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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

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 six common convolutional neural network architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile and InceptionV3. We studied six models (one for each architecture) which were pre-trained on a vast repository of generic (non-CXR) images, as well as a seventh DenseNet121 model, which was pre-trained on a repository of CXR images. For each model, we replaced the output layers with custom fully connected layers for the task of binary classification of images as COVID-19 positive or negative. Performance metrics were calculated on a hold-out test set with CXRs from patients who were not included in the training/validation set.

RESULTS : When pre-trained on generic images, the VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile, and InceptionV3 architectures respectively produced hold-out test set areas under the receiver operating characteristic (AUROCs) of 0.98, 0.95, 0.97, 0.95, 0.99, and 0.96 for the COVID-19 classification of CXRs. The X-ray pre-trained DenseNet121 model, in comparison, had a test set AUROC of 0.87.

DISCUSSION : Common convolutional neural network architectures with parameters pre-trained on generic images yield high-performance and well-calibrated COVID-19 CXR classification.

Maharjan Jenish, Calvert Jacob, Pellegrini Emily, Green-Saxena Abigail, Hoffman Jana, McCoy Andrea, Mao Qingqing, Das Ritankar

2021-Jul-24

COVID-19, Classification, Deep learning, Diagnosis, Neural networks, X-rays

Radiology Radiology

COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework.

In Medical image analysis

With the global outbreak of COVID-19 in early 2020, rapid diagnosis of COVID-19 has become the urgent need to control the spread of the epidemic. In clinical settings, lung infection segmentation from computed tomography (CT) images can provide vital information for the quantification and diagnosis of COVID-19. However, accurate infection segmentation is a challenging task due to (i) the low boundary contrast between infections and the surroundings, (ii) large variations of infection regions, and, most importantly, (iii) the shortage of large-scale annotated data. To address these issues, we propose a novel two-stage cross-domain transfer learning framework for the accurate segmentation of COVID-19 lung infections from CT images. Our framework consists of two major technical innovations, including an effective infection segmentation deep learning model, called nCoVSegNet, and a novel two-stage transfer learning strategy. Specifically, our nCoVSegNet conducts effective infection segmentation by taking advantage of attention-aware feature fusion and large receptive fields, aiming to resolve the issues related to low boundary contrast and large infection variations. To alleviate the shortage of the data, the nCoVSegNet is pre-trained using a two-stage cross-domain transfer learning strategy, which makes full use of the knowledge from natural images (i.e., ImageNet) and medical images (i.e., LIDC-IDRI) to boost the final training on CT images with COVID-19 infections. Extensive experiments demonstrate that our framework achieves superior segmentation accuracy and outperforms the cutting-edge models, both quantitatively and qualitatively.

Liu Jiannan, Dong Bo, Wang Shuai, Cui Hui, Fan Deng-Ping, Ma Jiquan, Chen Geng

2021-Aug-06

COVID-19, Computed tomography, Lung infection segmentation, Transfer learning

General General

Recent omics-based computational methods for COVID-19 drug discovery and repurposing.

In Briefings in bioinformatics

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.

Tayara Hilal, Abdelbaky Ibrahim, To Chong Kil

2021-Aug-21

COVID-19, SARS-CoV-2, deep learning, drug discovery, omics data analysis

Surgery Surgery

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

In JAMIA open

Objective : With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.

Materials and Methods : Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.

Results : This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.

Discussion : Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.

Conclusion : This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.

Sahoo Himanshu S, Silverman Greg M, Ingraham Nicholas E, Lupei Monica I, Puskarich Michael A, Finzel Raymond L, Sartori John, Zhang Rui, Knoll Benjamin C, Liu Sijia, Liu Hongfang, Melton Genevieve B, Tignanelli Christopher J, Pakhomov Serguei V S

2021-Jul

and symptoms, artificial intelligence, clinical decision support systems, follow-up studies, information extraction, natural language processing, signs

Surgery Surgery

Influenza and Anosmia: important prediction factors for severity and death of COVID-19.

In The Journal of infection

OBJECTIVES : To investigate the factors related to the severity and mo rtality of COVID-19 using big data-machine learning techniques.

METHODS : This study included 8070 patients in South Korea diagnosed with COVID-19 between January and July 2020, and whose data were available from the National-Health-Insurance-Service.

RESULTS : Machine-learning algorithms were performed to evaluate the effects of comorbidities on severity and mortality of COVID-19. The most common comorbidities of COVID-19 were pulmonary inflammation followed by hypertension. The model that best predicted severity was a neural network (AUC: 85.06%). The most important variable for predicting severity in the neural network model was a history of influenza (relative importance: 0.083). The model that best predicted mortality was the logistic regression elastic net (EN) model (AUC: 93.86%). The most important variables for mortality in the EN model were age (coefficient: 2.136) and anosmia (coefficient: -1.438).

CONCLUSIONS : In COVID-19 patients, influenza was found to be a major adverse factor in addition to old age and male. In addition, anosmia was found to be a major factor associated with lower severity and mortality. Therefore, in the current situation where there is no adequate COVID-19 treatment at present, examining the patient's history of influenza vaccination and anosmia in addition to age and sex will be an important indicator for predicting the severity and mortality of COVID-19 patients.

You Yeon Seok, Kim Jong Seung

2021-Aug-18

COVID 19, Comorbidities, Machine Learning, Modeling, Mortality, SARS-CoV-2, Severity

General General

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

In Briefings in bioinformatics

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Xu Zhaochun, Luo Meng, Lin Weizhong, Xue Guangfu, Wang Pingping, Jin Xiyun, Xu Chang, Zhou Wenyang, Cai Yideng, Yang Wenyi, Nie Huan, Jiang Qinghua

2021-Aug-20

T cell receptor, ensemble deep learning, peptide, peptide-TCR interaction

General General

Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia.

In Sustainable cities and society

To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale, which is followed by NSGA-II to find a range of Pareto optimal solutions for ensuring the minimization of the infection rate and mortality at the same time. Experimental results demonstrate that the predictive model can alert the local government in advance, allowing the accused time to put forward relevant measures. The temperature in the category of environment and the stringency index belonging to the social factor are identified as the top 2 important features to exert a greater impact on the virus transmission. Moreover, optimal solutions provide references to design the best control strategies towards pandemic containment and prevention that can accommodate the country-specific circumstance, which are possible to decrease the two objectives by more than 95%. In particular, appropriate adjustment of social-related features needs to take priority over others, since it can bring about at least 1.47% average improvement of two objectives compared to environmental factors.

Pan Yue, Zhang Limao, Yan Zhenzhen, Lwin May O, Skibniewski Miroslaw J

2021-Aug-13

COVID-19, multi-objective optimization, random forest regression feature important analysis, sustainability

Public Health Public Health

Emergency care and the patient experience: Using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic.

In Health and technology

The COVID-19 pandemic has presented many unique challenges to patient care especially in emergency medicine. These challenges result in an altered patient experience. Patient experience refers to the cumulative impression made on patients during their medical visit and is measured by a standardized survey tool. Patient experience is considered a key measure of quality of care. The volume of survey data received makes it difficult to spot trends and concerns in patient comments. Topic modeling and sentiment analysis are well documented analytic techniques that can be used to gain insight into patient experience and make sense of vast quantities of data. This study examined three periods of time, pre, during and post-COVID-19 first wave in order to identify key trends in sentiment and topics related to patient experience. Previously collected, anonymized Press Ganey (PG) survey data was used from three northeastern emergency department that make up an academic emergency department. Data was collected for three contiguous time periods: Pre-COVID-19 (12/10/2019- 3/10/2020), During COVID-19: (3/11/2020-6/10/2020), and Post-first wave COVID-19 (6/11/2020- 9/10/2020). Preprocessing of the data was carried out then a sentiment label (i.e., positive, negative, neutral, mixed) was assigned by the tool. These labels were used to assess the validity of Press Ganey labels. Next, a topic modeling approach from machine learning was used to analyze the contents of the patient comments and uncover concerns and perceptions of patient experiences. Themes that emerged from the analysis of patient comments included concerns over personal safety and exposure to the virus, exclusion of family from decision making and care and high levels of scrutiny over systems issues, care, and treatment protocols. Topic modeling showed shifting priorities and concerns throughout the three periods examined. Prior to the pandemic, patient comments were largely positive and focused on technical expertise and perceptions of competence. New topics and concerns that patients reported relevant to the pandemic were identified during-COVID-19. Comments on systems issues regarding processes to limit viral spread and concerns over family/visitor restrictions were dominant. Although there was evidence of praise and appreciation of the efforts of staff there was also a high level of scrutiny of the processes encountered during the emergency visit. Sentiment analysis and topic modeling offer a unique method for organizing and analyzing the shifting concerns of patients and families. Suggestions of interventions are made to address these evolving concerns. The automation of analysis using artificial intelligence would allow for rapid and accurate analysis of patient feedback.

Chekijian Sharon, Li Huan, Fodeh Samah

2021-Aug-13

COVID-19, Emergency medicine, Machine learning, Patient experience, Sentiment analysis, Thematic analysis

General General

Predicting special care during the COVID-19 pandemic: a machine learning approach.

In Health information science and systems

More than ever, COVID-19 is putting pressure on health systems worldwide, especially in Brazil. In this study, we propose a method based on statistics and machine learning that uses blood lab exam data from patients to predict whether patients will require special care (hospitalization in regular or special-care units). We also predict the number of days the patients will stay under such care. The two-step procedure developed uses Bayesian Optimisation to select the best model among several candidates. This leads us to final models that achieve 0.94 area under ROC curve performance for the first target and 1.87 root mean squared error for the second target (which is a 77% improvement over the mean baseline)-making our model ready to be deployed as a decision system that could be available for everyone interested. The analytical approach can be used in other diseases and can help to plan hospital resources in other contexts.

Bezzan Vitor P, Rocco Cleber D

2021-Dec

Applied AI, Bayesian Optimisation, Blood exam, COVID-19, Hospital management, Machine learning

General General

Fine-grained data reveal segregated mobility networks and opportunities for local containment of COVID-19.

In Scientific reports ; h5-index 158.0

Deriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence of the extent to which the reductions in mobility alter the way people from different regions of cities are connected, and what containment policies could complement mobility reductions to conquer the pandemic. Here, we examined individual movements in 21 of the most affected counties in the United States, showing that mobility reduction leads to a segregated place network and alters its relationship with pandemic spread. Our findings suggest localized area-specific policies, such as geo-fencing, as viable alternatives to city-wide lockdown for conquering the pandemic after mobility was reduced.

Fan Chao, Lee Ronald, Yang Yang, Mostafavi Ali

2021-Aug-19

Public Health Public Health

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study.

In Scientific reports ; h5-index 158.0

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

Dabbah Mohammad A, Reed Angus B, Booth Adam T C, Yassaee Arrash, Despotovic Aleksa, Klasmer Benjamin, Binning Emily, Aral Mert, Plans David, Morelli Davide, Labrique Alain B, Mohan Diwakar

2021-Aug-19

General General

Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks.

In ISA transactions

Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the SpInItIbD-N (suspicious Sp, infected In, intensive care It, intubated Ib, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear SpInItIbD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear SpInItIbD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.

Tutsoy Onder, Polat Adem

2021-Aug-09

COVID-19, Casualties, Linear dynamics, Model, Non-linear dynamics, Pandemic, Prediction

General General

An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans.

In Artificial intelligence in medicine ; h5-index 34.0

COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.

Pennisi Matteo, Kavasidis Isaak, Spampinato Concetto, Schinina Vincenzo, Palazzo Simone, Salanitri Federica Proietto, Bellitto Giovanni, Rundo Francesco, Aldinucci Marco, Cristofaro Massimo, Campioni Paolo, Pianura Elisa, Di Stefano Federica, Petrone Ada, Albarello Fabrizio, Ippolito Giuseppe, Cuzzocrea Salvatore, Conoci Sabrina

2021-Aug

COVID-19 detection, Deep learning, Lung segmentation

Radiology Radiology

Quantitative CT for detecting COVID‑19 pneumonia in suspected cases.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Corona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia.

METHODS : A total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia.

RESULTS : The volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of - 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of - 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at - 400 HU, - 350 HU, and - 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of - 300 HU.

CONCLUSIONS : Quantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases.

Lu Weiping, Wei Jianguo, Xu Tingting, Ding Miao, Li Xiaoyan, He Mengxue, Chen Kai, Yang Xiaodan, She Huiyuan, Huang Bingcang

2021-Aug-19

Artificial intelligence, COVID-19, Quantitative CT, Suspected cases, Threshold segmentation

General General

External Validation and Recalibration of the CURB-65 and PSI for Predicting 30-Day Mortality and Critical Care Intervention in Multiethnic Patients with COVID-19.

In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

OBJECTIVES : To validate and recalibrate the CURB-65 and pneumonia severity index (PSI) in predicting 30-day mortality and critical care intervention (CCI) in a multiethnic population with COVID-19, along with evaluating both models in predicting CCI.

METHODS : Retrospective data was collected for 1181 patients admitted to the largest hospital in Qatar with COVID-19 pneumonia. The area under the curve (AUC), calibration curves, and other metrics were bootstrapped to examine the performance of the models. Variables constituting the CURB-65 and PSI scores underwent further analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) along with logistic regression to develop a model predicting CCI. Complex machine learning models were built for comparative analysis.

RESULTS : The PSI performed better than CURB-65 in predicting 30-day mortality (AUC 0.83, 0.78 respectively), while CURB-65 outperformed PSI in predicting CCI (AUC 0.78, 0.70 respectively). The modified PSI/CURB-65 model (respiratory rate, oxygen saturation, hematocrit, age, sodium, and glucose) predicting CCI had excellent accuracy (AUC 0.823) and good calibration.

