive symptoms. Treatment participants averaged 920 in-app text messages (SD = 892, Median = 701); 96% of completed lessons were rated positively; and 88% would recommend W-SUDs. Relative to waitlist, W-SUDs participants significantly reduced past-month substance use occasions (M = -9.1, SE = 2.0 vs. M = -3.3, SE = 1.8; p = .039). Secondary substance use and mood outcomes did not change significantly by group; however, reductions in substance use occasions correlated significantly with increased confidence and fewer substance use problems, cravings, depression and anxiety symptoms, and pandemic-related mental health effects (p-value<.05).

CONCLUSIONS : W-SUDs was associated with significant reductions in substance use occasions. Reduction in substance use occasions was associated with better outcomes, including improved mental health. W-SUDs satisfaction was high.

Prochaska Judith J, Vogel Erin A, Chieng Amy, Baiocchi Michael, Maglalang Dale Dagar, Pajarito Sarah, Weingardt Kenneth R, Darcy Alison, Robinson Athena

2021-Aug-27

Artificial intelligence, COVID-19 pandemic, Randomized controlled trial, Relational conversational agent, Substance-related disorders

General General

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality.

In World psychiatry : official journal of the World Psychiatric Association (WPA)

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

Torous John, Bucci Sandra, Bell Imogen H, Kessing Lars V, Faurholt-Jepsen Maria, Whelan Pauline, Carvalho Andre F, Keshavan Matcheri, Linardon Jake, Firth Joseph

2021-Oct

chatbots, digital health, digital phenotyping, implementation, mHealth, mental health, psychiatry, smartphone apps, social media, virtual reality

General General

Biomarkers and Immune Repertoire Metrics Identified by Peripheral Blood Transcriptomic Sequencing Reveal the Pathogenesis of COVID-19.

In Frontiers in immunology ; h5-index 100.0

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global crisis; however, our current understanding of the host immune response to SARS-CoV-2 infection remains limited. Herein, we performed RNA sequencing using peripheral blood from acute and convalescent patients and interrogated the dynamic changes of adaptive immune response to SARS-CoV-2 infection over time. Our results revealed numerous alterations in these cohorts in terms of gene expression profiles and the features of immune repertoire. Moreover, a machine learning method was developed and resulted in the identification of five independent biomarkers and a collection of biomarkers that could accurately differentiate and predict the development of COVID-19. Interestingly, the increased expression of one of these biomarkers, UCHL1, a molecule related to nervous system damage, was associated with the clustering of severe symptoms. Importantly, analyses on immune repertoire metrics revealed the distinct kinetics of T-cell and B-cell responses to SARS-CoV-2 infection, with B-cell response plateaued in the acute phase and declined thereafter, whereas T-cell response can be maintained for up to 6 months post-infection onset and T-cell clonality was positively correlated with the serum level of anti-SARS-CoV-2 IgG. Together, the significantly altered genes or biomarkers, as well as the abnormally high levels of B-cell response in acute infection, may contribute to the pathogenesis of COVID-19 through mediating inflammation and immune responses, whereas prolonged T-cell response in the convalescents might help these patients in preventing reinfection. Thus, our findings could provide insight into the underlying molecular mechanism of host immune response to COVID-19 and facilitate the development of novel therapeutic strategies and effective vaccines.

Liu Yang, Wu Yankang, Liu Bing, Zhang Youpeng, San Dan, Chen Yu, Zhou Yu, Yu Long, Zeng Haihong, Zhou Yun, Zhou Fuxiang, Yang Heng, Yin Lei, Huang Yafei

2021

IgG, SARS-CoV-2, biomarker, machine learning, transcriptomic characteristics

General General

Anthraquinolone and quinolizine derivatives as an alley of future treatment for COVID-19: an in silico machine learning hypothesis.

In Scientific reports ; h5-index 158.0

Coronavirus disease 2019 (Covid-19), caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has come to the fore in Wuhan, China in December 2019 and has been spreading expeditiously all over the world due to its high transmissibility and pathogenicity. From the outbreak of COVID-19, many efforts are being made to find a way to fight this pandemic. More than 300 clinical trials are ongoing to investigate the potential therapeutic option for preventing/treating COVID-19. Considering the critical role of SARS-CoV-2 main protease (Mpro) in pathogenesis being primarily involved in polyprotein processing and virus maturation, it makes SARS-CoV-2 main protease (Mpro) as an attractive and promising antiviral target. Thus, in our study, we focused on SARS-CoV-2 main protease (Mpro), used machine learning algorithms and virtually screened small derivatives of anthraquinolone and quinolizine from PubChem that may act as potential inhibitor. Prioritisation of cavity atoms obtained through pharmacophore mapping and other physicochemical descriptors of the derivatives helped mapped important chemical features for ligand binding interaction and also for synergistic studies with molecular docking. Subsequently, these studies outcome were supported through simulation trajectories that further proved anthraquinolone and quinolizine derivatives as potential small molecules to be tested experimentally in treating COVID-19 patients.

Samarth Nikhil, Kabra Ritika, Singh Shailza

2021-Sep-09

General General

Remdesivir and EIDD-1931 Interact with Human Equilibrative Nucleoside Transporters 1 and 2: Implications for Reaching SARS-CoV-2 Viral Sanctuary Sites.

In Molecular pharmacology

Equilibrative nucleoside transporters (ENTs) are present at the blood-testis barrier (BTB), where they can facilitate antiviral drug disposition to eliminate a sanctuary site for viruses detectable in semen. The purpose of this study was to investigate ENT-drug interactions with three nucleoside analogs remdesivir, molnupiravir and its active metabolite, EIDD-1931 and four non-nucleoside molecules repurposed as antivirals for COVID-19. The study used 3D pharmacophores for ENT1 and ENT2 substrates and inhibitors and Bayesian machine learning models to identify potential interactions with these transporters. In vitro transport experiments demonstrated that remdesivir was the most potent inhibitor of ENT-mediated [3H] uridine uptake (ENT1 IC50: 38.65 mM; ENT2 IC50: 76.72 mM), followed by EIDD-1931 (ENT1 IC50: 258.9 mM; ENT2 IC50: 467.3 mM), while molnupiravir was a modest inhibitor (ENT1 IC50: 701.0 mM; ENT2 IC50: 851.4 mM). Other proposed antivirals failed to inhibit ENT-mediated [3H] uridine uptake below 1 mM. Remdesivir accumulation decreased in the presence of NBMPR by 30% in ENT1 cells (p = 0.0248) and 27% in ENT2 cells (p = 0.0054). EIDD-1931 accumulation decreased in the presence of NBMPR by 77% in ENT1 cells (p = 0.0463 ) and by 64% in ENT2 cells (p = 0.0132), supporting computational predictions that both are ENT substrates which may be important for efficacy against COVID-19. NBMPR failed to decrease molnupiravir uptake, suggesting that ENT interaction is likely inhibitory. Our combined computational and in vitro data can be used to identify additional ENT-drug interactions to improve our understanding of drugs that can circumvent the BTB. Significance Statement Significance statement: This study identified remdesivir and EIDD-1931 as substrates of equilibrative nucleoside transporters 1 and 2. This provides a potential mechanism for uptake of these drugs into cells and may be important for antiviral potential in the testes and other tissues expressing these transporters.

Miller Siennah R, McGrath Meghan E, Zorn Kimberley M, Ekins Sean, Wright Stephen H, Cherrington Nathan J

2021-Sep-09

Antiviral drugs, Transporter-mediated drug/metabolite disposition, Uptake transporters (OATP, OAT, OCT, PEPT, MCT, NTCP, ASBT, etc.), Uptake transporters (OATP, OAT, OCT, PEPT, MCT, NTCP, ASBT, etc.)s, antivirals, drug transport

General General

Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19.

In Current medical imaging

BACKGROUND : Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales.

CASE PRESENTATION : The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual's regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data.

CONCLUSION : Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.

Kumar Ajay, Kolnure Smita Nivrutti, Abhishek Kumar, Fadi-Al-Turjman Nerurkar, Pranav Ghalib, Muhammad Rukunuddin Shankar

2021-Sep-08

Big data analysis, COVID-19, Deep learning, Time series forecasting Infectious disease modeling

General General

Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic.

In International journal of environmental research and public health ; h5-index 73.0

Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.

Tang Zhongyun, Hu Haiyang, Xu Chonghuan, Zhao Kaidi

2021-Aug-27

COVID-19 pandemic, GRU-AE, biomedical signal monitoring framework, healthy monitoring, telemedicine

Pathology Pathology

Adoption of Digital Pathology in Developing Countries: From Benefits to Challenges.

In Journal of the College of Physicians and Surgeons--Pakistan : JCPSP

Digital pathology and the use of artificial intelligence constitute undisputedly the future of modern pathology. The outcomes and benefits of the whole slide imaging are beyond the scope of traditional microscopy, which the pathologists were using for decades. COVID-19 pandemic has further highlighted the importance of digital pathology as it offers the pathologists to work from their place of comfort and bridges the gap of physical barriers. In addition to the many advantages, there are certain limitations and challenges, which have to be overcomed particularly in the developing world. The major issue is the cost of scanners and technical support and training of staff. However, despite all these problems and challenges that exist, these can be resolved with the passage of time, where the role of world leader organisations will be of great importance in resolving these challenges. Key Words: Digital pathology, Artificial intelligence, Whole slide imaging.

Zehra Talat, Shabbir Asma

2021-Sep

General General

Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images.

In IEEE journal of biomedical and health informatics

Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using Coalition Game Theory, Information Theory, and Lambda fuzzy approximation. Three different sets of Fuzzy Measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures three Choquet Integrals are calculated and their decisions are finally combined.We have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. To the best of our knowledge, our experimental results outperform many recently proposed methods. Source code available at https://github.com/subhankar01/Covid-Chestxray-lambda-fuzzy.

Bhowal Pratik, Sen Subhankar, Yoon Jin Hee, Geem Zong Woo, Sarkar Ram

2021-Sep-09

General General

A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media.

In PeerJ. Computer science

Background : Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people's daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages.

Methods : This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods.

Results : Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model.

Lu Heng-Yang, Fan Chenyou, Song Xiaoning, Fang Wei

2021

COVID-19, Few-shot learning, Multi-modality, Rumor detection, Social media

General General

A Continuum Deformation Approach for Growth Analysis of COVID-19 in the United States.

In Scientific reports ; h5-index 158.0

The COVID-19 global pandemic has significantly impacted every aspect of life all over the world. The United States is reported to have suffered more than 20% of the global casualties from this pandemic. It is imperative to investigate the growth dynamics of the disease in the US based on varying geographical and governmental factors that best manifest itself in each State of the Country. This paper utilizes a hybrid machine learning and continuum deformation-based approach for analyzing the stability and growth rate of the pandemic. To this end, principal stress values of the pandemic continuum body are obtained using Mohr's Circle method and overlapping, moving windows of data are analysed successively. This helps in finding the correlations between the growth rate and Governments' action/Public's reaction. Government actions include "state of emergency", "shelter at place", and "phase declarations". We also consider the vaccination rate milestones, which shows us the coordinated Governments' action/Public's reaction. Finally, a number of recommendations are made to the Governments and people for better management of future pandemics.

Hemmati Sadra, Rastgoftar Hossein

2021-Sep-08

General General

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.

In Critical care (London, England)

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes.

METHODS : Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die.

RESULTS : SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors.

CONCLUSIONS : An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Banoei Mohammad M, Dinparastisaleh Roshan, Zadeh Ali Vaeli, Mirsaeidi Mehdi

2021-Sep-08

COVID-19, Machine learning, Mortality, Prediction model, SARS-CoV-2

Radiology Radiology

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).

METHODS : In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS : For the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model.

CONCLUSION : The machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

Chen Hui Juan, Mao Li, Chen Yang, Yuan Li, Wang Fei, Li Xiuli, Cai Qinlei, Qiu Jie, Chen Feng

2021-Sep-08

Coronavirus Disease 2019 (COVID-19), Machine learning, Non-COVID-19 pneumonia, Radiomics

General General

Screening Anti-inflammatory, Anticoagulant, and Respiratory Agents for SARS-CoV-2 3CLpro Inhibition from Chemical Fingerprints Through a Deep Learning Approach.

In Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion

BACKGROUND : Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 2019 (COVID-19), triggers a pathophysiological process linked not only to viral mechanisms of infectivity, but also to the pattern of host response. Drug repurposing is a promising strategy for rapid identification of treatments for SARS-CoV-2 infection, and several attractive molecular viral targets can be exploited. Among those, 3CL protease is a potential target of great interest.

OBJECTIVE : The objective of the study was to screen potential 3CLpro inhibitors compounds based on chemical fingerprints among anti-inflammatory, anticoagulant, and respiratory system agents.

METHODS : The screening was developed based on a drug property prediction framework, in which the evaluated property was the ability to inhibit the activity of the 3CLpro protein, and the predictions were performed using a dense neural network trained and validated on bioassay data.

RESULTS : On the validation and test set, the model obtained area under the curve values of 98.2 and 76.3, respectively, demonstrating high specificity for both sets (98.5% and 94.7%). Regarding the 1278 compounds screened, the model indicated four anti-inflammatory agents, two anticoagulants, and one respiratory agent as potential 3CLpro inhibitors.

CONCLUSIONS : Those findings point to a possible desirable synergistic effect in the management of patients with COVID-19 and provide potential directions for in vitro and in vivo research, which are indispensable for the validation of their results.

Caires Silveira Elena

2021-Sep-08

General General

Discovering New Trends & Connections: Current Applications of Biomedical Text Mining.

In Medical reference services quarterly

The explosive growth of digital information in recent years has amplified the information overload experienced by today's health-care professionals. In particular, the wide variety of unstructured text makes it difficult for researchers to find meaningful data without spending a considerable amount of time reading. Text mining can be used to facilitate better discoverability and analysis, and aid researchers in identifying critical trends and connections. This column will introduce key text-mining terms, recent use cases of biomedical text mining, and current applications for this technology in medical libraries.

Rahaman Tariq

COVID-19, data mining, emerging technology, information overload, machine learning

Public Health Public Health

The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic.

In Online social networks and media

During the coronavirus disease 2019 (COVID-19) pandemic, the video-sharing platform YouTube has been serving as an essential instrument to widely distribute news related to the global public health crisis and to allow users to discuss the news with each other in the comment sections. Along with these enhanced opportunities of technology-based communication, there is an overabundance of information and, in many cases, misinformation about current events. In times of a pandemic, the spread of misinformation can have direct detrimental effects, potentially influencing citizens' behavioral decisions (e.g., to not socially distance) and putting collective health at risk. Misinformation could be especially harmful if it is distributed in isolated news cocoons that homogeneously provide misinformation in the absence of corrections or mere accurate information. The present study analyzes data gathered at the beginning of the pandemic (January-March 2020) and focuses on the network structure of YouTube videos and their comments to understand the level of informational homogeneity associated with misinformation on COVID-19 and its evolution over time. This study combined machine learning and network analytic approaches. Results indicate that nodes (either individual users or channels) that spread misinformation were usually integrated in heterogeneous discussion networks, predominantly involving content other than misinformation. This pattern remained stable over time. Findings are discussed in light of the COVID-19 "infodemic" and the fragmentation of information networks.

Röchert Daniel, Shahi Gautam Kishore, Neubaum German, Ross Björn, Stieglitz Stefan

2021-Aug-30

COVID-19, Deep Learning, Social Media, Homogeneity, Infodemic, Misinformation, Network Analysis, YouTube

Radiology Radiology

AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.

In Medical image analysis

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

Soda Paolo, D’Amico Natascha Claudia, Tessadori Jacopo, Valbusa Giovanni, Guarrasi Valerio, Bortolotto Chandra, Akbar Muhammad Usman, Sicilia Rosa, Cordelli Ermanno, Fazzini Deborah, Cellina Michaela, Oliva Giancarlo, Callea Giovanni, Panella Silvia, Cariati Maurizio, Cozzi Diletta, Miele Vittorio, Stellato Elvira, Carrafiello Gianpaolo, Castorani Giulia, Simeone Annalisa, Preda Lorenzo, Iannello Giulio, Del Bue Alessio, Tedoldi Fabio, Alí Marco, Sona Diego, Papa Sergio

2021-Aug-28

Artificial intelligence, COVID-19, Deep learning, Prognosis

Public Health Public Health

Sociodemographic predictors of COVID-19 vaccine acceptance: a nationwide US-based survey study.

In Public health

OBJECTIVES : Acceptance of COVID-19 vaccination is attributable to sociodemographic factors and their complex interactions. Attitudes towards COVID-19 vaccines in the United States are changing frequently, especially since the launch of the vaccines and as the United States faces a third wave of the pandemic. Our primary objective was to determine the relative influence of sociodemographic predictors on COVID-19 vaccine acceptance. The secondary objectives were to understand the reasons behind vaccine refusal and compare COVID-19 vaccine acceptance with influenza vaccine uptake.

STUDY DESIGN : This was a nationwide US-based survey study.

METHODS : A REDCap survey link was distributed using various online platforms. The primary study outcome was COVID-19 vaccine acceptance (yes/no). Sociodemographic factors, such as age, ethnicity, gender, education, family income, healthcare worker profession, residence regions, local healthcare facility and 'vaccine launch' period (pre vs post), were included as potential predictors. The differences in vaccine acceptance rates among sociodemographic subgroups were estimated by Chi-squared tests, whereas logistic regression and neural networks computed the prediction models and determined the predictors of relative significance.

RESULTS : Among 2978 eligible respondents, 81.1% of participants were likely to receive the vaccine. All the predictors demonstrated significant associations with vaccine acceptance, except vaccine launch period. Regression analyses eliminated gender and vaccine launch period from the model, and the machine learning model reproduced the regression result. Both models precisely predicted individual vaccine acceptance and recognised education, ethnicity and age as the most important predictors. Fear of adverse effects and concern with efficacy were the principal reasons for vaccine refusal.

CONCLUSIONS : Sociodemographic predictors, such as education, ethnicity and age, significantly influenced COVID-19 vaccine acceptance, and concerns of side-effects and efficacy led to increased vaccine hesitancy.

Mondal P, Sinharoy A, Su L

2021-Jul-29

COVID-19, COVID-19 vaccine, Machine learning, Prediction model, Sociodemographic predictors, Vaccine hesitancy

Dermatology Dermatology

Perspectives on the Future Development of Mobile Applications for Dermatology Clinical Research.

In Dermatology and therapy

The COVID-19 pandemic significantly impacted clinical research in dermatology and practices around the country transitioned to teledermatology amid physical distancing requirements. Despite their growing use in teledermatology and clinical care, dermatology applications have not been studied extensively in the research space. The use of mobile applications has the potential to improve the experience of study subjects and physicians and increase the pool of individuals willing to participate in research beyond the pandemic. We discuss the various pros and cons of mobile apps, as well as the necessary components they require to successfully conduct research.

Hadeler Edward, Hong Julie, Mosca Megan, Hakimi Marwa, Brownstone Nicholas, Bhutani Tina, Liao Wilson

2021-Sep-07

AI, Applications, Apps, Artificial intelligence, Clinical research, Mobile, Phone, Remote research, Teledermatology, Telemedicine

Radiology Radiology

Cardiovascular CT and MRI in 2020: Review of Key Articles.

In Radiology ; h5-index 91.0

Despite the global coronavirus pandemic, cardiovascular imaging continued to evolve throughout 2020. It was an important year for cardiac CT and MRI, with increasing prominence in cardiovascular research, use in clinical decision making, and in guidelines. This review summarizes key publications in 2020 relevant to current and future clinical practice. In cardiac CT, these have again predominated in assessment of patients with chest pain and structural heart diseases, although more refined CT techniques, such as quantitative plaque analysis and CT perfusion, are also maturing. In cardiac MRI, the major developments have been in patients with cardiomyopathy and myocarditis, although coronary artery disease applications remain well represented. Deep learning applications in cardiovascular imaging have continued to advance in both CT and MRI, and these are now closer than ever to routine clinical adoption. Perhaps most important has been the rapid deployment of MRI in enhancing understanding of the impact of COVID-19 infection on the heart. Although this review focuses primarily on articles published in Radiology, attention is paid to other leading journals where published CT and MRI studies will have the most clinical and scientific value to the practicing cardiovascular imaging specialist.

Gulsin Gaurav S, McVeigh Niall, Leipsic Jonathon A, Dodd Jonathan D

2021-Sep-07

Radiology Radiology

Detection of COVID-19 Findings by the Local Interpretable Model-agnostic Explanations Method of Types-Based Activations Extracted from CNNs.

In Biomedical signal processing and control

Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.

Toğaçar Mesut, Muzoğlu Nedim, Ergen Burhan, Sıddık Binboğa Yarman Bekir, Mesrur Halefoğlu Ahmet

2021-Sep-02

COVID-19, Chest CT findings, Deep learning, Image processing, Medical decision support system

Public Health Public Health

Coevolution of COVID-19 research and China's policies.

In Health research policy and systems

BACKGROUND : In the era of evidence-based policy-making (EBPM), scientific outputs and public policy should engage with each other in a more interactive and coherent way. Notably, this is becoming increasingly critical in preparing for public health emergencies.

METHODS : To explore the coevolution dynamics between science and policy (SAP), this study explored the changes in, and development of, COVID-19 research in the early period of the COVID-19 outbreak in China, from 30 December 2019 to 26 June 2020. In this study, VOSviewer was adopted to calculate the link strength of items extracted from scientific publications, and machine learning clustering analysis of scientific publications was carried out to explore dynamic trends in scientific research. Trends in relevant policies that corresponded to changing trends in scientific research were then traced.

RESULTS : The study observes a salient change in research content as follows: an earlier focus on "children and pregnant patients", "common symptoms", "nucleic acid test", and "non-Chinese medicine" was gradually replaced with a focus on "aged patients", "pregnant patients", "severe symptoms and asymptomatic infection", "antibody assay", and "Chinese medicine". "Mental health" is persistent throughout China's COVID-19 research. Further, our research reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. The average issuance time of relevant COVID-19 policies in China is 8.36 days after the launching of related research.

CONCLUSIONS : In the early stage of the outbreak in China, the formulation of research-driven-COVID-19 policies and related scientific research followed a similar dynamic trend, which is clearly a manifestation of a coevolution model (CEM). The results of this study apply more broadly to the formulation of policies during public health emergencies, and provide the foundation for future EBPM research.

Cheng Xi, Tang Li, Zhou Maotian, Wang Guoyan

2021-Sep-06

COVID-19, China, Coevolution model, Science and policy

General General

A Comprehensive Survey of COVID-19 Detection Using Medical Images.

In SN computer science

The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.

Shah Faisal Muhammad, Joy Sajib Kumar Saha, Ahmed Farzad, Hossain Tonmoy, Humaira Mayeesha, Ami Amit Saha, Paul Shimul, Jim Md Abidur Rahman Khan, Ahmed Sifat

2021

AI, COVID-19, CT scan, Deep learning, Medical image, Survey, X-ray

General General

Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter.

In SN computer science

** : Disinformation (fake news) is a major problem that affects modern populations, especially in an era when information can be spread from one corner of the world to another in just one click. The diffusion of misinformation becomes more problematic when it addresses issues related to health, as it can affect people at both the individual and population levels. Through the ideas proposed by cultural evolution theory, in this study, we seek to understand the dynamics of disseminating messages (cultural traits) with untrue content (maladaptive traits). For our investigation, we used the scenario caused by the Coronavirus Disease 2019 (COVID-19) pandemic as a model. The instability caused by the pandemic provides a good model for the study of adapted and maladaptive traits, as the information can directly affect individual and population fitness. Through data collected on the Twitter platform (259,176 tweets) and using machine learning techniques and web scraping, we built a predictive model to analyze the following questions: (1) Is false information more shared? (2) Is false information more adopted? (3) Do people with social prestige influence the dissemination of maladaptive traits of COVID-19? We observed that fake news features contained in messages with false information were shared and adopted as unblemished messages. We also observed that social prestige was not a determining factor for the diffusion of maladaptive traits. Even with the ability to allow connections between individuals participating in social media, some factors such as attachment to cultural traits and the formation of social bubbles can favor isolation and decrease connectivity between individuals. Consequently, in the scenario of isolation between groups and low connectivity between individuals, there is a reduction in cultural exchange between people, which interferes with the dynamics of the selection of cultural traits. Thus, maladaptive (harmful) traits are favored and maintained in the cultural system. We also argue that the local Brazilian cultural context can be a determining factor for maintaining maladaptive traits. We conclude that in an unstable (pandemic) scenario, the information transmitted on Twitter is not reliable in relation to the increase in fitness, which may occur because of the low cultural exchange promoted by the personalization of the social network and cultural context of the population.