CONCLUSIONS : Our study recalibrated, externally validated the PSI and CURB-65 for predicting 30-day mortality and CCI, and developed a model for predicting CCI. Our tool can potentially guide clinicians in Qatar to stratify patients with COVID-19 pneumonia.

Elmoheen Amr, Abdelhafez Ibrahim, Awad Waleed, Bahgat Mohamed, Elkandow Ali, Tarig Amina, Arshad Nauman, Mohamed Khoulod, Al-Hitmi Maryam, Saad Mona, Emam Fatima, Taha Samah, Bashir Khalid, Azad Aftab

2021-Aug-17

COVID-19, CURB-65, PSI, critical care intervention, mortality

Internal Medicine Internal Medicine

Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients.

In American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

This analysis, using data from the Brazilian kidney transplant (KT) COVID-19 study, seeks to develop a prediction score to assist in COVID-19 risk stratification in KT recipients. 1,379 patients (35 sites) were enrolled, and a machine learning approach was used to fit models in a derivation cohort. A reduced Elastic Net model was selected, and the accuracy to predict the 28-day fatality after the COVID-19 diagnosis, assessed by the area under the ROC curve (AUC-ROC), was confirmed in a validation cohort. The better calibration values were used to build the applicable ImAgeS score. The 28-day fatality rate was 17% (n=235), which was associated with increasing age, hypertension and cardiovascular disease, higher body mass index, dyspnea, and use of mycophenolate acid or azathioprine. Higher kidney graft function, longer time of symptoms until COVID-19 diagnosis, presence of anosmia or coryza, and use of mTOR inhibitor were associated with reduced risk of death. The coefficients of the best model were used to build the predictive score, which achieved an AUC-ROC of 0.767 [95% CI 0.698 - 0.834] in the validation cohort. In conclusion, the easily applicable predictive model could assist health-care practitioners in identifying non-hospitalized kidney transplant patients that may require more intensive monitoring.

Modelli de Andrade Luis Gustavo, de Sandes-Freitas Tainá Veras, Requião-Moura Lúcio R, Almeida Viana Laila, Cristelli Marina Pontello, Garcia Valter Duro, Alcântara Aline Lima Cunha, de Matos Esmeraldo Ronaldo, Abbud Filho Mario, Pacheco-Silva Alvaro, Cristina Ribeiro de Lima Carneiro Erika, Manfro Roberto Ceratti, Costa Kellen Micheline Alves Henrique, Simão Denise Rodrigues, de Sousa Marcos Vinicius, de Mello Santana Viviane Brandão Bandeira, Noronha Irene L, Romão Elen Almeida, Zanocco Juliana Aparecida, Arimatea Gustavo Guilherme Queiroz, De Boni Monteiro de Carvalho Deise, Tedesco-Silva Helio, Medina-Pestana José

2021-Aug-20

General General

Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation.

In IEEE journal of biomedical and health informatics

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. Features extracted from the two parallel encoders are concatenated for the subsequent decoder part. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. In addition, experimental results on large scale 2D dataset with CT slices show that our method significantly outperforms cutting-edge segmentation methods on all evaluation metrics. Our proposed method promotes new insights into annotation-efficient deep learning for COVID-19 infection segmentation and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations.

Zhang Yichi, Liao Qingcheng, Yuan Lin, Zhu He, Xing Jiezhen, Zhang Jicong

2021-Aug-20

General General

Natural Language Processing Enabling COVID-19 Predictive Analytics to Support Data-Driven Patient Advising and Pooled Testing.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : The COVID-19 pandemic response at MUSC included virtual care visits for patients with suspected SARS-CoV-2 infection. The telehealth system used for these visits only exports a text note to integrate with the EHR, but structured and coded information about COVID-19 (e.g., exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing.

METHODS : To capture COVID-19 information from multiple sources, a new data mart and a new Natural Language Processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information.

RESULTS : The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81-92% and to enable pooled testing with a negative predictive value of 90-91% reducing the required tests to about 63%.

CONCLUSION : SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing.

Meystre Stéphane M, Heider Paul M, Kim Youngjun, Davis Matthew, Obeid Jihad, Madory James, Alekseyenko Alexander V

2021-Aug-20

Data Science [L01.305], Machine Learning [G17.035.250.500], Medical Informatics [L01.313.500], Natural Language Processing (NLP) [L01.224.050.375.580]

General General

Digital Transformation in Ophthalmic Clinical Care During the COVID-19 Pandemic.

In Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

COVID-19 has placed unprecedented pressure on health systems globally, whereas simultaneously stimulating unprecedented levels of transformation. Here, we review digital adoption that has taken place during the pandemic to drive improvements in ophthalmic clinical care, with a specific focus on out-of-hospital triage and services, clinical assessment, patient management, and use of electronic health records. We show that although there have been some successes, shortcomings in technology infrastructure prepandemic became only more apparent and consequential as COVID-19 progressed. Through our review, we emphasize the need for clinicians to better grasp and harness key technology trends such as telecommunications and artificial intelligence, so that they can effectively and safely shape clinical practice using these tools going forward.

Kim Soyang Ella, Logeswaran Abison, Kang Swan, Stanojcic Nick, Wickham Louisa, Thomas Peter, Li Ji-Peng Olivia

General General

Risk profiles for negative and positive COVID-19 hospitalized patients.

In Computers in biology and medicine

COVID-19 is a viral infection that affects people differently, where the majority of cases develop mild symptoms, some people require hospitalization, and unfortunately, a small number of patients perish. Hence, identifying risk factors is critical for physicians to make treatment decisions. The purpose of this article is to determine whether unsupervised analysis of risk factors in positive and negative COVID-19 subjects can aid in the identification of a set of reliable and clinically relevant risk profiles. Positive and negative patients hospitalized were randomly selected from the Mexican Open Registry between March and May 2020. Thirteen risk factors, three distinct outcomes, and COVID-19 test results were used to categorize registry patients. As a result, the dataset was reported via 6144 different risk profiles for each age group. The unsupervised learning method is proposed in this study to discover the most prevalent risk profiles. The data was partitioned into discovery (70%) and validation (30%) sets. The discovery set was analyzed using the partition around medoids (PAM) method, and the stable set of risk profiles was estimated using robust consensus clustering. The PAM models' reliability was validated by predicting the risk profile of subjects from the validation set and patients admitted in November 2020. In the validation set, the clinical relevance of the risk profiles was evaluated by determining the prevalence of three patient outcomes: pneumonia diagnosis, ICU admission, or death. Six positive and five negative COVID-19 risk profiles were identified, with significant statistical differences between them. As a result, PAM clustering with consensus mapping is a viable method for discovering unsupervised risk profiles in subjects with severe respiratory health problems.

Nezhadmoghadam Fahimeh, Tamez-Peña Jose

2021-Aug-09

COVID-19, Cluster analysis, Consensus clustering, Decision trees, Reproducibility of results, Risk factors, Unsupervised machine learning

General General

Using social media Reddit data to examine foster families' concerns and needs during COVID-19.

In Child abuse & neglect

BACKGROUND : COVID-19 is likely to have negatively impacted foster families but few data sources are available to confirm this.

OBJECTIVE : The current study used Reddit social media data to examine how foster families are faring in the pandemic. Discussion topics were identified and examined for changes before and after COVID-19.

PARTICIPANTS AND SETTING : Comments were collected from three Reddit online discussion boards dedicated to foster families (N = 11,830).

METHODS : We used machine learning techniques, including Latent Dirichlet Allocation, for topic modeling and textual analysis for qualitative coding of the Reddit comments.

RESULTS : Results showed that three main topics had both significant quantitative and meaningful qualitative changes before and after COVID-19. There were significant increases in conversation about becoming a foster parent (F = 5.75, p = 0.02) and activities for foster children (F = 10.61, p = 0.001), whereas there was a significant decrease in discussing permanency (F = 9.46, p = 0.003) before and after the onset of COVID-19. Qualitative coding showed that regarding the topic of becoming a parent, excitement over approval of foster care license before COVID-19 shifted to foster families' increased anxieties about delays in their licensing cases after COVID-19. For permanency, content changed from the best interest of the child and reunifications before COVID-19 to concerns over family separations and permanency challenges after COVID-19. Regarding activities for foster children, content related to everyday activities before COVID-19 changed to specific activities foster children and families could do during lockdowns. Results suggest areas child welfare workers may focus on to better support foster families during and after the pandemic.

Lee Joyce Y, Chang Olivia D, Ammari Tawfiq

2021-Aug-10

COVID-19, Former foster youth, Foster families, Machine learning, Textual analysis, Topic modeling

General General

Substance use, depression, and loneliness among American veterans during the COVID-19 pandemic.

In The American journal on addictions ; h5-index 27.0

BACKGROUND AND OBJECTIVES : Behavioral health issues, such as substance use, depression, and social isolation, are of grave concern during COVID-19, especially for vulnerable populations. One such population is US veterans, who have high rates of pre-existing behavioral health conditions and may thus be at-risk for poorer outcomes. The current study aimed to investigate substance use among US veterans during COVID-19 as a function of pre-existing depression, loneliness, and social support.

METHODS : We investigated the relationship between pre-pandemic depression and substance use during COVID-19 using linear (alcohol) and logistic (cannabis) regression among a large sample of US veterans (N = 1230). We then tested if loneliness and social support moderated these effects.

RESULTS : Though there was a decrease in alcohol and cannabis use among the overall sample, veterans who screened for depression prior to the pandemic exhibited higher levels of substance use after the pandemic's onset. Loneliness compounded the effects of depression on rates of alcohol use. Social support was not protective for the effects of depression on either alcohol or cannabis use.

DISCUSSION AND CONCLUSIONS : Veterans with pre-existing depression may be in need of attention for substance use behaviors. Interventions aimed at alleviating loneliness among veterans may be useful in mitigating alcohol use, but not cannabis use, amid COVID-19.

SCIENTIFIC SIGNIFICANCE : Our findings are among the first to report tangible behavioral health outcomes experienced by US veterans as a result of COVID-19. Results can help inform treatment efforts for veterans who are struggling with substance use during and post-pandemic.

Fitzke Reagan E, Wang Jennifer, Davis Jordan P, Pedersen Eric R

2021-Aug-19

General General

COVID-19 sentiment analysis via deep learning during the rise of novel cases.

In PloS one ; h5-index 176.0

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.

Chandra Rohitash, Krishna Aswin

2021

Surgery Surgery

Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design.

In Briefings in bioinformatics

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.

Lv Hao, Shi Lei, Berkenpas Joshua William, Dao Fu-Ying, Zulfiqar Hasan, Ding Hui, Zhang Yang, Yang Liming, Cao Renzhi

2021-Aug-19

COVID-19, SARS-CoV-2, artificial intelligence, drug, machine learning, vaccine

Cardiology Cardiology

Predictors of Mortality Among Long-Term Care Residents with SARS-CoV-2 Infection.

In Journal of the American Geriatrics Society ; h5-index 64.0

BACKGROUND : While individuals living in long-term care (LTC) homes have experienced adverse outcomes of SARS-CoV-2 infection, few studies that have examined a broad range of predictors of 30-day mortality in this population.

METHODS : We studied residents living in LTC homes in Ontario, Canada, who underwent PCR testing for SARS-CoV-2 infection from January 1 to August 31, 2020, and examined predictors of all-cause death within 30 days after a positive test for SARS-CoV-2. We examined a broad range of risk factor categories including demographics, comorbidities, functional status, laboratory tests, and characteristics of the LTC facility and surrounding community were examined. In total, 304 potential predictors were evaluated for their association with mortality using machine learning (Random Forest).

RESULTS : A total of 64,733 residents of LTC, median age 86 (78, 91) years (31.8% men), underwent SARS-CoV-2 testing, of whom 5029 (7.8%) tested positive. Thirty-day mortality rates were 28.7% (1442 deaths) after a positive test. Of 59,702 residents who tested negative, 2652 (4.4%) died within 30 days of testing. Predictors of mortality after SARS-CoV-2 infection included age, functional status (e.g., activity of daily living score, pressure ulcer risk), male sex, undernutrition, dehydration risk, prior hospital contacts for respiratory illness, and duration of comorbidities (e.g., heart failure, COPD). Lower GFR, hemoglobin concentration, lymphocyte count, and serum albumin were associated with higher mortality. After combining all covariates to generate a risk index, mortality rate in the highest risk quartile was 48.3% compared to 7% in the first quartile (odds ratio 12.42, 95%CI; 6.67, 22.80, p<0.001). Deaths continued to increase rapidly for 15 days after the positive test.

CONCLUSIONS : LTC residents, particularly those with reduced functional status, comorbidities, and abnormalities on routine laboratory tests, are at high risk for mortality after SARS-CoV-2 infection. Recognizing high-risk residents in LTC may enhance institution of appropriate preventative measures.

Lee Douglas S, Ma Shihao, Chu Anna, Wang Chloe X, Wang Xuesong, Austin Peter C, McAlister Finlay A, Kalmady Vasu Sunil, Kapral Moira K, Kaul Padma, Ko Dennis T, Rochon Paula, Schull Michael J, Rubin Barry B, Wang Bo

2021-Aug-19

General General

Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations.

In Frontiers in research metrics and analytics

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

Aljohani Tahani, Cristea Alexandra I

2021

CNN, MOOC, RNN, employment status, gender, learner profiling, treeLSTM

Ophthalmology Ophthalmology

Teleophthalmology and Artificial Intelligence As Game Changers in Ophthalmic Care After the COVID-19 Pandemic.

In Cureus

The current COVID-19 pandemic has boosted a sudden demand for telemedicine due to quarantine and travel restrictions. The exponential increase in the use of telemedicine is expected to affect ophthalmology drastically. The aim of this review is to discuss the utility, effectiveness and challenges of teleophthalmological new tools for eyecare delivery as well as its implementation and possible facilitation with artificial intelligence. We used the terms: "teleophthalmology," "telemedicine and COVID-19," "retinal diseases and telemedicine," "virtual ophthalmology," "cost effectiveness of teleophthalmology," "pediatric teleophthalmology," "Artificial intelligence and ophthalmology," "Glaucoma and teleophthalmology" and "teleophthalmology limitations" in the database of PubMed and selected the articles being published in the course of 2015-2020. After the initial search, 321 articles returned as relevant. A meticulous screening followed and eventually 103 published manuscripts were included and used as our references. Emerging in the market, teleophthalmology is showing great potential for the future of ophthalmological care, benefiting both patients and ophthalmologists in times of pandemics. The spectrum of eye diseases that could benefit from teleophthalmology is wide, including mostly retinal diseases such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration but also glaucoma and anterior segment conditions. Simultaneously, artificial intelligence provides ways of implementing teleophthalmology easier and with better outcomes, contributing as significant changing factors for ophthalmology practice after the COVID-19 pandemic.

Nikolaidou Anna, Tsaousis Konstantinos T

2021-Jul

artificial intelligence, covid-19, machine learning, teleophthalmology, virtual clinics

Public Health Public Health

The Interactive Management of the SARS-CoV-2 Virus: The Social Cohesion Index, a Methodological-Operational Proposal.

In Frontiers in psychology ; h5-index 92.0

This contribution places itself within the emergency context of the COVID-19 spread. Until medical research identifies a cure acting at an organic level, it is necessary to manage what the emergency generates among the members of the Community in interactive terms in a scientific and methodologically well-founded way. This is in order to promote, among the members of the Community, the pursuit of the common aim of reducing the spread of infection, with a view to community health as a whole. In addition, being at the level of interactions enables us to move towards a change of these interactions in response to the COVID-19 emergency, in order to manage what will happen in the future, in terms of changes in the interactive arrangements after the emergency itself. This becomes possible by shifting away from the use of deterministic-causal references to the use of the uncertainty of interaction as an epistemological foundation principle. Managing the interactive (and non-organic) fallout of the emergency in the Community is made possible by the formalisation of the interactive modalities (the Discursive Repertories) offered by Dialogical Science. To place oneself within this scientific panorama enables interaction measurements: so, the interaction measurement indexes offers a range of generative possibilities of realities built by the speeches of the Community members. Moreover, the Social Cohesion measurement index, in the area of Dialogical Science, makes available to public policies the shared measure of how and by how much the Community is moving towards the common purpose of reducing the contagion spread, rather than moving towards other personal and not shared goals (for instance, having a walk in spite of the lockdown). In this index, the interaction between the Discursive Repertories and the "cohesion weight" associated with them offers a Cohesion output: the data allow to manage operationally what happens in the Community in a shared way and in anticipation, without leaving the interactions between its members to chance. In this way, they can be directed towards the common purpose through appropriate interventions relevant to the interactive set-up described in the data. The Cohesion measure makes it possible to operate effectively and efficiently, thanks to the possibility of monitoring the progress of the interventions implemented and evaluating their effectiveness. In addition, the use of predictive Machine Learning models, applied to interactive cohesion data, allows for immediate and efficient availability of the measure itself, optimising time and resources.

Turchi Gian Piero, Dalla Riva Marta Silvia, Ciloni Caterina, Moro Christian, Orrù Luisa

2021

COVID-19, community, dialogical science, emergency, interaction, public health, social cohesion

Ophthalmology Ophthalmology

[Telemedical applications in ophthalmology in times of COVID-19].

In Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft

BACKGROUND : During the coronavirus disease 2019 (COVID-19) pandemic access to and utilization of ophthalmologic healthcare providers was partially restricted.

OBJECTIVE : This article provides an overview of already available tele-ophthalmologic applications for better care during the pandemic as well as those still under development.

MATERIAL AND METHODS : The study included an analysis of current scientific publications, analysis of unrestricted screening applications in smart device app stores as well as telemetric medical products specifically designed for home monitoring and discussion of the requirements of an integrated ophthalmologic video consultation.

RESULTS : There is significant interest in tele-ophthalmologic applications and devices as evidenced by a rise in the number of relevant publications. Freely available screening tests for smart phones and tablets are as a rule currently not validated and show significant discrepancies from established standard tests. Telemetric medical devices show great potential for home monitoring in chronic ophthalmologic diseases but must first become established in the clinical routine.

CONCLUSION : There is an unmet need for systematic analysis, development and validation of telemedical applications and medical products for ophthalmology in order to advantageously utilize the potential of telemedicine and to incorporate this into an ophthalmologic video consultation.

Choritz Lars, Hoffmann Michael, Thieme Hagen

2021-Aug-18

Artificial intelligence, COVID-19 pandemic, Home monitoring, Self-measurement, Video consultation

General General

Erratum: On the role of artificial intelligence in medical imaging of COVID-19.

In Patterns (New York, N.Y.)

[This corrects the article DOI: 10.1016/j.patter.2021.100269.].

Born Jannis, Beymer David, Rajan Deepta, Coy Adam, Mukherjee Vandana V, Manica Matteo, Prasanna Prasanth, Ballah Deddeh, Guindy Michal, Shaham Dorith, Shah Pallav L, Karteris Emmanouil, Robertus Jan L, Gabrani Maria, Rosen-Zvi Michal

2021-Aug-13

General General

Deep forest model for diagnosing COVID-19 from routine blood tests.

In Scientific reports ; h5-index 158.0

The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

AlJame Maryam, Imtiaz Ayyub, Ahmad Imtiaz, Mohammed Ameer

2021-Aug-17

Cardiology Cardiology

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

In Critical care (London, England)

BACKGROUND : Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.

METHODS : A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.

RESULTS : 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.

CONCLUSIONS : Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Magunia Harry, Lederer Simone, Verbuecheln Raphael, Gilot Bryant Joseph, Koeppen Michael, Haeberle Helene A, Mirakaj Valbona, Hofmann Pascal, Marx Gernot, Bickenbach Johannes, Nohe Boris, Lay Michael, Spies Claudia, Edel Andreas, Schiefenhövel Fridtjof, Rahmel Tim, Putensen Christian, Sellmann Timur, Koch Thea, Brandenburger Timo, Kindgen-Milles Detlef, Brenner Thorsten, Berger Marc, Zacharowski Kai, Adam Elisabeth, Posch Matthias, Moerer Onnen, Scheer Christian S, Sedding Daniel, Weigand Markus A, Fichtner Falk, Nau Carla, Prätsch Florian, Wiesmann Thomas, Koch Christian, Schneider Gerhard, Lahmer Tobias, Straub Andreas, Meiser Andreas, Weiss Manfred, Jungwirth Bettina, Wappler Frank, Meybohm Patrick, Herrmann Johannes, Malek Nisar, Kohlbacher Oliver, Biergans Stephanie, Rosenberger Peter

2021-Aug-17

ARDS, COVID-19, Critical care, Outcome, Prognostic models

General General

GUIdEStaR (G-quadruplex, uORF, IRES, Epigenetics, Small RNA, Repeats), the integrated metadatabase in conjunction with neural network methods

bioRxiv Preprint

GUIdEStaR integrates existing databases of various types of G-quadruplex, upstream Open Reading Frame (uORF), Internal Ribosome Entry Site (IRES), methylation to RNA and histone protein, small RNA, and repeats. GUIdEStaR consists of approx. 40,000 genes and 320,000 transcripts. An mRNA transcript is divided into 5 regions (5'UTR, 3'UTR, exon, intron, and biological region) where each region contains presence-absence data of 169 different types of elements. Recently, artificial intelligence (AI) based analysis of sequencing data has been gaining popularity in the area of bioinformatics. GUIdEStaR generates datasets that can be used as inputs to AI methods. At the GUIdEStaR homepage, users submit gene symbols by clicking a Send button, and shortly result files in CSV format are available for download at the result website. Users have an option to send the result files to their email addresses. Additionally, the entire database and the example Java codes are also freely available for download. Here, we demonstrate the database usage with three neural network classification studies- 1) small RNA study for classifying transcription factor (TF) genes into either one of TF mediated by small RNA originated from SARS-CoV-2 or by human microRNA (miRNA), 2) cell membrane receptor study for classifying receptor genes as either with virus interaction or without one, and 3) nonsense mediated mRNA decay (NMD) study for classifying cell membrane and nuclear receptors as either NMD target or non-target. GUIdEStaR is available for access to the easy-to-use web-based database at www.guidestar.kr and for download at https://sourceforge.net/projects/guidestar.

Kang, J. E.

2021-08-19

General General

Proceedings of the 1st International Workshop on Adaptive Cyber Defense

ArXiv Preprint

The 1st International Workshop on Adaptive Cyber Defense was held as part of the 2021 International Joint Conference on Artificial Intelligence. This workshop was organized to share research that explores unique applications of Artificial Intelligence (AI) and Machine Learning (ML) as foundational capabilities for the pursuit of adaptive cyber defense. The cyber domain cannot currently be reliably and effectively defended without extensive reliance on human experts. Skilled cyber defenders are in short supply and often cannot respond fast enough to cyber threats. Building on recent advances in AI and ML the Cyber defense research community has been motivated to develop new dynamic and sustainable defenses through the adoption of AI and ML techniques to both cyber and non-cyber settings. Bridging critical gaps between AI and Cyber researchers and practitioners can accelerate efforts to create semi-autonomous cyber defenses that can learn to recognize and respond to cyber attacks or discover and mitigate weaknesses in cooperation with other cyber operation systems and human experts. Furthermore, these defenses are expected to be adaptive and able to evolve over time to thwart changes in attacker behavior, changes in the system health and readiness, and natural shifts in user behavior over time. The Workshop (held on August 19th and 20th 2021 in Montreal-themed virtual reality) was comprised of technical presentations and a panel discussion focused on open problems and potential research solutions. Workshop submissions were peer reviewed by a panel of domain experts with a proceedings consisting of 10 technical articles exploring challenging problems of critical importance to national and global security. Participation in this workshop offered new opportunities to stimulate research and innovation in the emerging domain of adaptive and autonomous cyber defense.

Damian Marriott, Kimberly Ferguson-Walter, Sunny Fugate, Marco Carvalho

2021-08-19

General General

Prediction of COVID Criticality Score with Laboratory, Clinical and CT Images using Hybrid Regression Models.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Rapid and precise diagnosis of COVID-19 is very critical in hotspot regions. The main aim of this proposed work is to investigate the baseline, laboratory and CT features of COVID-19 affected patients of two groups (Early and Critical stages). The detection model for COVID-19 is built depending upon the manifestations that define the severity of the disease.

METHODS : The CT scan images are fed into the various deep learning, machine learning and hybrid learning models to mine the necessary features and predict CT Score. The predicted CT score along with other clinical, laboratory and CT scan image features are then passed to train the various Regression models for predicting the COVID Criticality (CC) Score. These baseline, laboratory and CT features of COVID-19 are reduced using Statistical analysis and Univariate logistic regression analysis.

RESULTS : When analysing the prediction of CT scores using images alone, AlexNet+Lasso yields better outcome with regression score of 0.9643 and RMSE of 0.0023 when compared with Decision tree (RMSE of 0.0034; Regression score of 0.9578) and GRU (RMSE of 0.1253; regression score of 0.9323). When analysing the prediction of CC scores using CT scores and other baseline, laboratory and CT features, VGG-16+Linear Regression yields better results with regression score of 0.9911 and RMSE of 0.0002 when compared with Linear SVR (RMSE of 0.0006; Regression score of 0.9911) and LSTM (RMSE of 0.0005; Regression score of 0.9877). The correlation analysis is performed to identify the significance of utilizing other features in prediction of CC Score. The correlation coefficient of CT scores with actual value is 0.93 and 0.92 for Early stage group and Critical stage group respectively. The correlation coefficient of CC scores with actual value is 0.96 for Early stage group and 0.95 for Critical stage group.The classification of COVID-19 patients are carried out with the help of predicted CC Scores.

CONCLUSIONS : This proposed work is carried out in the motive of helping radiologists in faster categorization of COVID patients as Early or Severe staged using CC Scores. The automated prediction of COVID Criticality Score using our diagnostic model can help radiologists and physicians save time for carrying out further treatment and procedures.

Perumal Varalakshmi, Narayanan Vasumathi, Rajasekar Sakthi Jaya Sundar

2021-Aug-10

COVID Criticality score, Chest CT images, Clinical features, Convolutional neural network (CNN), Laboratory features, Regression models

General General

Antibody Attributes that Predict the Neutralization and Effector Function of Polyclonal Responses to SARS-CoV-2.

In medRxiv : the preprint server for health sciences

While antibodies provide significant protection from SARS-CoV-2 infection and disease sequelae, the specific attributes of the humoral response that contribute to immunity are incompletely defined. In this study, we employ machine learning to relate characteristics of the polyclonal antibody response raised by natural infection to diverse antibody effector functions and neutralization potency with the goal of generating both accurate predictions of each activity based on antibody response profiles as well as insights into antibody mechanisms of action. To this end, antibody-mediated phagocytosis, cytotoxicity, complement deposition, and neutralization were accurately predicted from biophysical antibody profiles in both discovery and validation cohorts. These predictive models identified SARS-CoV-2-specific IgM as a key predictor of neutralization activity whose mechanistic relevance was supported experimentally by depletion. Validated models of how different aspects of the humoral response relate to antiviral antibody activities suggest desirable attributes to recapitulate by vaccination or other antibody-based interventions.