Supplementary Information : The online version contains supplementary material available at 10.1007/s42979-021-00836-w.

de Oliveira Danilo Vicente Batista, Albuquerque Ulysses Paulino

2021

Cultural selection bias, Machine learning, Maladaptive traits, Misinformation, Online information, Pandemic

General General

Data interpretation and visualization of COVID-19 cases using R programming.

In Informatics in medicine unlocked

Background : Data analysis and visualization are essential for exploring and communicating medical research findings, especially when working with COVID records.

Results : Data on COVID-19 diagnosed cases and deaths from December 2019 is collected automatically from www.statista.com, datahub.io, and the Multidisciplinary Digital Publishing Institute (MDPI). We have developed an application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using Statista, Data Hub, and MDPI data from densely populated countries like the United States, Japan, and India using R programming.

Conclusions : The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application will help with a better understanding of the SARS-CoV-2 epidemic worldwide.

Rimal Yagyanath, Gochhait Saikat, Bisht Aakriti

2021-Aug-30

Coronavirus, Covid-19, Data visualization, Machine learning, Open data map

Public Health Public Health

Containing the Transmission of COVID-19: A Modeling Study in 160 Countries.

In Frontiers in medicine

Background: It is much valuable to evaluate the comparative effectiveness of the coronavirus disease 2019 (COVID-19) prevention and control in the non-pharmacological intervention phase of the pandemic across countries and identify useful experiences that could be generalized worldwide. Methods: In this study, we developed a susceptible-exposure-infectious-asymptomatic-removed (SEIAR) model to fit the daily reported COVID-19 cases in 160 countries. The time-varying reproduction number (R t ) that was estimated through fitting the mathematical model was adopted to quantify the transmissibility. We defined a synthetic index (I AC ) based on the value of R t to reflect the national capability to control COVID-19. Results: The goodness-of-fit tests showed that the SEIAR model fitted the data of the 160 countries well. At the beginning of the epidemic, the values of R t of countries in the European region were generally higher than those in other regions. Among the 160 countries included in the study, all European countries had the ability to control the COVID-19 epidemic. The Western Pacific Region did best in continuous control of the epidemic, with a total of 73.76% of countries that can continuously control the COVID-19 epidemic, while only 43.63% of the countries in the European Region continuously controlled the epidemic, followed by the Region of Americas with 52.53% of countries, the Southeast Asian Region with 48% of countries, the African Region with 46.81% of countries, and the Eastern Mediterranean Region with 40.48% of countries. Conclusion: Large variations in controlling the COVID-19 epidemic existed across countries. The world could benefit from the experience of some countries that demonstrated the highest containment capabilities.

Niu Yan, Rui Jia, Wang Qiupeng, Zhang Wei, Chen Zhiwei, Xie Fang, Zhao Zeyu, Lin Shengnan, Zhu Yuanzhao, Wang Yao, Xu Jingwen, Liu Xingchun, Yang Meng, Zheng Wei, Chen Kaixin, Xia Yilan, Xu Lijuan, Zhang Shi, Ji Rongrong, Jin Taisong, Chen Yong, Zhao Benhua, Su Yanhua, Song Tie, Chen Tianmu, Hu Guoqing

2021

COVID-19, epidemic, mathematical model, the effective reproduction number, transmissibility

General General

Good Proctor or "Big Brother"? Ethics of Online Exam Supervision Technologies.

In Philosophy & technology

Online exam supervision technologies have recently generated significant controversy and concern. Their use is now booming due to growing demand for online courses and for off-campus assessment options amid COVID-19 lockdowns. Online proctoring technologies purport to effectively oversee students sitting online exams by using artificial intelligence (AI) systems supplemented by human invigilators. Such technologies have alarmed some students who see them as a "Big Brother-like" threat to liberty and privacy, and as potentially unfair and discriminatory. However, some universities and educators defend their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This essay provides one of the first sustained moral philosophical analyses of these technologies, focusing on ethical notions of academic integrity, fairness, non-maleficence, transparency, privacy, autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics, and all are relevant to education. The essay discusses these ethical issues. It also offers suggestions for educational institutions and educators interested in the technologies about the kinds of inquiries they need to make and the governance and review processes they might need to adopt to justify and remain accountable for using online proctoring technologies. The rapid and contentious rise of proctoring software provides a fruitful ethical case study of how AI is infiltrating all areas of life. The social impacts and moral consequences of this digital technology warrant ongoing scrutiny and study.

Coghlan Simon, Miller Tim, Paterson Jeannie

2021-Aug-31

Artificial intelligence, Ethics, Machine learning, Online assessment, Proctoring, Universities

Surgery Surgery

Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers.

In The EPMA journal

Background : The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records.

Objective : This study presents the application of machine learning techniques to these records to predict the blood culture's outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes.

Methods : Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture.

Results : The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93.

Conclusions : Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers' importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.

Garnica Oscar, Gómez Diego, Ramos Víctor, Hidalgo J Ignacio, Ruiz-Giardín José M

2021-Aug-31

Bacteraemia diagnosis, Bacteraemia prediction, Blood culture’s outcome prediction, COVID-19, Health policy, Healthcare economy, Individualised electronic patient record analysis, K-Nearest neighbours, Machine learning, Modelling, Personalised antibiotic treatment, Predictive, Preventive and personalised medicine (PPPM/3PM), Random forest, Support vector machine

General General

Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data.

In Journal of medical and biological engineering

Purpose : In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19.

Methods : In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT scans. The model proposed in this study uses a three-step architecture, consisting of a convolutional autoencoder based unsupervised feature extractor, a multi-objective genetic algorithm (MOGA) based feature selector, and a Bagging Ensemble of support vector machines based binary classifier. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model.

Results : The best performing classifier within 127 ms per image achieved an accuracy of 98.79%, the precision of 98.47%, area under curve of 0.998, and an F1 score of 98.85% on 497 test images. The proposed model outperforms the current state of the art COVID-19 diagnostic techniques in terms of speed and accuracy.

Conclusion : The experimental results prove the superiority of the proposed methodology in comparison to existing methods.The study also comprehensively compares various feature selection techniques and highlights the importance of feature selection in medical image data problems.

Bansal S, Singh M, Dubey R K, Panigrahi B K

2021-Sep-01

Convolutional autoencoder, Coronavirus (COVID-19), Feature subset selection, Multi-objective genetic algorithm

General General

ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images.

In Multimedia tools and applications

The COVID-19 virus has caused a worldwide pandemic, affecting numerous individuals and accounting for more than a million deaths. The countries of the world had to declare complete lockdown when the coronavirus led to community spread. Although the real-time Polymerase Chain Reaction (RT-PCR) test is the gold-standard test for COVID-19 screening, it is not satisfactorily accurate and sensitive. On the other hand, Computer Tomography (CT) scan images are much more sensitive and can be suitable for COVID-19 detection. To this end, in this paper, we develop a fully automated method for fast COVID-19 screening by using chest CT-scan images employing Deep Learning techniques. For this supervised image classification problem, a bootstrap aggregating or Bagging ensemble of three transfer learning models, namely, Inception v3, ResNet34 and DenseNet201, has been used to boost the performance of the individual models. The proposed framework, called ET-NET, has been evaluated on a publicly available dataset, achieving 97.81 ± 0.53 % accuracy, 97.77 ± 0.58 % precision, 97.81 ± 0.52 % sensitivity and 97.77 ± 0.57 % specificity on 5-fold cross-validation outperforming the state-of-the-art method on the same dataset by 1.56%. The relevant codes for the proposed approach are accessible in: https://github.com/Rohit-Kundu/ET-NET_Covid-Detection.

Kundu Rohit, Singh Pawan Kumar, Ferrara Massimiliano, Ahmadian Ali, Sarkar Ram

2021-Aug-31

Bagging ensemble classifier, COVID-19 screening, CT-scan image, Computer-aided detection, Coronavirus, Deep learning, Transfer learning

General General

Machine Learning Model Applied on Chest X-Ray Images Enables Automatic Detection of COVID-19 Cases with High Accuracy.

In International journal of general medicine

Purpose : This research was designed to investigate the application of artificial intelligence (AI) in the rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) using digital chest X-ray images, and to develop a robust computer-aided application for the automatic classification of COVID-19 pneumonia from other pneumonia and normal images.

Materials and Methods : A total of 1100 chest X-ray images were randomly selected from three different open sources, containing 300 X-ray images of confirmed COVID-19 patients, 400 images of other pneumonia patients, and 400 normal X-ray images. In this study, a classical machine learning approach was employed. The model was built using the support vector machine (SVM) classifier algorithm. The SVM was trained by 630 features obtained from the HOG descriptor, which was quantized into 30 orientation bins in the range between 0 and 360. The model was validated using a 10-fold cross-validation method. The performance of the model was evaluated using appropriate classification metrics, including sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, kappa, and accuracy.

Results : The multi-level classification model was able to distinguish COVID-19 patients with a sensitivity of 97.92% and specificity of 98.91%, for the internal testing or cross-validation. For the independent external testing, the model showed sensitivity of 95% and specificity of 98.13%, for distinguishing COVID-19 from other pneumonia and no-findings. The binary classification model was able to distinguish COVID-19 patients with a sensitivity of 99.58% and specificity of 99.69%, for the internal testing. For the independent external testing, the model showed a sensitivity of 98.33% and specificity of 100%, for distinguishing COVID-19 from normal images.

Conclusion : The model can achieve the rapid and accurate identification of COVID-19 patients from chest X-rays with more than 97% accuracy. This high accuracy and very rapid computer-aided diagnostic approach would be very helpful to control the pandemic.

Erdaw Yabsera, Tachbele Erdaw

2021

SARS-CoV-2, artificial intelligence, automatic classification, diagnosis, pneumonia

Radiology Radiology

The human-AI scoring system: A new method for CT-based assessment of COVID-19 severity.

In Technology and health care : official journal of the European Society for Engineering and Medicine

BACKGROUND : Chest computed tomography (CT) plays an important role in the diagnosis and assessment of coronavirus disease 2019 (COVID-19).

OBJECTIVE : To evaluate the value of an artificial intelligence (AI) scoring system for radiologically assessing the severity of COVID-19.

MATERIALS AND METHODS : Chest CT images of 81 patients (61 of normal type and 20 of severe type) with confirmed COVID-19 were used. The test data were anonymized. The scores achieved by four methods (junior radiologists; AI scoring system; human-AI segmentation system; human-AI scoring system) were compared with that by two experienced radiologists (reference score). The mean absolute errors (MAEs) between the four methods and experienced radiologists were calculated separately. The Wilcoxon test is used to predict the significance of the severity of COVID-19. Then use Spearman correlation analysis ROC analysis was used to evaluate the performance of different scores.

RESULTS : The AI score had a relatively low MAE (1.67-2.21). Score of human-AI scoring system had the lowest MAE (1.67), a diagnostic value almost equal to reference score (r= 0.97), and a strongest correlation with clinical severity (r= 0.59, p< 0.001). The AUCs of reference score, score of junior radiologists, AI score, score of human-AI segmentation system, and score of human-AI scoring system were 0.874, 0.841, 0.852, 0.857 and 0.865, respectively.

CONCLUSION : The human-AI scoring system can help radiologists to improve the accuracy of COVID-19 severity assessment.

Liu Mingzhu, Lv Weifu, Yin Baocai, Ge Yaqiong, Wei Wei

2021-Aug-27

COVID-19, artificial intelligence, chest CT, severity assessment

General General

Machine learning identifies molecular regulators and therapeutics for targeting SARS-CoV2-induced cytokine release.