Natarajan Harini, Xu Shiwei, Crowley Andrew R, Butler Savannah E, Weiner Joshua A, Bloch Evan M, Littlefield Kirsten, Benner Sarah E, Shrestha Ruchee, Ajayi Olivia, Wieland-Alter Wendy, Sullivan David, Shoham Shmuel, Quinn Thomas C, Casadevall Arturo, Pekosz Andrew, Redd Andrew D, Tobian Aaron A R, Connor Ruth I, Wright Peter F, Ackerman Margaret E

2021-Aug-08

Public Health Public Health

Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England.

In EClinicalMedicine

Background : Since its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital.

Methods : We examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021.

Findings : Across data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May.

Interpretation : The outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control efforts.

Funding : National Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy, Academy of Medical Sciences Bill,Melinda Gates Foundation, Imperial College Healthcare NHS Trust, The Novo Nordisk Foundation, MRC Centre for Global Infectious Disease Analysis, Community Jameel, Cancer Research UK, Imperial College COVID-19 Research Fund, Medical Research Council, Wellcome Sanger Institute.

Mishra Swapnil, Mindermann Sören, Sharma Mrinank, Whittaker Charles, Mellan Thomas A, Wilton Thomas, Klapsa Dimitra, Mate Ryan, Fritzsche Martin, Zambon Maria, Ahuja Janvi, Howes Adam, Miscouridou Xenia, Nason Guy P, Ratmann Oliver, Semenova Elizaveta, Leech Gavin, Sandkühler Julia Fabienne, Rogers-Smith Charlie, Vollmer Michaela, Unwin H Juliette T, Gal Yarin, Chand Meera, Gandy Axel, Martin Javier, Volz Erik, Ferguson Neil M, Bhatt Samir, Brauner Jan M, Flaxman Seth

2021-Sep

Epidemiology, Genomic surveillance, Public health, SARS-CoV-2, Variants of concern, Waste water monitoring

General General

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans.

In PeerJ. Computer science

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.

Alshazly Hammam, Linse Christoph, Abdalla Mohamed, Barth Erhardt, Martinetz Thomas

2021

Automated diagnosis, COVID-19 detection, Computed tomography, Deep learning, Multi-class classification, SARS-CoV-2

Radiology Radiology

Multidimensional Evaluation of All-Cause Mortality Risk and Survival Analysis for Hospitalized Patients with COVID-19.

In International journal of medical sciences

Background: Coronavirus disease 2019 (COVID-19) has caused over 3.8 million deaths globally. Up to date, the number of death in 2021 is more than that in 2020 globally. Here, we aimed to compare clinical characteristics of deceased patients and recovered patients, and analyze the risk factors of death to help reduce mortality of COVID-19. Methods: In this retrospective study, a total of 2719 COVID-19 patients were enrolled, including 109 deceased patients and 2610 recovered patients. Medical records of all patients were collected between February 4, 2020, and April 7, 2020. Clinical characteristics, laboratory indices, treatments, and deep-learning system- assessed lung lesion volumes were analyzed. The effect of different medications on survival time of fatal cases was also investigated. Results: The deceased patients were older (73 years versus 60 years) and had a male predominance. Nausea (10.1% versus 4.1%) and dyspnea (54.1% versus 39.2%) were more common in deceased patients. The proportion of patients with comorbidities in deceased patients was significantly higher than those in recovered patients. The median times from hospital admission to outcome in deceased patients and recovered patients were 9 days and 13 days, respectively. Patients with severe or critical COVID-19 were more frequent in deceased group. Leukocytosis (11.35×109/L versus 5.60×109/L) and lymphocytopenia (0.52×109/L versus 1.58×109/L) were shown in patients who died. The level of prothrombin time, activated partial prothrombin time, D-dimer, aspartate aminotransferase, alanine aminotransferase, urea, creatinine, creatine kinase, glucose, brain natriuretic peptide, and inflammatory indicators were significantly higher in deceased patients than in recovered patients. The volumes of ground-glass, consolidation, total lesions and total lung in all patients were quantified. Complications were more common in deceased patients than in recovered patients; respiratory failure (57.8%), septic shock (36.7%), and acute respiratory distress syndrome (26.6%) were the most common complications in patients who died. Many treatments were more frequent in deceased patients, such as antibiotic therapy (88.1% versus 53.7%), glucocorticoid treatment (70.6% versus 11.0%), intravenous immunoglobin treatment (36.6% versus 4.9%), invasive mechanical ventilation (62.3% versus 3.8%). Antivirals, antibiotics, traditional Chinese medicines and glucocorticoid treatment may significantly increase the survival time of fatal cases. Quantitative computed tomography imaging results were correlated with biochemical markers. Conclusions: Most patients with fatal outcomes were more likely to have common comorbidities. The leading causes of death were respiratory failure and multiple organ dysfunction syndrome. Acute respiratory distress syndrome, respiratory failure and septic shock were the most common serious complications. Antivirals, antibiotics, traditional Chinese medicines, and glucocorticoid treatment may prolong the survival time of deceased patients with COVID-19.

Li Jingwen, Luo Hu, Deng Gang, Chang Jinying, Qiu Xiaoming, Liu Chen, Qin Bo

2021

COVID-19, mortality risk, survival analysis

Public Health Public Health

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.

In Expert systems with applications

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Khan Muzammil, Mehran Muhammad Taqi, Haq Zeeshan Ul, Ullah Zahid, Naqvi Salman Raza, Ihsan Mehreen, Abbass Haider

2021-Dec-15

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

Radiology Radiology

iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients.

In NPJ digital medicine

Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.

Wang Jun, Liu Chen, Li Jingwen, Yuan Cheng, Zhang Lichi, Jin Cheng, Xu Jianwei, Wang Yaqi, Wen Yaofeng, Lu Hongbing, Li Biao, Chen Chang, Li Xiangdong, Shen Dinggang, Qian Dahong, Wang Jian

2021-Aug-16

Pathology Pathology

Signatures of COVID-19 Severity and Immune Response in the Respiratory Tract Microbiome.

In mBio

Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease. Here, we investigated the respiratory tract microbiome in coronavirus disease 2019 (COVID-19) and its relationship to disease severity, systemic immunologic features, and outcomes. We examined 507 oropharyngeal, nasopharyngeal, and endotracheal samples from 83 hospitalized COVID-19 patients as well as non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, and the commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR. We found that COVID-19 patients had upper respiratory microbiome dysbiosis and greater change over time than critically ill patients without COVID-19. Oropharyngeal microbiome diversity at the first time point correlated inversely with disease severity during hospitalization. Microbiome composition was also associated with systemic immune parameters in blood, as measured by lymphocyte/neutrophil ratios and immune profiling of peripheral blood mononuclear cells. Intubated patients showed patient-specific lung microbiome communities that were frequently highly dynamic, with prominence of Staphylococcus. Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity. Thus, the respiratory tract microbiome and commensal viruses are disturbed in COVID-19 and correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, its possible use as biomarkers, and the role of bacterial and viral taxa identified here in COVID-19 pathogenesis. IMPORTANCE COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of the respiratory tract, results in highly variable outcomes ranging from minimal illness to death, but the reasons for this are not well understood. We investigated the respiratory tract bacterial microbiome and small commensal DNA viruses in hospitalized COVID-19 patients and found that each was markedly abnormal compared to that in healthy people and differed from that in critically ill patients without COVID-19. Early airway samples tracked with the level of COVID-19 illness reached during hospitalization, and the airway microbiome also correlated with immune parameters in blood. These findings raise questions about the mechanisms linking SARS-CoV-2 infection and other microbial inhabitants of the airway, including whether the microbiome might regulate severity of COVID-19 disease and/or whether early microbiome features might serve as biomarkers to discriminate disease severity.

Merenstein Carter, Liang Guanxiang, Whiteside Samantha A, Cobián-Güemes Ana G, Merlino Madeline S, Taylor Louis J, Glascock Abigail, Bittinger Kyle, Tanes Ceylan, Graham-Wooten Jevon, Khatib Layla A, Fitzgerald Ayannah S, Reddy Shantan, Baxter Amy E, Giles Josephine R, Oldridge Derek A, Meyer Nuala J, Wherry E John, McGinniss John E, Bushman Frederic D, Collman Ronald G

2021-Aug-17

16S rRNA gene sequencing, SARS-CoV-2, anellovirus, coronavirus, redondovirus, respiratory microbiome

Radiology Radiology

Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.

In Cognitive computation

Background : COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation.

Methods : This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML.

Result : The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset.

Conclusion : The proposed method achieved better results when compared to the latest published work in this domain.

Amin Javaria, Sharif Muhammad, Gul Nadia, Kadry Seifedine, Chakraborty Chinmay

2021-Aug-10

CGAN, Classical machine learning, Quanvolutional neural network, ReLU, Softmax

Public Health Public Health

Profiling of Oral Microbiota and Cytokines in COVID-19 Patients.

In Frontiers in microbiology

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recently demonstrated in the sputum or saliva, suggesting how the shedding of viral RNA outlasts the end of symptoms. Recent data from transcriptome analysis show that the oral cavity mucosa harbors high levels of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease, serine 2 (TMPRSS2), highlighting its role as a double-edged sword for SARS-CoV-2 body entrance or interpersonal transmission. Here, we studied the oral microbiota structure and inflammatory profile of 26 naive severe coronavirus disease 2019 (COVID-19) patients and 15 controls by 16S rRNA V2 automated targeted sequencing and magnetic bead-based multiplex immunoassays, respectively. A significant diminution in species richness was observed in COVID-19 patients, along with a marked difference in beta-diversity. Species such as Prevotella salivae and Veillonella infantium were distinctive for COVID-19 patients, while Neisseria perflava and Rothia mucilaginosa were predominant in controls. Interestingly, these two groups of oral species oppositely clustered within the bacterial network, defining two distinct Species Interacting Groups (SIGs). COVID-19-related pro-inflammatory cytokines were found in both oral and serum samples, along with a specific bacterial consortium able to counteract them. We introduced a new parameter, named CytoCOV, able to predict COVID-19 susceptibility for an unknown subject at 71% of power with an Area Under Curve (AUC) equal to 0.995. This pilot study evidenced a distinctive oral microbiota composition in COVID-19 subjects, with a definite structural network in relation to secreted cytokines. Our results would be usable in clinics against COVID-19, using bacterial consortia as biomarkers or to reduce local inflammation.

Iebba Valerio, Zanotta Nunzia, Campisciano Giuseppina, Zerbato Verena, Di Bella Stefano, Cason Carolina, Luzzati Roberto, Confalonieri Marco, Palamara Anna Teresa, Comar Manola

2021

COVID-19, cytokines, machine learning, metagenomics, microbiota (D064307), network analysis, oral microbiota

General General

Intersection of Data Science and Smart Destinations: A Systematic Review.

In Frontiers in psychology ; h5-index 92.0

This systematic review adopts a formal and structured approach to review the intersection of data science and smart tourism destinations in terms of components found in previous research. The study period corresponds to 1995-2021 focusing the analysis mainly on the last years (2015-2021), identifying and characterizing the current trends on this research topic. The review comprises documentary research based on bibliometric and conceptual analysis, using the VOSviewer and SciMAT software to analyze articles from the Web of Science database. There is growing interest in this research topic, with more than 300 articles published annually. Data science technologies on which current smart destinations research is based include big data, smart data, data analytics, social media, cloud computing, the internet of things (IoT), smart card data, geographic information system (GIS) technologies, open data, artificial intelligence, and machine learning. Critical research areas for data science techniques and technologies in smart destinations are public tourism marketing, mobility-accessibility, and sustainability. Data analysis techniques and technologies face unprecedented challenges and opportunities post-coronavirus disease-2019 (COVID-19) to build on the huge amount of data and a new tourism model that is more sustainable, smarter, and safer than those previously implemented.

Aguirre Montero Alexander, López-Sánchez José Antonio

2021

COVID-19 pandemic, bibliometric review, conceptual analysis, data science, data science technologies, marketing data science, smart destinations

General General

Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19.

In Frontiers in pharmacology

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

Smith Daniel P, Oechsle Olly, Rawling Michael J, Savory Ed, Lacoste Alix M B, Richardson Peter John

2021

COVID-19, SARS-CoV-2, drug repurposing, human computer interaction, knowledge discovery and data mining, knowledge graph

General General

Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM.

In Soft computing

The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks' capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm's local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy.

Rauf Hafiz Tayyab, Gao Jiechao, Almadhor Ahmad, Arif Muhammad, Nafis Md Tabrez

2021-Aug-11

COVID-19, Gaussian distribution, Gaussian inertia weight, LSTM

General General

Application of machine learning (ML) and internet of things (IoT) in healthcare to predict and tackle pandemic situation.

In Distributed and parallel databases

The pandemic situation has pretentious the habitual life of the human, it also has surpassed the regional, social, business activities and forced human society to live in a limited boundary. In this paper, the application of the internet of things (IoT) and machine learning (ML) based system to combat pandemic situation in health care application has been discussed. The developed ML and IoT based monitoring system help in tracking the infected persons from the previous data and makes them get isolate from the non-infected person. The developed ML combined IoT system uses parallel computing in tracking the pandemic disease and also in the prevention of pandemic disease by predicting and analysing the data using artificial intelligence. The implementation of ML-based IoT in the pandemic situation in healthcare application has proved its performance in tracking and prevents the spreading of pandemic disease. It also further has a positive impact on reducing medical costs and has recorded improved treatment for infected patients. The proposed methodology has an accuracy of 93 % in monitoring and tracking. The result obtained help in preventing the spread of the pandemic and provide support to the healthcare system.

Sitharthan R, Rajesh M

2021-Aug-07

** Artificial intelligence , COVID-19 , Epidemic-monitoring and control , Healthcare , Internet of things , Machine learning **

General General

Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays.

In Pattern recognition

The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets.

Guarrasi Valerio, D’Amico Natascha Claudia, Sicilia Rosa, Cordelli Ermanno, Soda Paolo

2022-Jan

COVID-19, Convolutional neural networks, Deep-learning, Multi-expert systems, Optimization, X-ray

General General

Multi-Label Segmentation and Detection of COVID-19 Abnormalities from Chest Radiographs Using Deep Learning.

In Optik

Due to COVID-19, demand for Chest Radiographs (CXRs) have increased exponentially. Therefore, we present a novel fully automatic modified Attention U-Net (CXAU-Net) multi-class segmentation deep model that can detect common findings of COVID-19 in CXR images. The architectural design of this model includes three novelties: first, an Attention U-net model with channel and spatial attention blocks is designed that precisely localize multiple pathologies; second, dilated convolution applied improves the sensitivity of the model to foreground pixels with additional receptive fields valuation, and third a newly proposed hybrid loss function combines both area and size information for optimizing model. The proposed model achieves average accuracy, DSC, and Jaccard index scores of 0.951, 0.993, 0.984, and 0.921, 0.985, 0.973 for image-based and patch-based approaches respectively for multi-class segmentation on Chest X-ray 14 dataset. Also, average DSC and Jaccard index scores of 0.998, 0.989 are achieved for binary-class segmentation on the Japanese Society of Radiological Technology (JSRT) CXR dataset. These results illustrate that the proposed model outperformed the state-of-the-art segmentation methods.

Arora Ruchika, Saini Indu, Sood Neetu

2021-Aug-08

Attention U-Net, Covid-19 dataset, Deep Learning, Semantic segmentation

Surgery Surgery

[The impact of COVID-19 in patients with severe aortic stenosis: artificial intelligence research.]

In Cirugia espanola

INTRODUCTION : Untreated, severe, symptomatic aortic stenosis is associated with an ominous diagnosis without intervention. This study aims to determine the impact of the COVID-19 pandemic on the mortality of patients with severe stenosis during the first wave and compare it with the same period last year.

METHODS : All patients who went to the hospitals in a spanish region during the first wave, and in the same period of previous year, were analyzed using Artificial Intelligence-based software, evaluating the mortality of patients with severe aortic stenosis with and without COVID-19 during the pandemic and the pre-COVID era. Mortality of the three groups were compared. Regarding cardiac surgeries was a tendency to decrease (p=0.07) in patients without COVID-19 between the pandemic and the previous period was observed. A significant decrease of surgeries between patients with COVID-19 and without COVID-19 was shown.

RESULTS : Data showed 13.82% less admitted patients during the first wave. 1112 of them, had aortic stenosis and 5.48% were COVID-19 positive. Mortality was higher (p=0.01), in COVID-19 negative during the pandemic (4.37%) versus those in the pre-COVID19 era (2.57%); it was also in the COVID-19 positive group (11.47%), versus covid-19 negative (4.37%) during the first wave (p=0.01).

CONCLUSIONS : The study revealed a decrease in patients who went to the hospital and an excess of mortality in patients with severe AD without infection during the first wave, compared to the same period last year; and also, in COVID-19 positive patients versus COVID-19 negative.

Pascual-Tejerina Virginia, Beneyto Pedro, Cantón Tomás, Hernando Luis Manuel, Pajín Luis F, Moreu-Burgos José, López-Almodóvar Luis F, Rodríguez-Padial Luis

2021-Aug-07

Aortic stenosis, COVID-19, Cardiac Surgery, Mortality, Structural heart intervention

General General

Semi-supervised deep learning from time series clinical data for acute respiratory distress syndrome prediction: model development and validation study.

In JMIR formative research

BACKGROUND : A high number of patients hospitalized with COVID-19 also develop Acute Respiratory Distress Syndrome (ARDS).

OBJECTIVE : In response to the need for clinical decision support tools to help manage the next pandemic at early stages when limited labeled data are present, we developed machine learning algorithms using semi-supervised learning (SSL) techniques to predict ARDS in general and COVID-19 populations using limited labeled data.

METHODS : SSL techniques were applied to 29,127 encounters from patients admitted to seven United States hospitals from 5/1/2019-5/1/2021. A recurrent neural network (RNN) using a time series of electronic health record (EHR) data was applied at the time peripheral oxygen saturation (SpO2) fell below the normal range (< 97%) to predict subsequent development of ARDS in the remaining duration of the hospital stay. Model performance was assessed with regard to area under the receiver operating characteristic (AUROC) and area under the precision recall curve (AUPRC) on an external hold out test set.

RESULTS : In the whole dataset, the median time between the first SpO2 measurement < 97% and subsequent respiratory failure was 21 hours. The AUC for predicting subsequent ARDS was 0.73 when training on a labeled dataset of 6,930 patients, 0.78 when training on the labeled dataset that had been augmented with the unlabeled dataset of 16,173 patients using SSL techniques, and 0.84 when training on the entire training set of 23,103 labeled patients.

CONCLUSIONS : In the setting of time series inpatient data, with careful model training design, unlabeled data can be used to improve the performance of machine learning when labeled data to predict ARDS is scarce or expensive.

Lam Carson, Tso Chak Foon, Green-Saxena Abigail, Pellegrini Emily, Iqbal Zohora, Evans Daniel, Hoffman Jana, Calvert Jacob, Mao Qingqing, Das Ritankar

2021-Aug-01

Public Health Public Health

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves.

In Informatics in medicine unlocked

Objectives : The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data.

Methods : We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data.

Results : We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low.

Conclusion : Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

Kolozsvári László Róbert, Bérczes Tamás, Hajdu András, Gesztelyi Rudolf, Tiba Attila, Varga Imre, Al-Tammemi Ala’a B, Szőllősi Gergő József, Harsányi Szilvia, Garbóczy Szabolcs, Zsuga Judit

2021

Artificial intelligence, COVID-19, Epidemic curve, Long short-term memory, Recurrent neural networks

General General

On the relationship between COVID-19 reported fatalities early in the pandemic and national socio-economic status predating the pandemic.

In AIMS public health

This study investigates the relationship between socio-economic determinants pre-dating the pandemic and the reported number of cases, deaths, and the ratio of deaths/cases in 199 countries/regions during the first months of the COVID-19 pandemic. The analysis is performed by means of machine learning methods. It involves a portfolio/ensemble of 32 interpretable models and considers the case in which the outcome variables (number of cases, deaths, and their ratio) are independent and the case in which their dependence is weighted based on geographical proximity. We build two measures of variable importance, the Absolute Importance Index (AII) and the Signed Importance Index (SII) whose roles are to identify the most contributing socio-economic factors to the variability of the COVID-19 pandemic. Our results suggest that, together with the established influence on cases and deaths of the level of mobility, the specific features of the health care system (smart/poor allocation of resources), the economy of a country (equity/non-equity), and the society (religious/not religious or community-based vs not) might contribute to the number of COVID-19 cases and deaths heterogeneously across countries.

Foster Kathleen Lois, Selvitella Alessandro Maria

2021

COVID-19, Socio-economic determinants, cases and deaths, early pandemic, ensemble models, spatial effects, variable selection

General General

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19.

In Frontiers in public health

SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.

Logette Emmanuelle, Lorin Charlotte, Favreau Cyrille, Oshurko Eugenia, Coggan Jay S, Casalegno Francesco, Sy Mohameth François, Monney Caitlin, Bertschy Marine, Delattre Emilie, Fonta Pierre-Alexandre, Krepl Jan, Schmidt Stanislav, Keller Daniel, Kerrien Samuel, Scantamburlo Enrico, Kaufmann Anna-Kristin, Markram Henry

2021

COVID-19, SARS-CoV-2, carbohydrates, glucose, glycolysis, glycosylation, hyperglycemia, ketogenic diet

Public Health Public Health

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images.

In Arabian journal for science and engineering

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

Sundaram Sankar Ganesh, Aloyuni Saleh Abdullah, Alharbi Raed Abdullah, Alqahtani Tariq, Sikkandar Mohamed Yacin, Subbiah Chidambaram

2021-Aug-11

COVID19, Chest X-ray, Classification, Residual SqueezeNet, SegNet, Segmentation, Transfer learning

General General

An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images.

In Arabian journal for science and engineering

Coronavirus (COVID-19) is an epidemic that is rapidly spreading and causing a severe healthcare crisis resulting in more than 40 million confirmed cases across the globe. There are many intensive studies on AI-based technique, which is time consuming and troublesome by considering heavyweight models in terms of more training parameters and memory cost, which leads to higher time complexity. To improve diagnosis, this paper is aimed to design and establish a unique lightweight deep learning-based approach to perform multi-class classification (normal, COVID-19, and pneumonia) and binary class classification (normal and COVID-19) on X-ray radiographs of chest. This proposed CNN scheme includes the combination of three CBR blocks (convolutional batch normalization ReLu) with learnable parameters and one global average pooling (GP) layer and fully connected layer. The overall accuracy of the proposed model achieved 98.33% and finally compared with the pre-trained transfer learning model (DenseNet-121, ResNet-101, VGG-19, and XceptionNet) and recent state-of-the-art model. For validation of the proposed model, several parameters are considered such as learning rate, batch size, number of epochs, and different optimizers. Apart from this, several other performance measures like tenfold cross-validation, confusion matrix, evaluation metrics, sarea under the receiver operating characteristics, kappa score and Mathew's correlation, and Grad-CAM heat map have been used to assess the efficacy of the proposed model. The outcome of this proposed model is more robust, and it may be useful for radiologists for faster diagnostics of COVID-19.

Nayak Soumya Ranjan, Nayak Janmenjoy, Sinha Utkarsh, Arora Vaibhav, Ghosh Uttam, Satapathy Suresh Chandra

2021-Aug-09

COVID-19, Chest X-ray images, Convolutional neural networks, Optimization algorithms, Transfer learning

General General

Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images.

In Arabian journal for science and engineering

COVID-19 is an ongoing pandemic that is widely spreading daily and reaches a significant community spread. X-ray images, computed tomography (CT) images and test kits (RT-PCR) are three easily available options for predicting this infection. Compared to the screening of COVID-19 infection from X-ray and CT images, the test kits(RT-PCR) available to diagnose COVID-19 face problems such as high analytical time, high false negative outcomes, poor sensitivity and specificity. Radiological signatures that X-rays can detect have been found in COVID-19 positive patients. Radiologists may examine these signatures, but it's a time-consuming and error-prone process (riddled with intra-observer variability). Thus, the chest X-ray analysis process needs to be automated, for which AI-driven tools have proven to be the best choice to increase accuracy and speed up analysis time, especially in the case of medical image analysis. We shortlisted four datasets and 20 CNN-based models to test and validate the best ones using 16 detailed experiments with fivefold cross-validation. The two proposed models, ensemble deep transfer learning CNN model and hybrid LSTMCNN, perform the best. The accuracy of ensemble CNN was up to 99.78% (96.51% average-wise), F1-score up to 0.9977 (0.9682 average-wise) and AUC up to 0.9978 (0.9583 average-wise). The accuracy of LSTMCNN was up to 98.66% (96.46% average-wise), F1-score up to 0.9974 (0.9668 average-wise) and AUC up to 0.9856 (0.9645 average-wise). These two best pre-trained transfer learning-based detection models can contribute clinically by offering the patients prediction correctly and rapidly.

Khanna Munish, Agarwal Astitwa, Singh Law Kumar, Thawkar Shankar, Khanna Ashish, Gupta Deepak

2021-Aug-07

COVID-19 detection, Chest X-ray images, Convolutional neural network, Deep learning, Ensemble models

General General

ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences

bioRxiv Preprint

We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clade's information, which means it could be used for predictive tasks using virus sequence analysis.

Pesaranghader, A.; Pelletier, J.; Grenier, J.-C.; Poujol, R.; Hussin, J.

2021-08-16

General General

Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets.

In Computers in biology and medicine

In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO2< 60 mmHg, 489 cases) and Non-Severe (PaO2 ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram, pCO2, blood acid-base balance, prothrombin time, inflammation biomarkers, and glucose. The best fit of variables was successfully confirmed by either the Multilayer Perceptron, a feedforward neural network algorithm that performed machine recognition of severe COVID-19 with 96.5% precision, or by the C4.5 software, a supervised learning algorithm based on an objective-predefined variable (severity) that generated a decision tree with 89.4% precision. Finally, a complex bivariate Pearson's correlation matrix combined with advanced hierarchical clustering (dendrograms) were conducted for knowledge discovery. The hidden structure of the datasets revealed shift patterns related to the development of COVID-19-induced pneumonia that involved the lymphocyte-to-C-reactive protein and leukocyte-to-C-protein ratios, neutrophil %, pH and pCO2. The data mining approaches to the hematological fluctuations associated with severe COVID-19 pneumonia could not only anticipate adverse clinical outcomes, but also reveal putative therapeutic targets.

Pulgar-Sánchez Mary, Chamorro Kevin, Fors Martha, Mora Francisco X, Ramírez Hégira, Fernandez-Moreira Esteban, Ballaz Santiago J

2021-Aug-08

Blood gas analyses, COVID-19, Clinical laboratory techniques, Data mining, Machine learning, Medical informatics applications

General General

A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD.