In Molecular systems biology

Although 15-20% of COVID-19 patients experience hyper-inflammation induced by massive cytokine production, cellular triggers of this process and strategies to target them remain poorly understood. Here, we show that the N-terminal domain (NTD) of the SARS-CoV-2 spike protein substantially induces multiple inflammatory molecules in myeloid cells and human PBMCs. Using a combination of phenotypic screening with machine learning-based modeling, we identified and experimentally validated several protein kinases, including JAK1, EPHA7, IRAK1, MAPK12, and MAP3K8, as essential downstream mediators of NTD-induced cytokine production, implicating the role of multiple signaling pathways in cytokine release. Further, we found several FDA-approved drugs, including ponatinib, and cobimetinib as potent inhibitors of the NTD-mediated cytokine release. Treatment with ponatinib outperforms other drugs, including dexamethasone and baricitinib, inhibiting all cytokines in response to the NTD from SARS-CoV-2 and emerging variants. Finally, ponatinib treatment inhibits lipopolysaccharide-mediated cytokine release in myeloid cells in vitro and lung inflammation mouse model. Together, we propose that agents targeting multiple kinases required for SARS-CoV-2-mediated cytokine release, such as ponatinib, may represent an attractive therapeutic option for treating moderate to severe COVID-19.

Chan Marina, Vijay Siddharth, McNevin John, McElrath M Juliana, Holland Eric C, Gujral Taranjit S

2021-Sep

N-terminal domain, Ponatinib, SARS-CoV-2, kinases, machine learning

General General

ScanNet: An interpretable geometric deep learning model for structure-based protein binding site prediction

bioRxiv Preprint

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its function in vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/ .

Tubiana, J.; Schneidman-Duhovny, D.; Wolfson, H. J.

2021-09-06

General General

Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study.

In The Lancet. Infectious diseases

BACKGROUND : COVID-19 vaccines show excellent efficacy in clinical trials and effectiveness in real-world data, but some people still become infected with SARS-CoV-2 after vaccination. This study aimed to identify risk factors for post-vaccination SARS-CoV-2 infection and describe the characteristics of post-vaccination illness.

METHODS : This prospective, community-based, nested, case-control study used self-reported data (eg, on demographics, geographical location, health risk factors, and COVID-19 test results, symptoms, and vaccinations) from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile phone app. For the risk factor analysis, cases had received a first or second dose of a COVID-19 vaccine between Dec 8, 2020, and July 4, 2021; had either a positive COVID-19 test at least 14 days after their first vaccination (but before their second; cases 1) or a positive test at least 7 days after their second vaccination (cases 2); and had no positive test before vaccination. Two control groups were selected (who also had not tested positive for SARS-CoV-2 before vaccination): users reporting a negative test at least 14 days after their first vaccination but before their second (controls 1) and users reporting a negative test at least 7 days after their second vaccination (controls 2). Controls 1 and controls 2 were matched (1:1) with cases 1 and cases 2, respectively, by the date of the post-vaccination test, health-care worker status, and sex. In the disease profile analysis, we sub-selected participants from cases 1 and cases 2 who had used the app for at least 14 consecutive days after testing positive for SARS-CoV-2 (cases 3 and cases 4, respectively). Controls 3 and controls 4 were unvaccinated participants reporting a positive SARS-CoV-2 test who had used the app for at least 14 consecutive days after the test, and were matched (1:1) with cases 3 and 4, respectively, by the date of the positive test, health-care worker status, sex, body-mass index (BMI), and age. We used univariate logistic regression models (adjusted for age, BMI, and sex) to analyse the associations between risk factors and post-vaccination infection, and the associations of individual symptoms, overall disease duration, and disease severity with vaccination status.

FINDINGS : Between Dec 8, 2020, and July 4, 2021, 1 240 009 COVID Symptom Study app users reported a first vaccine dose, of whom 6030 (0·5%) subsequently tested positive for SARS-CoV-2 (cases 1), and 971 504 reported a second dose, of whom 2370 (0·2%) subsequently tested positive for SARS-CoV-2 (cases 2). In the risk factor analysis, frailty was associated with post-vaccination infection in older adults (≥60 years) after their first vaccine dose (odds ratio [OR] 1·93, 95% CI 1·50-2·48; p<0·0001), and individuals living in highly deprived areas had increased odds of post-vaccination infection following their first vaccine dose (OR 1·11, 95% CI 1·01-1·23; p=0·039). Individuals without obesity (BMI <30 kg/m2) had lower odds of infection following their first vaccine dose (OR 0·84, 95% CI 0·75-0·94; p=0·0030). For the disease profile analysis, 3825 users from cases 1 were included in cases 3 and 906 users from cases 2 were included in cases 4. Vaccination (compared with no vaccination) was associated with reduced odds of hospitalisation or having more than five symptoms in the first week of illness following the first or second dose, and long-duration (≥28 days) symptoms following the second dose. Almost all symptoms were reported less frequently in infected vaccinated individuals than in infected unvaccinated individuals, and vaccinated participants were more likely to be completely asymptomatic, especially if they were 60 years or older.

INTERPRETATION : To minimise SARS-CoV-2 infection, at-risk populations must be targeted in efforts to boost vaccine effectiveness and infection control measures. Our findings might support caution around relaxing physical distancing and other personal protective measures in the post-vaccination era, particularly around frail older adults and individuals living in more deprived areas, even if these individuals are vaccinated, and might have implications for strategies such as booster vaccinations.

FUNDING : ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, and the Alzheimer's Society.

Antonelli Michela, Penfold Rose S, Merino Jordi, Sudre Carole H, Molteni Erika, Berry Sarah, Canas Liane S, Graham Mark S, Klaser Kerstin, Modat Marc, Murray Benjamin, Kerfoot Eric, Chen Liyuan, Deng Jie, Österdahl Marc F, Cheetham Nathan J, Drew David A, Nguyen Long H, Pujol Joan Capdevila, Hu Christina, Selvachandran Somesh, Polidori Lorenzo, May Anna, Wolf Jonathan, Chan Andrew T, Hammers Alexander, Duncan Emma L, Spector Tim D, Ourselin Sebastien, Steves Claire J

2021-Sep-01

General General

Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care.

In Medicine, health care, and philosophy

Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert-client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client-expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence. This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics.

Parviainen Jaana, Rantala Juho

2021-Sep-04

COVID-19, Chatbot, Expertise, Health care, Professional ethics

General General

Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.

In Current diabetes reports ; h5-index 44.0

PURPOSE OF REVIEW : Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization.

RECENT FINDINGS : Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.

Rubin Daniel J, Shah Arnav A

2021-Sep-04

Diabetes, Predictive models, Readmission

General General

Key Contributions in Clinical Research Informatics.

In Yearbook of medical informatics ; h5-index 24.0

OBJECTIVES : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020.

METHOD : A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers.

RESULTS : Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records.

CONCLUSIONS : The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.

Daniel Christel, Bellamine Ali, Kalra Dipak

2021-Aug

General General

Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications.

In Yearbook of medical informatics ; h5-index 24.0

OBJECTIVES : Provide an overview of the emerging themes and notable papers which were published in 2020 in the field of Bioinformatics and Translational Informatics (BTI) for the International Medical Informatics Association Yearbook.

METHODS : A team of 16 individuals scanned the literature from the past year. Using a scoring rubric, papers were evaluated on their novelty, importance, and objective quality. 1,224 Medical Subject Headings (MeSH) terms extracted from these papers were used to identify themes and research focuses. The authors then used the scoring results to select notable papers and trends presented in this manuscript.

RESULTS : The search phase identified 263 potential papers and central themes of coronavirus disease 2019 (COVID-19), machine learning, and bioinformatics were examined in greater detail.

CONCLUSIONS : When addressing a once in a centruy pandemic, scientists worldwide answered the call, with informaticians playing a critical role. Productivity and innovations reached new heights in both TBI and science, but significant research gaps remain.

McGrath Scott P, Benton Mary Lauren, Tavakoli Maryam, Tatonetti Nicholas P

2021-Aug

Surgery Surgery

The Clinical Information Systems Response to the COVID-19 Pandemic.

In Yearbook of medical informatics ; h5-index 24.0

OBJECTIVE : The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.

METHODS : PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.

RESULTS : CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.

CONCLUSION : Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

Reeves J Jeffery, Pageler Natalie M, Wick Elizabeth C, Melton Genevieve B, Tan Yu-Heng Gamaliel, Clay Brian J, Longhurst Christopher A

2021-Aug

Public Health Public Health

Mapping the Role of Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the Literature.

In Yearbook of medical informatics ; h5-index 24.0

BACKGROUND : Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic.

METHODS : We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis.

RESULTS : A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers.

CONCLUSION : Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.

Tilahun Binyam, Gashu Kassahun Dessie, Mekonnen Zeleke Abebaw, Endehabtu Berhanu Fikadie, Angaw Dessie Abebaw

2021-Aug

Public Health Public Health

Health Data, Information, and Knowledge Sharing for Addressing the COVID-19.

In Yearbook of medical informatics ; h5-index 24.0

OBJECTIVES : To introduce the 2021 International Medical Informatics Association (IMIA) Yearbook by the editors.

METHODS : The editorial provides an introduction and overview to the 2021 IMIA Yearbook whose special topic is "Managing Pandemics with Health Informatics - Successes and Challenges". The Special Topic, the keynote paper, and survey papers are discussed. The IMIA President's statement and the IMIA dialogue with the World Health Organization are introduced. The sections' changes in the Yearbook Editorial Committee are also described.

RESULTS : Health informatics, in the context of a global pandemic, led to the development of ways to collect, standardize, disseminate and reuse data worldwide: public health data but also information from social networks and scientific literature. Fact checking methods were mostly based on artificial intelligence and natural language processing. The pandemic also introduced new challenges for telehealth support in times of critical response. Next generation sequencing in bioinformatics helped in decoding the sequence of the virus and the development of messenger ribonucleic acid (mRNA) vaccines.

CONCLUSIONS : The Corona Virus Disease 2019 (COVID-19) pandemic shows the need for timely, reliable, open, and globally available information to support decision making and efficiently control outbreaks. Applying Findable, Accessible, Interoperable, and Reusable (FAIR) requirements for data is a key success factor while challenging ethical issues have to be considered.

Soualmia Lina F, Hollis Kate Fultz, Mougin Fleur, Séroussi Brigitte

2021-Aug

Ophthalmology Ophthalmology

Applications of Haptic Technology, Virtual Reality, and Artificial Intelligence in Medical Training During the COVID-19 Pandemic.

In Frontiers in robotics and AI

This paper examines how haptic technology, virtual reality, and artificial intelligence help to reduce the physical contact in medical training during the COVID-19 Pandemic. Notably, any mistake made by the trainees during the education process might lead to undesired complications for the patient. Therefore, training of the medical skills to the trainees have always been a challenging issue for the expert surgeons, and this is even more challenging in pandemics. The current method of surgery training needs the novice surgeons to attend some courses, watch some procedure, and conduct their initial operations under the direct supervision of an expert surgeon. Owing to the requirement of physical contact in this method of medical training, the involved people including the novice and expert surgeons confront a potential risk of infection to the virus. This survey paper reviews recent technological breakthroughs along with new areas in which assistive technologies might provide a viable solution to reduce the physical contact in the medical institutes during the COVID-19 pandemic and similar crises.

Motaharifar Mohammad, Norouzzadeh Alireza, Abdi Parisa, Iranfar Arash, Lotfi Faraz, Moshiri Behzad, Lashay Alireza, Mohammadi Seyed Farzad, Taghirad Hamid D

2021

COVID-19 pandemic, artificial intelligence, haptic, medical training, virtual reality

General General

The Enigmatic COVID-19 Vulnerabilities and the Invaluable Artificial Intelligence (AI).

In Journal of multidisciplinary healthcare

The objective of the study is to conduct an exploratory review of the Covid-19 pandemic by focusing on the theme of Covid-19 pandemic morbidity and mortality, considering the dynamics of artificial intelligence and quality of life (QOL). The methods used in this research paper include a review of literature, anecdotal evidence, and reports on the morbidity of COVID-19, including the scope of its devastating effects in different countries such as the US, Africa, UK, China, and Brazil, among others. The findings of this study suggested that the devastating effects of the coronavirus are felt across different vulnerable populations. These include the elderly, front-line workers, marginalized communities, visible minorities, and more. The challenge in Africa is especially daunting because of inadequate infrastructure, and financial and human resources, among others. Besides, AI technology is being successfully used by scientists to enhance the development process of vaccines and drugs. However, its usage in other stages of the pandemic has not been adequately explored. Ultimately, it has been concluded that the effects of the Covid-19 are producing unprecedented and catastrophic outcomes in many countries. With a few exceptions, the common and current intervention approach is driven by many factors, including the compilation of relevant reliable and compelling data sets. On a positive note, the compelling trailblazing and catalytic contributions of AI towards the rapid discovery of COVID-19 vaccines are a good indication of future technological innovations and their effectiveness.

Lainjo Bongs

2021

Covid-19, artificial intelligence, lessons learned, quality of life, strategies

General General

Preparing for medical education after the COVID-19 pandemic: insightology in medicine.

In Korean journal of medical education

It is necessary to reflect on the question, "How to prepare for medical education after coronavirus disease 2019 (COVID-19)?" Although we are preparing for the era of Education 4.0 in line with the 4th industrial revolution of artificial intelligence and big data, most measures are focused on the methodologies of transferring knowledge; essential innovation is not being addressed. What is fundamentally needed in medicine is insightful intelligence that can see the invisible. We should not create doctors who only prescribe antispasmodics for abdominal pain, or antiemetic drugs for vomiting. Good clinical reasoning is not based on knowledge alone. Insightology in medicine is based on experience through Bayesian reasoning and imagination through the theory of mind. This refers to diagnosis of the whole, greater than the sum of its parts, by looking at the invisible using the Gestalt strategy. Identifying the missing process that links symptoms is essential. This missing process can be described in one word: context. An accurate diagnosis is possible only by understanding context, which can be done by standing in someone else's shoes. From the viewpoint of medicine, Education 4.0 is worrisome because people are still clinging to methodology. The subject we should focus on is "human", not "artificial" intelligence. We should first advance the "insightology in medicine" as a new paradigm, which is the "essence" that will never change even when rare "phenomena" such as the COVID-19 outbreak occur. For this reason, we should focus on teaching insightology in medicine, rather than teaching medical knowledge.

Choe Yon Ho

2021-Sep

COVID-19, Insight quotient, Insightology in medicine, Medical education

General General

Satellite data and machine learning reveal a significant correlation between NO2 and COVID-19 mortality.

In Environmental research ; h5-index 67.0

The Coronavirus disease 2019 (COVID-19) pandemic has officially spread all over the world since the beginning of 2020. Although huge efforts are addressed by scientists to shed light over the several questions raised by the novel SARS-CoV-2 virus, many aspects need to be clarified, yet. In particular, several studies have pointed out significant variations between countries in per-capita mortality. In this work, we investigated the association between COVID-19 mortality with climate variables and air pollution throughout European countries using the satellite remote sensing images provided by the Sentinel-5p mission. We analyzed data collected for two years of observations and extracted the concentrations of several pollutants; we used these measurements to feed a Random Forest regression. We performed a cross-validation analysis to assess the robustness of the model and compared several regression strategies. Our findings reveal a significant statistical association between air pollution (NO2) and COVID-19 mortality and a significant role played by the socio-demographic features, like the number of nurses or the hospital beds and the gross domestic product per capita.

Amoroso Nicola, Cilli Roberto, Maggipinto Tommaso, Monaco Alfonso, Tangaro Sabina, Bellotti Roberto

2021-Aug-30

Covid-19, Pollution, Remote sensing, Sentinel-5p

General General

COVID-19 Imaging-based AI Research - A Literature Review.

In Current medical imaging

BACKGROUND : The new coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Artificial intelligence (AI) assisted identification and detection of diseases is an ef-fective method of medical diagnosis.

OBJECTIVES : To present recent advances in AI-assisted diagnosis of COVID-19, we introduce major aspects of AI in the process of diagnosing COVID-19.

METHODS : In this paper, we firstly cover the latest collection and processing methods of da-tasets of COVID-19. The processing methods mainly include building public datasets, transfer learning, unsupervised learning and weakly supervised learning, semi-supervised learning methods and so on. Secondly, we introduce the algorithm application and evaluation metrics of AI in medical imaging segmentation and automatic screening. Then, we introduce the quantifi-cation and severity assessment of infection in COVID-19 patients based on image segmenta-tion and automatic screening. Finally, we analyze and point out the current AI-assisted diagno-sis of COVID-19 problems, which may provide useful clues for future work.

CONCLUSION : AI is critical for COVID-19 diagnosis. Combining chest imaging with AI can not only save time and effort, but also provide more accurate and efficient medical diagnosis results.

Ge Cheng, Zhang Lili, Xie Liangxu, Kong Ren, Zhang Hong, Chang Shan

2021-Sep-01

AI-assisted , Artificial intelligence, COVID-19, Medical diagnosis, Medical imaging, Segmentation

General General

COVID-19 detection method based on SVRNet and SVDNet in lung x-rays.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: To detect and diagnose coronavirus disease 2019 (COVID-19) better and faster, separable VGG-ResNet (SVRNet) and separable VGG-DenseNet (SVDNet) models are proposed, and a detection system is designed, based on lung x-rays to diagnose whether patients are infected with COVID-19. Approach: Combining deep learning and transfer learning, 1560 lung x-ray images in the COVID-19 x-ray image database (COVID-19 Radiography Database) were used as the experimental data set, and the most representative image classification models, VGG16, ResNet50, InceptionV3, and Xception, were fine-tuned and trained. Then, two new models for lung x-ray detection, SVRNet and SVDNet, were proposed on this basis. Finally, 312 test set images (including 44 COVID-19 and 268 normal images) were used as input to evaluate the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet models. Results: In the classification experiment of lung x-rays that tested positive and negative for COVID-19, the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet are 99.13%, 99.14%, 99.12% and 99.37%, 99.43%, 99.31%, respectively. Compared with the VGG16 network, SVRNet and SVDNet increased by 3.07%, 2.84%, 3.31% and 3.31%, 3.13%, 3.50%, respectively. On the other hand, the parameters of SVRNet and SVDNet are 5.65 × 10 6 and 6.57 × 10 6 , respectively. These are 61.56% and 55.31% less than VGG16, respectively. Conclusions: The SVRNet and SVDNet models proposed greatly reduce the number of parameters, while improving the accuracy and increasing the operating speed, and can accurately and quickly detect lung x-rays containing COVID-19.

Rao Kedong, Xie Kai, Hu Ziqi, Guo Xiaolong, Wen Chang, He Jianbiao

2021-Jan

COVID-19, deep learning, transfer learning, x-ray images

Radiology Radiology

Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19.

In Scientific reports ; h5-index 158.0

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For training and tuning the system, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. Lastly, to facilitate the continued development of AI models for CXR, we release our collected labels for the publicly available dataset.

Nabulsi Zaid, Sellergren Andrew, Jamshy Shahar, Lau Charles, Santos Edward, Kiraly Atilla P, Ye Wenxing, Yang Jie, Pilgrim Rory, Kazemzadeh Sahar, Yu Jin, Kalidindi Sreenivasa Raju, Etemadi Mozziyar, Garcia-Vicente Florencia, Melnick David, Corrado Greg S, Peng Lily, Eswaran Krish, Tse Daniel, Beladia Neeral, Liu Yun, Chen Po-Hsuan Cameron, Shetty Shravya

2021-Sep-01

General General

COVID-19 pandemic spread against countries' non-pharmaceutical interventions responses: a data-mining driven comparative study.

In BMC public health ; h5-index 82.0

BACKGROUND : The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020.

METHODS : Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans.

RESULTS : The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease.

CONCLUSIONS : The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.

Xylogiannopoulos Konstantinos F, Karampelas Panagiotis, Alhajj Reda

2021-Sep-01

COVID-19, Clustering, Data analysis, NPIs, Non-pharmaceutical interventions, Seasonal infections

General General

In-silico strategies to combat COVID-19: A comprehensive review.

In Biotechnology & genetic engineering reviews

The novel coronavirus SARS-CoV-2 since its emergence at Wuhan, China in December 2019 has been creating global health turmoil despite extensive containment measures and has resulted in the present pandemic COVID-19. Although the virus and its interaction with the host have been thoroughly characterized, effective treatment regimens beyond symptom-based care and repurposed therapeutics could not be identified. Various countries have successfully developed vaccines to curb the disease-transmission and prevent future outbreaks. Vaccination-drives are being conducted on a war-footing, but the process is time-consuming, especially in the densely populated regions of the world. Bioinformaticians and computational biologists have been playing an efficient role in this state of emergency to escalate clinical research and therapeutic development. However, there are not many reviews available in the literature concerning COVID-19 and its management. Hence, we have focused on designing a comprehensive review on in-silico approaches concerning COVID-19 to discuss the relevant bioinformatics and computational resources, tools, patterns of research, outcomes generated so far and their future implications to efficiently model data based on epidemiology; identify drug targets to design new drugs; predict epitopes for vaccine design and conceptualize diagnostic models. Artificial intelligence/machine learning can be employed to accelerate the research programs encompassing all the above urgent needs to counter COVID-19 and similar outbreaks.

Basu Soumya, Ramaiah Sudha, Anbarasu Anand

2021-Sep-01

Coronavirus disease (COVID)-19, Severe acute respiratory syndrome coronavirus (SARS-CoV)-2, bioinformatics, computational biology

General General

Auto informing COVID-19 detection result from x-ray/CT images based on deep learning.

In The Review of scientific instruments

It is no secret to all that the corona pandemic has caused a decline in all aspects of the world. Therefore, offering an accurate automatic diagnostic system is very important. This paper proposed an accurate COVID-19 system by testing various deep learning models for x-ray/computed tomography (CT) medical images. A deep preprocessing procedure was done with two filters and segmentation to increase classification results. According to the results obtained, 99.94% of accuracy, 98.70% of sensitivity, and 100% of specificity scores were obtained by the Xception model in the x-ray dataset and the InceptionV3 model for CT scan images. The compared results have demonstrated that the proposed model is proven to be more successful than the deep learning algorithms in previous studies. Moreover, it has the ability to automatically notify the examination results to the patients, the health authority, and the community after taking any x-ray or CT images.

Mahmood Ahlam Fadhil, Mahmood Saja Waleed

2021-Aug-01

General General

Correction: ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans.

In PloS one ; h5-index 176.0

[This corrects the article DOI: 10.1371/journal.pone.0250952.].

Yousefzadeh Mehdi, Esfahanian Parsa, Movahed Seyed Mohammad Sadegh, Gorgin Saeid, Rahmati Dara, Abedini Atefeh, Nadji Seyed Alireza, Haseli Sara, Karam Mehrdad Bakhshayesh, Kiani Arda, Hoseinyazdi Meisam, Roshandel Jafar, Lashgari Reza

2021

Public Health Public Health

Emotional Analysis of the COVID-19 First Flow in Greece Based on Twitter Posts.

In JMIR formative research

BACKGROUND : The effectiveness of public health measures depends upon a community's compliance, as well as on its positive or negative emotions.

OBJECTIVE : The purpose of this study was to perform an analysis of expressed emotions in Greek Twitter during the first flow of COVID-19.

METHODS : The study period was January 25th to June 30th, 2020. The data collection was performed via the Twitter filter streaming API using appropriate search keywords. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach (suggested by Colnerič and Demšar 2020) that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools like the six basic emotions (joy, sadness, disgust, fear, surprise, and anger) based on the Paul Eckman classification were adopted.

RESULTS : Surprise at the emerging contagion was the most frequent emotion detected, while the imposed isolation resulted mostly in anger (OR=2.108). Yet, Greeks felt rather safe during the first COVID-19 flow, while their positive and negative emotions reflected a masked "flight or fight" or fear vs. anger response to the epidemic contagion.

CONCLUSIONS : The emotional analysis emerges as a valid tool for epidemiology evaluations, design and public health strategy and surveillance.

CLINICALTRIAL : N/a.

Geronikolou Styliani, Drosatos George, Chrousos George

2021-Jul-06

Surgery Surgery

An early warning risk prediction tool for patients diagnosed with COVID-19: the statistical analysis plan for RECAP V1.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Since the start of the COVID-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient's clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient's risk of hospital admission, deterioration, and death.

OBJECTIVE : We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict risk of deterioration and hospitalisation.

METHODS : After the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We then will externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups, and applicability for different methods of data collection.

RESULTS : As of 10th of May 2021 we have recruited 3732 patients. A further 2088 patients have been recruited through NHS111 CCAS, and about 5000 through the DoctalyHealth platform.