In Scientific reports ; h5-index 158.0

Inflammatory bowel diseases (IBD), namely Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract. IBD patient conditions and treatments, such as with immunosuppressants, may result in a higher risk of viral and bacterial infection and more severe outcomes of infections. The effect of the clinical and demographic factors on the prognosis of COVID-19 among IBD patients is still a significant area of investigation. The lack of available data on a large set of COVID-19 infected IBD patients has hindered progress. To circumvent this lack of large patient data, we present a random sampling approach to generate clinical COVID-19 outcomes (outpatient management, hospitalized and recovered, and hospitalized and deceased) on 20,000 IBD patients modeled on reported summary statistics obtained from the Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD), an international database to monitor and report on outcomes of COVID-19 occurring in IBD patients. We apply machine learning approaches to perform a comprehensive analysis of the primary and secondary covariates to predict COVID-19 outcome in IBD patients. Our analysis reveals that age, medication usage and the number of comorbidities are the primary covariates, while IBD severity, smoking history, gender and IBD subtype (CD or UC) are key secondary features. In particular, elderly male patients with ulcerative colitis, several preexisting conditions, and who smoke comprise a highly vulnerable IBD population. Moreover, treatment with 5-ASAs (sulfasalazine/mesalamine) shows a high association with COVID-19/IBD mortality. Supervised machine learning that considers age, number of comorbidities and medication usage can predict COVID-19/IBD outcomes with approximately 70% accuracy. We explore the challenge of drawing demographic inferences from existing COVID-19/IBD data. Overall, there are fewer IBD case reports from US states with poor health ranking hindering these analyses. Generation of patient characteristics based on known summary statistics allows for increased power to detect IBD factors leading to variable COVID-19 outcomes. There is under-reporting of COVID-19 in IBD patients from US states with poor health ranking, underpinning the perils of using the repository to derive demographic information.

Roy Satyaki, Sheikh Shehzad Z, Furey Terrence S

2021-Aug-13

General General

An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms.

In Computers in biology and medicine

COVID-19 is a respiratory disease that, as of July 15th, 2021, has infected more than 187 million people worldwide and is responsible for more than 4 million deaths. An accurate diagnosis of COVID-19 is essential for the treatment and control of the disease. The use of computed tomography (CT) has shown to be promising for evaluating patients suspected of COVID-19 infection. The analysis of a CT examination is complex, and requires attention from a specialist. This paper presents a methodology for detecting COVID-19 from CT images. We first propose a convolutional neural network architecture to extract features from CT images, and then optimize the hyperparameters of the network using a tree Parzen estimator to choose the best parameters. Following this, we apply a selection of features using a genetic algorithm. Finally, classification is performed using four classifiers with different approaches. The proposed methodology achieved an accuracy of 0.997, a kappa index of 0.995, an AUROC of 0.997, and an AUPRC of 0.997 on the SARS-CoV-2 CT-Scan dataset, and an accuracy of 0.987, a kappa index of 0.975, an AUROC of 0.989, and an AUPRC of 0.987 on the COVID-CT dataset, using our CNN after optimization of the hyperparameters, the selection of features and the multi-layer perceptron classifier. Compared with pretrained CNNs and related state-of-the-art works, the results achieved by the proposed methodology were superior. Our results show that the proposed method can assist specialists in screening and can aid in diagnosing patients with suspected COVID-19.

Carvalho Edson D, Silva Romuere R V, Araújo Flávio H D, Rabelo Ricardo de A L, de Carvalho Filho Antônio Oseas

2021-Aug-06

COVID-19, Classification, Deep learning, Genetic algorithm, Parameter optimization

General General

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.

In Computers in biology and medicine

The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable diagnostic instruments to help medical personnel. Deep learning (DL) has been demonstrated to be useful for diagnosis using both computed tomography (CT) scans and chest X-rays (CXR), whereby the former typically yields more accurate results. However, the pivoting function of a CT scan during the pandemic presents several drawbacks, including high cost and cross-contamination problems. Radiation-free lung ultrasound (LUS) imaging, which requires high expertise and is thus being underutilised, has demonstrated a strong correlation with CT scan results and a high reliability in pneumonia detection even in the early stages. In this study, we developed a system based on modern DL methodologies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. Using a reliable dataset comprising ultrasound clips originating from linear and convex probes in 2908 frames from 450 hospitalised patients, we conducted an investigation into detecting Covid-19 patterns and ranking them considering two severity scales. This study differs from other research projects by its novel approach involving four and seven classes. Patients admitted to the ED underwent 12 LUS examinations in different chest parts, each evaluated according to standardised severity scales. We adopted residual convolutional neural networks (CNNs), transfer learning, and data augmentation techniques. Hence, employing methodological hyperparameter tuning, we produced state-of-the-art results meeting F1 score levels, averaged over the number of classes considered, exceeding 98%, and thereby manifesting stable measurements over precision and recall.

La Salvia Marco, Secco Gianmarco, Torti Emanuele, Florimbi Giordana, Guido Luca, Lago Paolo, Salinaro Francesco, Perlini Stefano, Leporati Francesco

2021-Aug-08

Deep learning, LUS Score, Lung ultrasound, SARS-CoV-2

Public Health Public Health

Graph-based open-ended survey on concerns related to COVID-19.

In PloS one ; h5-index 176.0

The COVID-19 pandemic is an unprecedented public health crisis with broad social and economic consequences. We conducted four surveys between April and August 2020 using the graph-based open-ended survey (GOS) framework, and investigated the most pressing concerns and issues for the general public in Japan. The GOS framework is a hybrid of the two traditional survey frameworks that allows respondents to post their opinions in a free-format style, which can subsequently serve as one of the choice items for other respondents, just as in a multiple-choice survey. As a result, this framework generates an opinion graph that relates opinions and respondents. We can also construct annotated opinion graphs to achieve a higher resolution. By clustering the annotated opinion graphs, we revealed the characteristic evolution of the response patterns as well as the interconnectedness and multi-faceted nature of opinions. Substantively, our notable finding is that "social pressure," not "infection risk," was one of the major concerns of our respondents. Social pressure refers to criticism and discrimination that they anticipate receiving from others should they contract COVID-19. It is possible that the collectivist nature of Japanese culture coupled with the government's policy of relying on personal responsibility to combat COVID-19 explains some of the above findings, as the latter has led to the emergence of vigilantes. The presence of mutual surveillance can contribute to growing skepticism toward others as well as fear of ostracism, which may have negative consequences at both the societal and individual levels.

Kawamoto Tatsuro, Aoki Takaaki, Ueda Michiko

2021

General General

Deep-learning based detection of COVID-19 using lung ultrasound imagery.

In PloS one ; h5-index 176.0

BACKGROUND : The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.

OBJECTIVE : To evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery.

METHODS : We adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm's step-down correction.

RESULTS : InceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models.

CONCLUSIONS : Deep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

Diaz-Escobar Julia, Ordóñez-Guillén Nelson E, Villarreal-Reyes Salvador, Galaviz-Mosqueda Alejandro, Kober Vitaly, Rivera-Rodriguez Raúl, Lozano Rizk Jose E

2021

General General

COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors.

In IEEE journal of biomedical and health informatics

Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83% and F1-score of 98.71% on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening.

An Jianpeng, Cai Qing, Qu Zhiyong, Gao Zhongke

2021-Aug-13

General General

Integrated Clinical and CT based Artificial Intelligence nomogram for predicting severity and need for ventilator support in COVID-19 patients: A multi-site study.

In IEEE journal of biomedical and health informatics

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N=869 patients from two sites (D1: N=822, D2: N=47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D1 train (N=606) and 30% test-set (D test : D1 test (N=216) + D2 (N=47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D1 train, (D1 train_sub, N=88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D1 train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0.81 on D test and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p<0.001). ClAIN improved the performance over AIP yielding an AUC of 0.84 (p=0.04) on D test. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.

Hiremath Amogh, Bera Kaustav, Yuan Lei, Vaidya Pranjal, Alilou Mehdi, Furin Jennifer, Armitage Keith, Gilkeson Robert, Ji Mengyao, Fu Pingfu, Gupta Amit, Lu Cheng, Madabushi Anant

2021-Aug-13

General General

Determination of critical decision points for COVID-19 measures in Japan.

In Scientific reports ; h5-index 158.0

Coronavirus disease 2019 (COVID-19) has spread throughout the world. The prediction of the number of cases has become essential to governments' ability to define policies and take countermeasures in advance. The numbers of cases have been estimated using compartment models of infectious diseases such as the susceptible-infected-removed (SIR) model and its derived models. However, the required use of hypothetical future values for parameters, such as the effective reproduction number or infection rate, increases the uncertainty of the prediction results. Here, we describe our model for forecasting future COVID-19 cases based on observed data by considering the time delay (tdelay). We used machine learning to estimate the future infection rate based on real-time mobility, temperature, and relative humidity. We then used this calculation with the susceptible-exposed-infectious-removed (SEIR) model to forecast future cases with less uncertainty. The results suggest that changes in mobility affect observed infection rates with 5-10 days of time delay. This window should be accounted for in the decision-making phase especially during periods with predicted infection surges. Our prediction model helps governments and medical institutions to take targeted early countermeasures at critical decision points regarding mobility to avoid significant levels of infection rise.

Kim Junu, Matsunami Kensaku, Okamura Kozue, Badr Sara, Sugiyama Hirokazu

2021-Aug-12

Public Health Public Health

Predictive modeling of COVID-19 case growth highlights evolving racial and ethnic risk factors in Tennessee and Georgia.

In BMJ health & care informatics

INTRODUCTION : The SARS-CoV-2 (COVID-19) pandemic has exposed the need to understand the risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health (SDOH) that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections.

METHODS : Our work combined publicly available COVID-19 statistics with county-level SDOH information. Machine learning models were trained to predict COVID-19 case growth and understand the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county.

RESULTS : The predictive models achieved a mean R2 of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the importance of SDOH data features over time to uncover the specific racial demographic characteristics strongly associated with COVID-19 incidence in Tennessee and Georgia counties. Our results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. For example, we find that African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality.

CONCLUSION : The dichotomy of demographic trends presented here emphasizes the importance of understanding the unique factors that influence COVID-19 incidence. Identifying these specific risk factors tied to COVID-19 case growth can help stakeholders target regional interventions to mitigate the burden of future outbreaks.

Gray Jamieson D, Harris Coleman R, Wylezinski Lukasz S, Spurlock Iii Charles F

2021-Aug

BMJ Health Informatics, COVID-19, computing methodologies

Pathology Pathology

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Tracking.

In Clinical chemistry ; h5-index 61.0

BACKGROUND : High sensitivity SARS-CoV-2 antigen assays are desirable to mitigate false negative results. Limited data are available to quantify and track SARS-CoV-2 antigen burden in respiratory samples from different populations.

METHODS : We developed the Microbubbling SARS-CoV-2 Antigen Assay (MSAA) with smartphone readout, with a limit of detection (LOD) of 0.5 pg/mL (10.6 fmol/L) nucleocapsid (N) antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs. We developed a computer vision and machine learning-based automatic microbubble image classifier to accurately identify positives and negatives, and quantified and tracked antigen dynamics in ICU COVID inpatients and immunocompromised COVID patients.

RESULTS : Compared to qualitative RT-PCR methods, the MSAA demonstrated a positive percent agreement (PPA) of 97% (95% confidence interval (CI), 92-99%) and a negative percent agreement (NPA) of 97% (95% CI, 94-100%) in a clinical validation study with 372 residual clinical NP swabs. In immunocompetent individuals, the antigen positivity rate in swabs decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity. Antigen was detected for longer and variable periods of time in immunocompromised patients with hematologic malignancies. Total microbubble volume, a quantitative marker of antigen burden, correlated inversely with Ct values and days-after-symptom-onset. Viral sequence variations were detected in patients with long duration of high antigen burden.

CONCLUSIONS : The MSAA enables sensitive and specific detection of acute infections, quantification and tracking of antigen burden, and may serve as a screening method in longitudinal studies to identify patients who are likely experiencing active rounds of ongoing replication and warrant close viral sequence monitoring.

Chen Hui, Li Zhao, Feng Sheng, Richard-Greenblatt Melissa, Hutson Emily, Andrianus Stefen, Glaser Laurel J, Rodino Kyle G, Qian Jianing, Jayaraman Dinesh, Collman Ronald G, Glascock Abigail, Bushman Frederic D, Lee Jae Seung, Cherry Sara, Fausto Alejandra, Weiss Susan R, Koo Hyun, Corby Patricia M, Oceguera Alfonso, O’Doherty Una, Garfall Alfred L, Vogl Dan T, Stadtmauer Edward A, Wang Ping

2021-Aug-12

SARS-CoV-2, longitudinal NP swab samples, microbubbling assay, viral antigen

General General

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding.

In Journal of chemical theory and computation

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

Uyar Arzu, Dickson Alex

2021-Aug-12

General General

A bagging dynamic deep learning network for diagnosing COVID-19.

In Scientific reports ; h5-index 158.0

COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.

Zhang Zhijun, Chen Bozhao, Sun Jiansheng, Luo Yamei

2021-Aug-11

General General

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.

In IEEE journal of biomedical and health informatics

SARS-CoV-2 has infected over ~165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ~3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of mean score vs. study and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

Suri Jasjit, Agarwal Sushant, Gupta Suneet K, Puvvula Anudeep, Viskovic Klaudija, Suri Neha, Alizad Azra, El-Baz Ayman, Saba Luca, Fatemi Mostafa, Naidu D Subbaram

2021-Aug-11

Ophthalmology Ophthalmology

COVID 19 repercussions in ophthalmology: a narrative review.

In Sao Paulo medical journal = Revista paulista de medicina

BACKGROUND : The new coronavirus of 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally and has repercussions within ophthalmological care. It has caused ocular manifestations in some patients, which can spread through eye secretions.

OBJECTIVES : The purpose of this review was to summarize the currently available evidence on COVID-19 with regard to its implications for ophthalmology.

DESIGN AND SETTING : Narrative review developed by a research group at Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil, and at Ludwig-Maximilians-Universität, Munich, Germany.

METHODS : We searched the literature on the repercussions of COVID-19 within ophthalmological care, using the MEDLINE and LILACS databases, with the keywords "COVID-19", "ophthalmology" and "coronavirus", from January 1, 2020, to March 27, 2021. Clinical trials, meta-analysis, randomized controlled trials, reviews and systematic reviews were identified.

RESULTS : We retrieved 884 references, of which 42 were considered eligible for intensive review and critical analysis. Most of the studies selected reported the evidence regarding COVID-19 and its implications for ophthalmology.

CONCLUSIONS : Knowledge of eye symptoms and ocular transmission of the virus remains incomplete. New clinical trials with larger numbers of patients may answer these questions in the future. Moreover, positively, implementation of innovative changes in medicine such as telemedicine and artificial intelligence may assist in diagnosing eye diseases and in training and education for students.

Dos Santos Martins Thiago Gonçalves, Dos Santos Martins Diogo Gonçalves, Dos Santos Martins Thomaz Gonçalves, Marinho Paula, Schor Paulo

General General

Analyzing COVID-19 Using Multisource Data: An Integrated Approach of Visualization, Spatial Regression, and Machine Learning.

In GeoHealth

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, was first identified in Wuhan, China, in December 2019. As the number of COVID-19 infections and deaths worldwide continues to increase rapidly, the prevention and control of COVID-19 remains urgent. This article aims to analyze COVID-19 from a geographical perspective, and this information can provide useful insights for rapid visualization of spatial-temporal epidemic information and identification of the factors important to the spread of COVID-19. A new type of vitalization method, called the point grid map, is integrated with calendar-based visualization to show the spatial-temporal variations in COVID-19. The combination of mixed geographically weighted regression (mixed GWR) and extreme gradient boosting (XGBoost) is used to identify the potential factors and the corresponding importance. The visualization results clearly reflect the spatial-temporal patterns of COVID-19. The quantified results reveal that the impact of population outflow from Wuhan is the most important factor and indicate statistically significant spatial heterogeneity. Our results provide insights into how multisource big geodata can be employed within the framework of integrating visualization and analytical methods to characterize COVID-19 trends. In addition, this work can help understand the influential factors for controlling and preventing epidemics, which is important for policy design and effective decision-making for controlling COVID-19. The results reveal that one of the most effective ways to control COVID-19 include controlling the source of infection, cutting off the transmission route, and protecting vulnerable groups.

Wu Chao, Zhou Mengjie, Liu Pengyu, Yang Mengjie

2021-Aug

COVID‐19, XGBoost, geographical perspective, mixed GWR, spatial‐temporal patterns, visualization

General General

Correlating Dynamic Climate Conditions and Socioeconomic-Governmental Factors to Spatiotemporal Spread of COVID-19 via Semantic Segmentation Deep Learning Analysis.

In Sustainable cities and society

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface day temperature (LSDT) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

Chew Alvin Wei Ze, Wang Ying, Zhang Limao

2021-Aug-05

COVID-19, climate conditions, deep learning, satellite images, semantic segmentation analysis, socioeconomic-governmental nexus

General General

How ICT can contribute to realize a sustainable society in the future: a CGE approach.

In Environment, development and sustainability

Many information and communications technology (ICT) services have become commonplace worldwide and are certain to continue to spread faster than before, particularly along with the commercialization of 5G and movement restrictions in response to the COVID-19 Pandemic. Although there is a concern that ICT equipment usage may increase power consumption and emit greenhouse gas (GHG) emissions, ICT has also been contributing to reducing GHG emissions through improved productivity and reduced mobility. This research targeted the main ICT services used in Japan and adopted a dynamic national computable general equilibrium model to quantitatively analyze future impacts on economic growth and GHG emission reduction until 2030 by using these ICTs, while considering both the increase in power consumption of ICT itself and the reduction in environmental load in other sectors. The results showed that the spread of ICT services, especially some artificial intelligence-based services, can improve productivity in most sectors through labor-saving and contribute to improving overall gross domestic product (GDP). Additionally, increased efficiency of logistics and manufacturing can greatly reduce the input of oil and coal products and so greatly contribute to GHG emission reduction. In 2030, compared with the baseline scenario in which all technology levels are fixed at current levels, at least 1% additional GDP growth and 4% GHG emission reduction can be expected by the targeted introduction of ICT in the ICT accelerated scenario in which the technology level of ICT accelerates. This also means ICT can potentially decouple the economy from the environment.

Zhang Xiaoxi, Shinozuka Machiko, Tanaka Yuriko, Kanamori Yuko, Masui Toshihiko

2021-Aug-05

CGE model, Digital technologies, Environmental impacts, GDP, GHG emissions, ICT, Scenarios

General General

C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT Images based on CBAM and EfficientNet.

In Chaos, solitons, and fractals

Covid-19, one of the biggest diseases of our age, continues to spread rapidly around the world. Studies continue rapidly for the diagnosis and treatment of this disease. It is of great importance that individuals who are infected with this virus be isolated from the rest of the society so that the disease does not spread further. In addition to the tests performed in the detection process of the patients, X-ray and computed tomography are also used. In this study, a new hybrid model that can diagnose Covid-19 from computed tomography images created using EfficientNet, one of the current deep learning models, with a model consisting of attention blocks is proposed. In the first step of this new model, channel attention, spatial attention, and residual blocks are used to extract the most important features from the images. The extracted features are combined in accordance with the hyper-column technique. The combined features are given as input to the EfficientNet models in the second step of the model. The deep features obtained from this proposed hybrid model were classified with the Support Vector Machine classifier after feature selection. Principal Components Analysis was used for feature selection. The approach can accurately predict Covid-19 with a 99% accuracy rate. The first four versions of EfficientNet are used in the approach. In addition, Bayesian optimization was used in the hyper parameter estimation of the Support Vector Machine classifier. Comparative performance analysis of the approach with other approaches in the field is given.

Canayaz Murat

2021-Aug-05

General General

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective.

In Journal of intelligent manufacturing

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

Kang SungKu, Jin Ran, Deng Xinwei, Kenett Ron S

2021-Aug-04

Computation pipelines, Cybermanufacturing, Industry 4.0, Machine learning, Manufacturing modeling and analysis

Public Health Public Health

Chloe for COVID-19: The Evolution of an Intelligent Conversational Agent to Address Infodemic Management Needs During the COVID-19 Pandemic.

In Journal of medical Internet research ; h5-index 88.0

There is an unprecedented demand for infodemic management due to rapidly evolving information about the novel COVID-19 pandemic. This viewpoint paper details the evolution of a Canadian digital information tool, Chloe for COVID-19, based on incremental leveraging of artificial intelligence techniques. By providing an accessible summary of Chloe's development, we show how proactive cooperation between health, technology, and corporate sectors can lead to a rapidly scalable, safe, and secure virtual chatbot to assist public health efforts in keeping Canadians informed. We then highlight Chloe's strengths, the challenges we faced during the development process, and future directions for the role of chatbots in infodemic management. The information presented here may guide future collaborative efforts in health technology in order to enhance access to accurate and timely health information to the public.

Siedlikowski Sophia, Noël Louis-Philippe, Moynihan Stephanie Anne, Robin Marc

2021-Jul-10

Pathology Pathology

Information retrieval in an infodemic: the case of COVID-19 publications.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The coronavirus disease (COVID-19) global health crisis has led to an exponential surge in the published scientific literature. In the attempt to tackle the pandemic, extremely large COVID-19-related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses.

OBJECTIVE : In the context of searching for scientific evidence in the deluge of COVID-19-related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language.

METHODS : Our multi-stage retrieval methodology combines probabilistic weighting models and re-ranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries are compared to documents and a series of post-processing methods are applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents.

RESULTS : The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed a BM25-based baseline, retrieving on average 83% of relevant documents in the top 20.

CONCLUSIONS : These results indicate that multi-stage retrieval supported by deep learning could enhance identification of literature for COVID-19-related questions posed using natural language.

CLINICALTRIAL :

Teodoro Douglas, Ferdowsi Sohrab, Borissov Nikolay, Kashani Elham, Vicente Alvarez David, Copara Jenny, Gouareb Racha, Naderi Nona, Amini Poorya

2021-Aug-05

General General

Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity.

In bioRxiv : the preprint server for biology

Antibody responses serve as the primary protection against SARS-CoV-2 infection through neutralization of viral entry into cells. We have developed a two-dimensional multiplex bead binding assay (2D-MBBA) that quantifies multiple antibody isotypes against multiple antigens from a single measurement. Here, we applied our assay to profile IgG, IgM and IgA levels against the spike antigen, its receptor-binding domain and natural and designed mutants. Machine learning algorithms trained on the 2D-MBBA data substantially improve the prediction of neutralization capacity against the authentic SARS-CoV-2 virus of serum samples of convalescent patients. The algorithms also helped identify a set of antibody isotypeâ€"antigen datasets that contributed to the prediction, which included those targeting regions outside the receptor-binding interface of the spike protein. We applied the assay to profile samples from vaccinated, immune-compromised patients, which revealed differences in the antibody profiles between convalescent and vaccinated samples. Our approach can rapidly provide deep antibody profiles and neutralization prediction from essentially a drop of blood without the need of BSL-3 access and provides insights into the nature of neutralizing antibodies. It may be further developed for evaluating neutralizing capacity for new variants and future pathogens.

Koide Akiko, Panchenko Tatyana, Wang Chan, Thannickal Sara A, Romero Larizbeth A, Teng Kai Wen, Li Francesca-Zhoufan, Akkappedi Padma, Corrado Alexis D, Caro Jessica, Diefenbach Catherine, Samanovic Marie I, Mulligan Mark J, Hattori Takamitsu, Stapleford Kenneth A, Li Huilin, Koide Shohei

2021-Aug-04

General General

Novel Face Mask Detection Technique using Machine Learning to control COVID'19 pandemic.

In Materials today. Proceedings

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of antiviral drugs is one of the reasons for the spreading of COVID-19 virus. This disease is spreading continuously and easily due to some common mistakes by people, like breathing, coughing and sneezing by infected persons. The main symptom is the normal flu. Therefore, in the present condition, the best precaution for this disease is the face mask, which covers both areas of mouth & nose. According to the government and the World Health Organization, everyone should wear a face mask in busy places like hospitals and marketplaces. In today's environment, it's difficult to tell if someone is wearing a mask or not, and physical inspection is impractical since it adds to labour costs. In this research, we present a mask detector that uses a machine learning facial categorization system to determine whether a person is wearing a mask or not, so that it may be connected to a CCTV system to verify that only persons wearing masks are allowed in.

Gupta Sandeep, Sreenivasu S V N, Chouhan Kuldeep, Shrivastava Anurag, Sahu Bharti, Manohar Potdar Ravindra

2021-Aug-04

AlexNet, COVID’19, Corona virus, Deep learning, Neural network, Sensor

Radiology Radiology

Validating deep learning inference during chest X-ray classification for COVID-19 screening.

In Scientific reports ; h5-index 158.0

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

Sadre Robbie, Sundaram Baskaran, Majumdar Sharmila, Ushizima Daniela

2021-Aug-09

General General

Learnings from the design and acceptance of the German COVID-19 tracing app for IS-driven crisis management: a design science research.

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

BACKGROUND : This article investigates the research problem of digital solutions to overcome the pandemic, more closely examining the limited effectiveness and scope of the governmental COVID-19 tracing apps, using the German COVID-19 tracing app (Corona-Warn-App) as an example. A well-designed and effective instrument in the technological toolbox is of utmost importance to overcome the pandemic.

METHOD : A multi-methodological design science research approach was applied. In three development and evaluation cycles, we presented, prototyped, and tested user-centered ideas of functional and design improvement. The applied procedure contains (1) a survey featuring 1993 participants from Germany for evaluating the current app, (2) a gathering of recommendations from epidemiologists and from a focus group discussion with IT and health experts identifying relevant functional requirements, and (3) an online survey combined with testing our prototype with 53 participants to evaluate the enhanced tracing app.

RESULTS : This contribution presents 14 identified issues of the German COVID-19 tracing app, six meta-requirements, and three design principles for COVID-19 tracing apps and future pandemic apps (e.g., more user involvement and transparency). Using an interactive prototype, this study presents an extended pandemic app, containing 13 potential front-end (i.e., information on the regional infection situation, education and health literacy, crowd and event notification) and six potential back-end functional requirements (i.e., ongoing modification of risk score calculation, indoor versus outdoor). In addition, a user story approach for the COVID-19 tracing app was derived from the findings, supporting a holistic development approach.

CONCLUSION : Throughout this study, practical relevant findings can be directly transferred to the German and other international COVID-19 tracing applications. Moreover, we apply our findings to crisis management theory-particularly pandemic-related apps-and derive interdisciplinary learnings. It might be recommendable for the involved decision-makers and stakeholders to forego classic application management and switch to using an agile setup, which allows for a more flexible reaction to upcoming changes. It is even more important for governments to have a well-established, flexible, design-oriented process for creating and adapting technology to handle a crisis, as this pandemic will not be the last one.