CONCLUSIONS : The methodology for the development of the RECAP V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritise COVID-19 patients.

CLINICALTRIAL : Trial registration number: NCT04435041.

INTERNATIONAL REGISTERED REPORT : DERR1-10.2196/30083.

Fiorentino Francesca, Prociuk Denys, Espinosa Gonzalez Ana Belen, Neves Ana Luisa, Husain Laiba, Ramtale Sonny Christian, Mi Emma, Mi Ella, Macartney Jack, Anand Sneha N, Sherlock Julian, Saravanakumar Kavitha, Mayer Erik, de Lusignan Simon, Greenhalgh Trisha, Delaney Brendan

2021-Jul-05

General General

American Veterans in the Era of COVID-19: Reactions to the Pandemic, Posttraumatic Stress Disorder, and Substance Use Behaviors.

In International journal of mental health and addiction ; h5-index 27.0

The COVID-19 pandemic may have a compounding effect on the substance use of American veterans with posttraumatic stress disorder (PTSD). This study investigated the relationship between PTSD and current reactions to COVID-19 on alcohol and cannabis use among veterans who completed a survey 1 month prior to the pandemic in the USA and a 6-month follow-up survey. We hypothesized that veterans with PTSD would experience more negative reactions to COVID-19 and increased alcohol and cannabis use behaviors over those without PTSD. Veterans with PTSD prior to the pandemic, relative to those without, endorsed poorer reactions, greater frequency of alcohol use, and greater cannabis initiation and use during the pandemic. Veterans with PTSD may use substances to manage COVID-related stress. Clinicians may see an increase in substance use among this group during and after the pandemic and may need to implement specific behavioral interventions to mitigate the negative effects of COVID-19.

Pedersen Eric R, Davis Jordan P, Fitzke Reagan E, Lee Daniel S, Saba Shaddy

2021-Aug-26

Alcohol, COVID-19, Cannabis, Posttraumatic stress disorder, Veterans

General General

Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic.

In Scientific reports ; h5-index 158.0

The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF's computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.

Guo Qian, Li Mo, Wang Chunhui, Guo Jinyuan, Jiang Xiaoqing, Tan Jie, Wu Shufang, Wang Peihong, Xiao Tingting, Zhou Man, Fang Zhencheng, Xiao Yonghong, Zhu Huaiqiu

2021-Aug-31

Pathology Pathology

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity.

In Light, science & applications

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.

Goswami Neha, He Yuchen R, Deng Yu-Heng, Oh Chamteut, Sobh Nahil, Valera Enrique, Bashir Rashid, Ismail Nahed, Kong Hyunjoon, Nguyen Thanh H, Best-Popescu Catherine, Popescu Gabriel

2021-Sep-01

Public Health Public Health

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing.

In Annual review of biomedical data science

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)-the branch of artificial intelligence that interprets human language-can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.

Chen Qingyu, Leaman Robert, Allot Alexis, Luo Ling, Wei Chih-Hsuan, Yan Shankai, Lu Zhiyong

2021-Jul-20

COVID-19, artificial intelligence, natural language processing, pandemic control, text mining

Public Health Public Health

Covid-19 rapid test by combining a Random Forest-based web system and blood tests.

In Journal of biomolecular structure & dynamics

The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.

Barbosa Valter Augusto de Freitas, Gomes Juliana Carneiro, de Santana Maíra Araújo, de Lima Clarisse Lins, Calado Raquel Bezerra, Bertoldo Júnior Cláudio Roberto, Albuquerque Jeniffer Emidio de Almeida, de Souza Rodrigo Gomes, de Araújo Ricardo Juarez Escorel, Mattos Júnior Luiz Alberto Reis, de Souza Ricardo Emmanuel, Dos Santos Wellington Pinheiro

2021-Aug-31

Covid-19, Covid-19 rapid test, blood tests, computer-aided diagnosis, machine learning for diagnosis, software-based rapid test

Internal Medicine Internal Medicine

Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, "I Think I Have Influenza," Contributes to the Positivity Rate.

In Cureus

Introduction Rapid influenza diagnostic tests (RIDTs) are considered essential for determining when to start influenza treatment using anti-influenza drugs, but their accuracy is about 70%. Under the COVID-19 pandemic, we hope to refrain from performing unnecessary RIDTs considering droplet infection of COVID-19 and influenza. We re-examined the medical questionnaire's importance and its relationship to the positivity of RIDTs. Then we built a positivity prediction model for RIDTs using automated artificial intelligence (AI). Methods We retrospectively investigated 96 patients who underwent RIDTs at the outpatient department from December 2019 to March 2020. We used a questionnaire sheet with 24 items before conducting RIDTs. The factors associated with the positivity of RIDTs were statistically analyzed. We then used an automated AI framework to produce the positivity prediction model using the 24 items, sex, and age, with five-fold cross-validation. Results Of the 47 women and 49 men (median age was 39 years), 56 patients were RIDT positive with influenza A. The AI-based model using 26 variables had an area under the curve (AUC) of 0.980. The stronger variables are subjective pretest probability, which is a numerically described score ranging from 0% to 100% of "I think I have influenza," cough, past hours after the onset, muscle pain, and maximum body temperature in order. Conclusion We easily built the RIDT positivity prediction model using automated AI. Its AUC was satisfactory, and it suggested the importance of a detailed medical interview. Both the univariate analysis and AI-based model suggested that subjective pretest probability, "I think I have influenza," might be useful.

Katsuki Masahito, Matsuo Mitsuhiro

2021-Jul

automated artificial intelligence (autoai), deep learning, influenza, medical interview, prediction one, rapid influenza diagnosis test, subjective pretest probability

General General

GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays.

In Pattern recognition

Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing automatic techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily dependent on the availability of a large and representative labelled dataset. The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease. Semi-supervised learning has shown the ability to match the incredible performance of supervised models whilst requiring a small fraction of the labelled examples. This makes the semi supervised paradigm an attractive option for identifying COVID-19. In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect the diffusion prediction output as pseudo-labels that are used in an iterative scheme in a deep net. We demonstrate, through our experiments, that our model is able to outperform the current leading supervised model with a tiny fraction of the labelled examples. Finally, we provide attention maps to accommodate the radiologist's mental model, better fitting their perceptual and cognitive abilities. These visualisation aims to assist the radiologist in judging whether the diagnostic is correct or not, and in consequence to accelerate the decision.

Aviles-Rivero Angelica I, Sellars Philip, Schönlieb Carola-Bibiane, Papadakis Nicolas

2021-Aug-26

COVID-19, Chest X-ray, Deep Learning, Explainability, Semi-Supervised Learning

Surgery Surgery

Mapping single-cell data to reference atlases by transfer learning.

In Nature biotechnology ; h5-index 151.0

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

Lotfollahi Mohammad, Naghipourfar Mohsen, Luecken Malte D, Khajavi Matin, Büttner Maren, Wagenstetter Marco, Avsec Žiga, Gayoso Adam, Yosef Nir, Interlandi Marta, Rybakov Sergei, Misharin Alexander V, Theis Fabian J

2021-Aug-30

General General

(Machine) Learning the mutation signatures of SARS-CoV-2: a primer for predictive prognosis

bioRxiv Preprint

Motivation: Continuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data available for Covid19 have added to the challenges of traceability of mutations of concern. The latter however provides an opportunity to utilize SARS-CoV-2 genomes and the mutations therein as "big data records" to comprehensively classify the variants through the (machine) learning of mutation patterns. The unprecedented sequencing effort and tracing of dis-ease outcomes provide an excellent ground for identifying important mutations by developing ma-chine learnt models or severity classifiers using mutation profile of SARS-CoV-2. This is expected to provide a significant impetus to the efforts towards not only identifying the mutations of concern but also exploring the potential of mutation driven predictive prognosis of SARS-CoV-2. Results: We describe how a graduated approach of building various severity specific machine learning classifiers, using only the mutation corpus of SARS-CoV-2 genomes, can potentially lead to the identification of important mutations and guide potential prognosis of infection. We demonstrate the applicability of model derived important mutations and use of Shapley values in order to identify the significant mutations of concern as well as for developing sparse models of outcome classification. A total of 77,284 outcome traced SARS-CoV-2 genomes were employed in this study which represented a total corpus of 30346 unique nucleotide mutations and 18647 amino acid mutations. Machine learning models pertaining to graduated classifiers of target outcomes namely "Asymptomatic, Mild, Symptomatic/Moderate, Severe and Fatal" were built considering the TRIPOD guidelines for predictive prognosis. Shapley values for model linked important mutations were employed to select significant mutations leading to identification of less than 20 outcome driving mutations from each classifier. We additionally describe the significance of adopting a "temporal modeling approach" to benchmark the predictive prognosis linked with continuously evolving pathogens. A chronologically distinct sampling is important in evaluating the performance of models trained on "past data" in accurately classifying prognosis linked with genomes of future (observed with new mutations). We conclude that while machine learning approach can play a vital role in identifying relevant mutations, caution should be exercised in using the mutation signatures for predictive prognosis in cases where new mutations have accumulated along with the previously observed mutations of concern.

Nagpal, S.; Pinna, N. K.; Srivastava, D.; Singh, R.; Mande, S. S.

2021-08-31

General General

Unsupervised clustering analysis reveals global population structure of SARS-CoV-2

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. Since we have little prior information about the newly emerged coronavirus, we applied a state-of-the-art unsupervised deep learning clustering algorithm 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. The varied proportions of the six clusters within different continents revealed specific geographical distributions. Comprehensive analysis indicated that genetic factors and human migration played an important role in shaping the specific geographical distribution of population. 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.

2021-08-30

General General

ANFIS-Net for automatic detection of COVID-19.

In Scientific reports ; h5-index 158.0

Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.

Al-Ali Afnan, Elharrouss Omar, Qidwai Uvais, Al-Maaddeed Somaya

2021-Aug-27

General General

Janus Kinase Inhibitors and Coronavirus Disease (COVID)-19: Rationale, Clinical Evidence and Safety Issues.

In Pharmaceuticals (Basel, Switzerland)

We are witnessing a paradigm shift in drug development and clinical practice to fight the novel coronavirus disease (COVID-19), and a number of clinical trials have been or are being testing various pharmacological approaches to counteract viral load and its complications such as cytokine storm. However, data on the effectiveness of antiviral and immune therapies are still inconclusive and inconsistent. As compared to other candidate drugs to treat COVID-19, Janus Kinase (JAK) inhibitors, including baricitinib and ruxolitinib, possess key pharmacological features for a potentially successful repurposing: convenient oral administration, favorable pharmacokinetic profile, multifunctional pharmacodynamics by exerting dual anti-inflammatory and anti-viral effects. Baricitinib, originally approved for rheumatoid arthritis, received Emergency Use Authorization in November 2020 by the Food and Drug Administration in combination with remdesivir for the treatment of COVID-19 in hospitalized patients ≥ 2 years old who require supplemental oxygen, invasive mechanical ventilation, or extracorporeal membrane oxygenation. By July 2021, the European Medicines Agency is also expected to issue the opinion on whether or not to extend its use in hospitalised patients from 10 years of age who require supplemental oxygen. Ruxolitinib, approved for myelofibrosis, was prescribed in patients with COVID-19 within an open-label Emergency Expanded Access Plan. This review will address key milestones in the discovery and use of JAK inhibitors in COVID-19, from artificial intelligence to current clinical evidence, including real world experience, and critically appraise emerging safety issues, namely infections, thrombosis, and liver injury. An outlook to ongoing studies (clinicaltrials.gov) and unpublished pharmacovigilance data is also offered.

Gatti Milo, Turrini Eleonora, Raschi Emanuel, Sestili Piero, Fimognari Carmela

2021-Jul-28

adverse effects, baricitinib, coronavirus disease (COVID)-19, drug interaction, janus kinase (JAK) inhibitors, real word evidence, ruxolitinib

Pathology Pathology

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.

In Pathogens (Basel, Switzerland)

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Arora Gunjan, Joshi Jayadev, Mandal Rahul Shubhra, Shrivastava Nitisha, Virmani Richa, Sethi Tavpritesh

2021-Aug-18

COVID-19, SARS-CoV-2, artificial intelligence, diagnosis, drug discovery, machine learning, pandemic, prediction, surveillance, vaccine

Public Health Public Health

Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.

In Microscopy research and technique

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

Amin Javeria, Anjum Muhammad Almas, Sharif Muhammad, Rehman Amjad, Saba Tanzila, Zahra Rida

2021-Aug-26

Deeplabv3, ResNet-18, denoise convolutional neural network (DnCNN), healthcare, public health, stack sparse autoencoder deep learning model (SSAE)

Radiology Radiology

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset.

In PeerJ. Computer science

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models' predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.

Fayemiwo Michael Adebisi, Olowookere Toluwase Ayobami, Arekete Samson Afolabi, Ogunde Adewale Opeoluwa, Odim Mba Obasi, Oguntunde Bosede Oyenike, Olaniyan Oluwabunmi Omobolanle, Ojewumi Theresa Omolayo, Oyetade Idowu Sunday, Aremu Ademola Adegoke, Kayode Aderonke Anthonia

2021

COVID-19 test results, Convolutional neural networks, Coronavirus, Deep transfer learning, Machine learning, VGG-16, VGG-19

oncology Oncology

Detection of COVID-19 from chest x-ray images using transfer learning.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: The objective of this study is to develop and evaluate a fully automated, deep learning-based method for detection of COVID-19 infection from chest x-ray images. Approach: The proposed model was developed by replacing the final classifier layer in DenseNet201 with a new network consisting of global averaging layer, batch normalization layer, a dense layer with ReLU activation, and a final classification layer. Then, we performed an end-to-end training using the initial pretrained weights on all the layers. Our model was trained using a total of 8644 images with 4000 images each in normal and pneumonia cases and 644 in COVID-19 cases representing a large real dataset. The proposed method was evaluated based on accuracy, sensitivity, specificity, ROC curve, and F 1 -score using a test dataset comprising 1729 images (129 COVID-19, 800 normal, and 800 pneumonia). As a benchmark, we also compared the results of our method with those of seven state-of-the-art pretrained models and with a lightweight CNN architecture designed from scratch. Results: The proposed model based on DenseNet201 was able to achieve an accuracy of 94% in detecting COVID-19 and an overall accuracy of 92.19%. The model was able to achieve an AUC of 0.99 for COVID-19, 0.97 for normal, and 0.97 for pneumonia. The model was able to outperform alternative models in terms of overall accuracy, sensitivity, and specificity. Conclusions: Our proposed automated diagnostic model yielded an accuracy of 94% in the initial screening of COVID-19 patients and an overall accuracy of 92.19% using chest x-ray images.

Manokaran Jenita, Zabihollahy Fatemeh, Hamilton-Wright Andrew, Ukwatta Eranga

2021-Jan

COVID-19, chest x-ray, deep learning, pretrained models, transfer learning

General General

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.

In Emergency medicine journal : EMJ

OBJECTIVE : Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.

METHODS : Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).

RESULTS : There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.

CONCLUSIONS : Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Walker Katie, Jiarpakdee Jirayus, Loupis Anne, Tantithamthavorn Chakkrit, Joe Keith, Ben-Meir Michael, Akhlaghi Hamed, Hutton Jennie, Wang Wei, Stephenson Michael, Blecher Gabriel, Paul Buntine, Sweeny Amy, Turhan Burak

2021-Aug-25

efficiency, emergency care systems, emergency department management, emergency department operations, emergency department utilisation, emergency departments

General General

Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.

In Genome biology and evolution ; h5-index 52.0

BACKGROUND : Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events.

METHODS : We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to all high-quality genomes available from GISAID. STs were clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states.

RESULTS : GNUVID is a highly scalable tool for viral genotype classification (https://github.com/ahmedmagds/GNUVID) that can quickly classify hundreds of thousands of genomes in a way that is consistent with phylogeny. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states, an average of 20.6 putative introductions and 7.5 exportations for each state over the time period analyzed. We introduce the use of effective diversity metrics (Hill numbers) that can be used to estimate the impact of interventions (eg., travel-restrictions, vaccine uptake, mask mandates) on the variation in circulating viruses.

CONCLUSIONS : Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. GNUVID classification lends itself to measures of ecological diversity, and, with systematic genomic sampling, it could be used to track circulating viral diversity and identify emerging clones and hotspots.

Moustafa Ahmed M, Planet Paul J

2021-Aug-25

COVID-19, clonal complex, lineages, machine learning, nomenclature, wgMLST

General General

Telehealth and virtual health monitoring in cystic fibrosis.

In Current opinion in pulmonary medicine

PURPOSE OF REVIEW : At many institutions, the Covid-19 pandemic made it necessary to rapidly change the way services are provided to patients, including those with cystic fibrosis (CF). The purpose of this review is to explore the past, present and future of telehealth and virtual monitoring in CF and to highlight certain challenges/considerations in developing such services.

RECENT FINDINGS : The Covid-19 pandemic has proven that telehealth and virtual monitoring are a feasible means for safely providing services to CF patients when traditional care is not possible. However, both telehealth and virtual monitoring can also provide further support in the future in a postcovid era through a hybrid-model incorporating traditional care, remote data collection and sophisticated platforms to manage and share data with CF teams.

SUMMARY : We provide a detailed overview of telehealth and virtual monitoring including examples of how paediatric and adult CF services adapted to the need for rapid change. Such services have proven popular with people with CF meaning that co-design with stakeholders will likely improve systems further. In the future, telehealth and virtual monitoring will become more sophisticated by harnessing increasingly powerful technologies such as artificial intelligence, connected monitoring devices and wearables. In this review, we harmonise definitions and terminologies before highlighting considerations and limitations for the future of telehealth and virtual monitoring in CF.

Vagg Tamara, Shanthikumar Shivanthan, Morrissy David, Chapman Wendy W, Plant Barry J, Ranganathan Sarath

2021-Aug-25

General General

Did chatbots miss their "Apollo Moment"? Potential, gaps, and lessons from using collaboration assistants during COVID-19.

In Patterns (New York, N.Y.)

Artificial intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people. In this survey paper, I look at how collaboration assistants (chatbots for short), a type of AI that allows people to interact with them naturally (such as using speech, gesture, and text), have been used during a true global exigency-the COVID-19 pandemic. The key observation is that chatbots missed their "Apollo Moment" when at the time of need, they could have provided people with useful and life-saving contextual, personalized, and reliable decision support at a scale that the state-of-the-art makes possible. By "Apollo Moment", I refer to the opportunity for a technology to attain the pinnacle of its impact. I review the chatbot capabilities that are feasible with existing methods, identify the potential that chatbots could have met, and highlight the use-cases they were deployed on, the challenges they faced, and gaps that persisted. Finally, I draw lessons that, if implemented, would make them more relevant in future health emergencies.

Srivastava Biplav

2021-Aug-13

COVID-19, chatbots, collaborative assistants, conversational assistants, gaps, increasing impact, survey

General General

Neural connectome prospectively encodes the risk of post-traumatic stress disorder (PTSD) symptom during the COVID-19 pandemic.

In Neurobiology of stress

Background : The novel coronavirus (COVID-19) pandemic has affected humans worldwide and led to unprecedented stress and mortality. Detrimental effects of the pandemic on mental health, including risk of post-traumatic stress disorder (PTSD), have become an increasing concern. The identification of prospective neurobiological vulnerability markers for developing PTSD symptom during the pandemic is thus of high importance.

Methods : Before the COVID-19 outbreak (September 20, 2019-January 11, 2020), some healthy participants underwent resting-state functional connectivity MRI (rs-fcMRI) acquisition. We assessed the PTSD symptomology of these individuals during the peak of COVID-19 pandemic (February 21, 2020-February 28, 2020) in China. This pseudo-prospective cohort design allowed us to test whether the pre-pandemic neural connectome status could predict the risk of developing PTSD symptom during the pandemic.

Results : A total of 5.60% of participants (n = 42) were identified as being high-risk to develop PTSD symptom and 12.00% (n = 90) exhibited critical levels of PTSD symptoms during the COVID-19 pandemic. Pre-pandemic measures of functional connectivity (the neural connectome) prospectively classified those with heightened risk to develop PTSD symptom from matched controls (Accuracy = 76.19%, Sensitivity = 80.95%, Specificity = 71.43%). The trained classifier generalized to an independent sample. Continuous prediction models revealed that the same connectome could accurately predict the severity of PTSD symptoms within individuals (r 2 = 0.31p<.0).

Conclusions : This study confirms COVID-19 break as a crucial stressor to bring risks developing PTSD symptom and demonstrates that brain functional markers can prospectively identify individuals at risk to develop PTSD symptom.

Chen Zhiyi, Feng Pan, Becker Benjamin, Xu Ting, Nassar Matthew R, Sirois Fuschia, Hommel Bernhard, Zhang Chenyan, He Qinghua, Qiu Jiang, He Li, Lei Xu, Chen Hong, Feng Tingyong

2021-Nov

COVID-19, Deep learning, Post-traumatic stress disorder, Prospective diagnosis

Radiology Radiology

Biomarkers of Coagulation and Inflammation in COVID-19-Associated Ischemic Stroke.

In Stroke ; h5-index 83.0

BACKGROUND AND PURPOSE : We sought to determine if biomarkers of inflammation and coagulation can help define coronavirus disease 2019 (COVID-19)-associated ischemic stroke as a novel acute ischemic stroke (AIS) subtype.

METHODS : We performed a machine learning cluster analysis of common biomarkers in patients admitted with severe acute respiratory syndrome coronavirus 2 to determine if any were associated with AIS. Findings were validated using aggregate data from 3 large healthcare systems.

RESULTS : Clustering grouped 2908 unique patient encounters into 4 unique biomarker phenotypes based on levels of c-reactive protein, D-dimer, lactate dehydrogenase, white blood cell count, and partial thromboplastin time. The most severe cluster phenotype had the highest prevalence of AIS (3.6%, P<0.001), in-hospital AIS (53%, P<0.002), severe AIS (31%, P=0.004), and cryptogenic AIS (73%, P<0.001). D-dimer was the only biomarker independently associated with prevalent AIS with quartile 4 having an 8-fold higher risk of AIS compared to quartile 1 (P=0.005), a finding that was further corroborated in a separate cohort of 157 patients hospitalized with COVID-19 and AIS.

CONCLUSIONS : COVID-19-associated ischemic stroke may be related to COVID-19 illness severity and associated coagulopathy as defined by increasing D-dimer burden.

Esenwa Charles, Cheng Natalie T, Luna Jorge, Willey Joshua, Boehme Amelia K, Kirchoff-Torres Kathryn, Labovitz Daniel, Liberman Ava L, Mabie Peter, Moncrieffe Khadean, Soetanto Ainie, Lendaris Andrea, Seiden Johanna, Goldman Inessa, Altschul David, Holland Ryan, Benton Joshua, Dardick Joseph, Fernandez-Torres Jenelys, Flomenbaum David, Lu Jenny, Malaviya Avinash, Patel Nikunj, Toma Aureliana, Lord Aaron, Ishida Koto, Torres Jose, Snyder Thomas, Frontera Jennifer, Yaghi Shadi

2021-Aug-25

COVID-19, biomarker, inflammation, ischemic stroke, mortality

General General

Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning

bioRxiv Preprint

The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape or increased pathogenicity. Early prediction of emerging strains could be vital to pandemic preparedness but remains an open challenge. Here, we developed Strainflow, to learn the latent dimensions of 0.9 million high-quality SARS-CoV-2 genome sequences, and used machine learning algorithms to predict upcoming caseloads of SARS-CoV-2. In our Strainflow model, SARS-CoV-2 genome sequences were treated as documents, and codons as words to learn unsupervised codon embeddings (latent dimensions). We discovered that codon-level changes lead to a change in the entropy of the latent dimensions. We used a machine learning algorithm to find the most relevant latent dimensions called Dimensions of Concern (DoCs) of SARS-CoV-2 spike genes, and demonstrate their potential to provide a lead time for predicting new caseloads in several countries. The DoCs capture codons associated with global Variants of Concern (VOCs) and Variants of Interest (VOIs), and may be surveilled to predict country-specific emergence and spread of SARS-CoV-2 variants.

Nagpal, S.; Pal, R.; Ashima, ; Tyagi, A.; Tripathi, S.; Nagori, A.; Ahmad, S.; Mishra, H. P.; Kutum, R.; Sethi, T.

2021-08-26

Pathology Pathology

Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality.

In The Journal of clinical investigation ; h5-index 129.0

There is an urgent need to identify cellular/molecular mechanisms responsible for severe COVID-19 progressing to mortality. We initially performed untargeted/targeted lipidomics and focused biochemistry on 127 plasma samples and found elevated metabolites associated with secreted phospholipase A2 (sPLA2) activity and mitochondrial dysfunction in severe COVID-19 patients. Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA2 Group IIA (sPLA2-IIA), with a median value 9.6-fold higher than mild patients and 5.0-fold higher than severe COVID-19 survivors. Elevated sPLA2-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA2-IIA levels as a central node in stratifying patients that succumbed to COVID-19. Random forest analysis and LASSO-based regression analysis additionally identified sPLA2-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than either alone. An independent cohort (n=154) confirmed higher plasma sPLA2-IIA levels in deceased patients vs. severe or mild COVID-19, with the PLA-BUN index-based decision tree satisfactorily stratifying mild, severe, and deceased COVID-19 patients. With clinically tested inhibitors available, this study supports sPLA2-IIA as a therapeutic target to reduce COVID-19 mortality.

Snider Justin M, You Jeehyun Karen, Wang Xia, Snider Ashley J, Hallmark Brian, Zec Manja M, Seeds Michael C, Sergeant Susan, Johnstone Laurel, Wang Qiuming, Sprissler Ryan, Carr Tara F, Lutrick Karen, Parthasarathy Sairam, Bime Christian, Zhang Hao H, Luberto Chiara, Kew Richard R, Hannun Yusuf A, Guerra Stefano, McCall Charles E, Yao Guang, Del Poeta Maurizio, Chilton Floyd H

2021-Aug-24

COVID-19, Cellular immune response, Inflammation, Molecular pathology

Ophthalmology Ophthalmology

Effect of mutation and vaccination on spread, severity, and mortality of COVID-19 disease.

In Journal of medical virology

COVID-19 had different waves within thesame country. The spread rate and severity showed different properties within the COVID-19 different waves. The present work aims to compare the spread and the severity of the different waves using the available data of confirmed COVID-19 cases and death cases. Real-datasets collected from the Johns Hopkins University Center for Systems Science were used to perform a comparative study between COVID-19 different waves in 12 countries with the highest total performed tests for SARS-Cov-2 detection in the world (Italy, Brazil, Japan, Germany, Spain, India, USA, UAE, Poland, Colombia, Turkey, and Switzerland). The total number of confirmed cases and death cases in different waves of COVID-19 were compared to that of the previous one for equivalent periods. The total number of death cases in each wave was presented as a percentage of the total number of confirmed cases for the same periods. In all the selected 12 countries, wave 2 had a much higher number of confirmed cases than that in wave 1. However, the death cases increase was not comparable with that of the confirmed cases to the extent that some countries had lower death cases than in wave 1, UAE, and Spain. The death cases as a percentage of the total number of confirmed cases in wave 1 was much higher than that in wave 2. Some countries had waves 3 and 4. Waves 3 and 4 were had lower confirmed cases than wave 2, however, the death cases were variable in different countries. The death cases in waves 3 and 4 were similar to or higher than wave 2 in most countries. Wave 2 of COVID-19 had a much higher spread rate but much lower severity resulting in a lower death rate in wave 2 compared to that of the first wave. Waves 3 and 4 had lower confirmed cases than wave 2; that could be due to the presence of proper treatment and vaccination. However, that was not reflected in the death cases which were similar to or higher than wave 2 in most countries. Further studies are needed to explain these findings. This article is protected by copyright. All rights reserved.

Zawbaa Hossam M, Osama Hasnaa, El-Gendy Ahmed, Saeed Haitham, Harb Hadeer S, Madney Yasmin M, Abdelrahman Mona, Mohsen Marwa, Ali Ahmed Ma, Nicola Mina, Elgendy Marwa O, Ibrahim Ihab A, Abdelrahim Mohamed Ea

2021-Aug-24

COVID-19, confirmed case, death ca, death cases, h case, ionvaccinewave 1wave 2, smutat, vaccine

General General

Docking-Generated Multiple Ligand Poses for Bootstrapping Bioactivity Classifying Machine Learning: Repurposing Covalent Inhibitors for COVID-19-Related TMPRSS2 as Case Study.

In Computational and structural biotechnology journal

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Hatmal Ma’mon M, Abuyman Omar, Taha Mutasem

2021-Aug-19

Bootstrapping, Covalent docking, Docking, Ligand-Receptor Contact Fingerprints, Machine Learning, Scoring

Public Health Public Health

Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine.

In Computers in biology and medicine

Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.

Shi Beibei, Ye Hua, Zheng Long, Lyu Juncheng, Chen Cheng, Heidari Ali Asghar, Hu Zhongyi, Chen Huiling, Wu Peiliang

2021-Jul-30

Biochemical indexes, COVID-19, Colony predation algorithm, Coronavirus disease 2019, Kernel extreme learning machine, Warning system

General General

The Amplifying Effect of Conflicts on Case Fatality Rate of COVID-19: Evidence From 120 Countries.

In Frontiers in public health

Using the COVID-19 database of Johns Hopkins University, this study examines the determinants of the case fatality rate of COVID-19. We consider various potential determinants of the mortality risk of COVID-19 in 120 countries. The Ordinary Least Squares (OLS) and the Kernel-based Regularized Least Squares (KRLS) estimations show that internal and external conflicts are positively related to the case fatality rates. This evidence is robust to the exclusion of countries across different regions. Thus, the evidence indicates that conflict may explain significant differences in the case fatality rate of COVID-19 across countries.

Zhai Yonghui, Jiang Dayang, Gozgor Giray, Cho Eunho

2021

COVID-19 pandemic, armed conflicts, case fatality rate, machine learning estimator, mortality risk

General General

Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation.

In Sustainable cities and society

The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen.

Prakash N B, Murugappan M, Hemalakshmi G R, Jayalakshmi M, Mahmud Mufti

2021-Aug-16

COVID-19, Chest X-Ray, GMM, SqueezeNet, Super pixel

Ophthalmology Ophthalmology

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021.

In The EPMA journal

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.

Golubnitschaja Olga, Liskova Alena, Koklesova Lenka, Samec Marek, Biringer Kamil, Büsselberg Dietrich, Podbielska Halina, Kunin Anatolij A, Evsevyeva Maria E, Shapira Niva, Paul Friedemann, Erb Carl, Dietrich Detlef E, Felbel Dieter, Karabatsiakis Alexander, Bubnov Rostyslav, Polivka Jiri, Polivka Jiri, Birkenbihl Colin, Fröhlich Holger, Hofmann-Apitius Martin, Kubatka Peter

2021-Aug-17

Adults, Anorexia athletica, Anthropometrics, Artificial intelligence in medicine, BMI deviation, Big data management, Biomarker panel, Body fluids, Body weight, COVID-19, Cancers, Cardiovascular disease, Communicable, Deficits, Disease development, Elderly, Endothelin-1, Fat, Flammer syndrome, Health economy, Health policy, Healthcare, Hypoxic effects, Immune system, Individualised patient profile, Inflammation, Innovative population Screening Programme, Intentional, Manifestation, Medical imaging, Metabolic pathways, Microbiome, Modelling, Molecular patterns, Multi-level diagnostics, Multi-parametric analysis, Neurodegeneration, Neurology, Non-communicable disorders, Nutrition, Overweight, Pathology, Population health, Predictive preventive personalised medicine (3PM/PPPM), Pregnancy, Progression, ROS, Reproductive dysfunction, Sports medicine, Stroke, Systemic ischemia, Underweight, Unintentional, Vasoconstriction, Weight loss, Well-being, Wound healing, Youth

Public Health Public Health

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks.

In Cognitive computation

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.

Grissette Hanane, Nfaoui El Habib

2021-Aug-18

Affective computing, Biomedical sentiment analysis, Distributed biomedical vocabularies, Pandemic COVID-19, Sentic computing, Social networks

General General

Computer-aided detection of COVID-19 from CT scans using an ensemble of CNNs and KSVM classifier.

In Signal, image and video processing

Corona Virus Disease-2019 (COVID-19) is a global pandemic which is spreading briskly across the globe. The gold standard for the diagnosis of COVID-19 is viral nucleic acid detection with real-time polymerase chain reaction (RT-PCR). However, the sensitivity of RT-PCR in the diagnosis of early-stage COVID-19 is less. Recent research works have shown that computed tomography (CT) scans of the chest are effective for the early diagnosis of COVID-19. Convolutional neural networks (CNNs) are proven successful for diagnosing various lung diseases from CT scans. CNNs are composed of multiple layers which represent a hierarchy of features at each level. CNNs require a big number of labeled instances for training from scratch. In medical imaging tasks like the detection of COVID-19 where there is a difficulty in acquiring a large number of labeled CT scans, pre-trained CNNs trained on a huge number of natural images can be employed for extracting features. Feature representation of each CNN varies and an ensemble of features generated from various pre-trained CNNs can increase the diagnosis capability significantly. In this paper, features extracted from an ensemble of 5 different CNNs (MobilenetV2, Shufflenet, Xception, Darknet53 and EfficientnetB0) in combination with kernel support vector machine is used for the diagnosis of COVID-19 from CT scans. The method was tested using a public dataset and it attained an area under the receiver operating characteristic curve of 0.963, accuracy of 0.916, kappa score of 0.8305, F-score of 0.91, sensitivity of 0.917 and positive predictive value of 0.904 in the prediction of COVID-19.

Abraham Bejoy, Nair Madhu S

2021-Aug-16

CNN, COVID-19, Computed tomography, Computer-aided diagnosis, KSVM

General General

Predictive Mathematical Models of the Short-Term and Long-Term Growth of the COVID-19 Pandemic.

In Computational and mathematical methods in medicine

The prediction of the dynamics of the COVID-19 outbreak and the corresponding needs of the health care system (COVID-19 patients' admissions, the number of critically ill patients, need for intensive care units, etc.) is based on the combination of a limited growth model (Verhulst model) and a short-term predictive model that allows predictions to be made for the following day. In both cases, the uncertainty analysis of the prediction is performed, i.e., the set of equivalent models that adjust the historical data with the same accuracy. This set of models provides the posterior distribution of the parameters of the predictive model that adjusts the historical series. It can be extrapolated to the same analyzed time series (e.g., the number of infected individuals per day) or to another time series of interest to which it is correlated and used, e.g., to predict the number of patients admitted to urgent care units, the number of critically ill patients, or the total number of admissions, which are directly related to health needs. These models can be regionalized, that is, the predictions can be made at the local level if data are disaggregated. We show that the Verhulst and the Gompertz models provide similar results and can be also used to monitor and predict new outbreaks. However, the Verhulst model seems to be easier to interpret and to use.

Fernández-Martínez Juan Luis, Fernández-Muñiz Zulima, Cernea Ana, Kloczkowski Andrzej

2021

General General

Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic.

In Soft computing

The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

Basha Sameh H, Anter Ahmed M, Hassanien Aboul Ella, Abdalla Areeg

2021-Aug-18

Automated Neurotrophic rule-based, COVID-19, Chest X-Rays images, Neurotrophic classification system, Reduction rule-based

General General

Leveraging Weather Data for Forecasting Cases-to-Mortality Rates Due to COVID-19.

In Chaos, solitons, and fractals

There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor - temperature - was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries. © 2017 Elsevier Inc. All rights reserved.

Iloanusi Ogechukwu N, Ross Arun

2021-Aug-18

COVID-19, COVID-19 cases-to-mortality ratios, deep learning, forecasting, rainfall, random forest, regression analysis, relative humidity, solar irradiation, temperature, weather conditions

Public Health Public Health

An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection.

In Cell & bioscience

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines.

Safarchi Azadeh, Fatima Shadma, Ayati Zahra, Vafaee Fatemeh

2021-Aug-22

COVID-19, Diagnostics, SARS-COV-2, Treatment, Vaccines

General General

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.

In Clinical imaging

INTRODUCTION : The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of chest X-rays (CXRs) as COVID-19 positive or negative.

METHODS : The primary dataset consisted of 294 COVID-19 positive and 294 COVID-19 negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images. We used