Behne Alina, Krüger Nicolai, Beinke Jan Heinrich, Teuteberg Frank

2021-Aug-09

Corona-Warn-App, Crisis management, Design science, Prototype, Tracing apps, User experience design

General General

Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic.

In Journal of big data

As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.

Grybauskas Andrius, Pilinkienė Vaida, Stundžienė Alina

2021

Apartments, Big data, Machine learning, Pandemics, Real estate, TOM

Radiology Radiology

A Novel Weighted Consensus Machine Learning Model for COVID-19 Infection Classification Using CT Scan Images.

In Arabian journal for science and engineering

As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives.

Bondugula Rohit Kumar, Udgata Siba K, Bommi Nitin Sai

2021-Aug-02

COVID-19, Chest CT scan, Machine learning, Weighted consensus model

General General

Nature inspired optimization model for classification and severity prediction in COVID-19 clinical dataset.

In Journal of ambient intelligence and humanized computing

The spread rate of COVID-19 is expected to be high in the wake of the virus's mutated strain found recently in a few countries. Fast diagnosis of the disease and knowing its severity are the two significant concerns of all physicians. Even though positive or negative diagnosis can be obtained through the RT-PCR test, an automatic model that predicts severity and the diagnosis will help medical practitioners to a great extend for affirming medication. Machine learning is an efficient tool that can process vast volume of data deposited in various formats, including clinical symptoms. In this work, we have developed machine learning models for analysing a clinical data set comprising 65000 records of patients, consisting of 26 features. An optimum set of features was derived from this data set by the proposed variant of artificial bee colony optimization algorithm. By making use of these features, a binary classifier is modelled with support vector machine for the screening of COVID-19 patients. Different models were tested for this purpose and the support vector machine has showcased the highest accuracy of 96%. Successively, severity prediction in COVID positive patients was also performed successfully by the logistic regression model. The model managed to predict three severity status viz mild, moderate, and severe. The confusion matrix and the precision-recall values (0.96 and 0.97) of the binary classifier indicate the classifier's efficiency in predicting positive cases correctly. The receiver operating curve generated for the severity predicting model shows the highest accuracy, 96.0% for class 1 and 85.0% for class 2 patients. Doctors can infer these results to finalize the type of treatment/care/facilities that need to be given to the patients from time to time.

Suma L S, Anand H S, Vinod Chandra S S

2021-Jul-31

Artificial bee colony optimization, COVID-19, Logistic regression, Severity prediction, Support vector machine

General General

An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2.

In Frontiers in microbiology

Because of the catastrophic outbreak of global coronavirus disease 2019 (COVID-19) and its strong infectivity and possible persistence, computational repurposing of existing approved drugs will be a promising strategy that facilitates rapid clinical treatment decisions and provides reasonable justification for subsequent clinical trials and regulatory reviews. Since the effects of a small number of conditionally marketed vaccines need further clinical observation, there is still an urgent need to quickly and effectively repurpose potentially available drugs before the next disease peak. In this work, we have manually collected a set of experimentally confirmed virus-drug associations through the publicly published database and literature, consisting of 175 drugs and 95 viruses, as well as 933 virus-drug associations. Then, because the samples are extremely sparse and unbalanced, negative samples cannot be easily obtained. We have developed an ensemble model, EMC-Voting, based on matrix completion and weighted soft voting, a semi-supervised machine learning model for computational drug repurposing. Finally, we have evaluated the prediction performance of EMC-Voting by fivefold crossing-validation and compared it with other baseline classifiers and prediction models. The case study for the virus SARS-COV-2 included in the dataset demonstrates that our model achieves the outperforming AUPR value of 0.934 in virus-drug association's prediction.

Li Wen, Wang Shulin, Xu Junlin

2021

SARS-CoV-2, computational drug repurposing, matrix completion, virus-drug association prediction, weighted voting ensemble model

General General

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic.

In Frontiers in psychology ; h5-index 92.0

Background : Especially in the current crisis of the COVID-19 pandemic and the lockdown it entailed, technology became crucial. Machines need to be able to interpret and represent human behavior, to improve human interaction with technology. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being.

Methods : This work presents the theoretical framework of a psychologically driven computing ontology, CCOnto, describing situation-based human behavior in relation to psychological states and traits. In this manuscript, we use and apply CCOnto as a theoretical and formal description system to categorize psychological factors that influence student behavior during the COVID-19 situation. By doing so, we show the added value of ontologies, i.e., their ability to automatically organize information from unstructured human data by identifying and categorizing relevant psychological concepts.

Results : The already existing CCOnto was modified to automatically categorize university students' state and trait markers related to different aspects of student behavior, including learning, worrying, health, and socially based on psychological theorizing and psychological data conceptualization.

Discussion : The paper discusses the potential advantages of using ontologies for describing and modeling psychological research questions. The handling of dataset completion, unification, and its explanation by means of Artificial Intelligence and Machine Learning models is also discussed.

Bolock Alia El, Abdennadher Slim, Herbert Cornelia

2021

COVID 19, character computing, education, emotion, mental health, personality, psychological ontologies

General General

Securing Your Relationship: Quality of Intimate Relationships During the COVID-19 Pandemic Can Be Predicted by Attachment Style.

In Frontiers in psychology ; h5-index 92.0

The COVID-19 pandemic along with the restrictions that were introduced within Europe starting in spring 2020 allows for the identification of predictors for relationship quality during unstable and stressful times. The present study began as strict measures were enforced in response to the rising spread of the COVID-19 virus within Austria, Poland, Spain and Czech Republic. Here, we investigated quality of romantic relationships among 313 participants as movement restrictions were implemented and subsequently phased out cross-nationally. Participants completed self-report questionnaires over a period of 7 weeks, where we predicted relationship quality and change in relationship quality using machine learning models that included a variety of potential predictors related to psychological, demographic and environmental variables. On average, our machine learning models predicted 29% (linear models) and 22% (non-linear models) of the variance with regard to relationship quality. Here, the most important predictors consisted of attachment style (anxious attachment being more influential than avoidant), age, and number of conflicts within the relationship. Interestingly, environmental factors such as the local severity of the pandemic did not exert a measurable influence with respect to predicting relationship quality. As opposed to overall relationship quality, the change in relationship quality during lockdown restrictions could not be predicted accurately by our machine learning models when utilizing our selected features. In conclusion, we demonstrate cross-culturally that attachment security is a major predictor of relationship quality during COVID-19 lockdown restrictions, whereas fear, pathogenic threat, sexual behavior, and the severity of governmental regulations did not significantly influence the accuracy of prediction.

Eder Stephanie J, Nicholson Andrew A, Stefanczyk Michal M, Pieniak Michał, Martínez-Molina Judit, Pešout Ondra, Binter Jakub, Smela Patrick, Scharnowski Frank, Steyrl David

2021

COVID-19, attachment style, intimate relationships, machine learning, pair bond, relationship quality

General General

Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic.

In Process biochemistry (Barking, London, England)

Artificial intelligence (AI), a method of simulating the human brain in order to complete tasks in a more effective manner, has had numerous implementations in fields from manufacturing sectors to digital electronics. Despite the potential of AI, it may be obstinate to assume that the person administered society would rely solely on AI; with an example being the healthcare field. With the ever-expanding discoveries made on a regular basis regarding the growth of various diseases and its preservations, utilizing brain power may be deemed essential, but that doesn't leave AI as a redundant asset. With the years of accumulated data regarding patterns and the analysis of various medical circumstances, algorithms can be formed, which could further assist in situations such as diagnosis support and population health management. This matter becomes even more relevant in today's society with the currently ongoing COVID-19 pandemic by SARS-CoV-2. With the uncertainty of this pandemic from strain variants to the rolling speeds of vaccines, AI could be utilized to our advantage in order to assist us with the fight against COVID-19. This review briefly discusses the application of AI in the COVID-19 situation for various health benefits.

Gopinath Nishanth

2021-Nov

Artificial intelligence, Coronavirus, Deep learning, Epidemic, Machine learning

General General

Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images.

In Expert systems with applications

The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 (i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.

Morís Daniel Iglesias, de Moura Ramos José Joaquim, Buján Jorge Novo, Hortas Marcos Ortega

2021-Dec-15

COVID-19, CycleGAN, Data augmentation, Deep learning, Screening, X-ray portable device

General General

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing.

In Expert systems with applications

This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase-Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model's practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.

Verma Amar Kumar, Vamsi Inturi, Saurabh Prerna, Sudha Radhika, G R Sabareesh, S Rajkumar

2021-Dec-15

COVID-19, Medical imaging, Transfer learning, Wavelets, rRT-PCR

General General

Analyzing Effects of The COVID-19 Pandemic on Road Traffic Safety: The Cases of New York City, Los Angeles, and Boston

ArXiv Preprint

The COVID-19 pandemic has resulted in significant social and economic impacts throughout the world. In addition to the health consequences, the impacts on traffic behaviors have also been sudden and dramatic. We have analyzed how the road traffic safety of New York City, Los Angeles, and Boston in the U.S. have been impacted by the pandemic and corresponding local government orders and restrictions. To be specific, we have studied the accident hotspots' distributions before and after the outbreak of the pandemic and found that traffic accidents have shifted in both location and time compared to previous years. In addition, we have studied the road network characteristics in those hotspot regions with the hope to understand the underlying cause of the hotspot shifts.

Lahari Karadla, Weizi Li

2021-08-10

General General

Helicobacter pylori - 2021.

In Orvosi hetilap

Összefoglaló. A Helicobacter pylori továbbra is a világ legelterjedtebb fertőzése: prevalenciája a fejlődő országokban 70-80%, a fejlett országokban csökkenő tendenciát mutat. A dél-magyarországi véradókban a prevalencia 32%-ra csökkent. A migráció a befogadó ország számára a fertőzés fokozott kockázatával jár. A szövettani diagnózisban az immunhisztokémiai vizsgálat pontosabb a hagyományos Giemsa-festésnél. A mesterséges intelligencia érzékenysége a hagyományos endoszkópiáéval összehasonlítva 87%, pontossága 86%. Az újgenerációs szekvenálással lehetséges egy biopsziás mintából több antibiotikumérzékenység meghatározása. A Helicobacter pylori kezelésének európai regisztere kimutatta, hogy 2013 és 2018 között a bizmutalapú négyes vagy a 14 napos egyidejű négyes kezelések hatásosabbak, mint a hagyományos hármas kezelés, de elterjedésük igen lassú folyamat, jelentős földrajzi különbségekkel. Az új típusú koronavírus (SARS-CoV-2) felléphet Helicobacter pylori fertőzésben is, egymás kóros hatását felerősítve. A diagnosztikai módszerek korlátozottak. Protonpumpagátlók szedése növeli a COVID-19-fertőzés kockázatát és annak súlyos kimenetelét. Előzetesen ismert peptikus fekély, vérzés, illetve antikoguláns kezelés előtt az eradikáció a vírusos fertőzés lezajlása után indokolt. A probiotikumoknak az eradikációra gyakorolt hatásáról 20, közepes minőségű metaanalízis született, így a konszenzusokban foglalt álláspontok sem egyértelműek: a jövőben ezt tisztázni kell. Orv Hetil. 2021; 162(32): 1275-1282. Summary. Helicobacter pylori is still the most widespread infection in the world: its overall prevalence is 70-80% in developing regions, but fortunately it is decreasing in the Western world. The prevalence in blood donors from South-Eastern Hungary decreased from 63% in the 1990's to 32% in 2019. Migration constitutes an increased risk of infection for the destination countries. Immunohistochemistry has proven to be more accurate in histological diagnosis than the conventional Giemsa stain. The sensitivity and accuracy of artificial intelligence as compared to videoendoscopy were 87% and 86%, respectively. The European Register on the management of Helicobacter pylori infection revealed that concomitant quadruple and 14-day bismuth-based therapies are more efficient than triple combinations, although their incorporation in practice is a long-lasting process, with large geographical variations. The novel type of coronavirus (SARS-CoV-2) can also occur in Helicobacter pylori-infected patients, mutually enhancing their pathogenetic effects. Diagnostic possibilities are limited in this setting. The use of proton pump inhibitors increases the risk of viral infection and the severity of the disease. Eradication treatment seems justified in patients with previously known peptic ulcers or gastrointestinal bleeding, or before starting anticoagulant treatment, but must be postponed after resolution of viral infection. The effect of probiotics on eradication was addressed by 20, medium-to-low quality meta-analyses and so, the recommendations of the guidelines are equivocal, which must be clarified in the future with higher quality studies. Orv Hetil. 2021; 162(32): 1275-1282.

Buzás György Miklós

2021-Aug-08

European Registry on Helicobacter pylori management, Európai Helicobacter pylori kezelési regiszter, Helicobacter pylori, SARS-CoV-2, antibiotics, eradication, eradikáció, probiotic, probiotikum, proton pump inhibitors, protonpumpagátlók

General General

A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe.

In Briefings in bioinformatics

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here, we propose a novel framework, named AIMEE, integrating AI model and enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2), which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value <3 μM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE.

Hu Fan, Wang Lei, Hu Yishen, Wang Dongqi, Wang Weijie, Jiang Jianbing, Li Nan, Yin Peng

2021-Aug-09

SARS-CoV-2 3CL inhibitors, deep learning, drug discovery, model interpretation

General General

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review.

In Frontiers in artificial intelligence

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

Deng Hanqiu, Li Xingyu

2021

COVID-19, ROI segmentation, chest imaging, diagnostic model, image analysis, machine learning, prognosis prediction, severity assessment

Radiology Radiology

Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19.

In Informatics in medicine unlocked

There is a crucial need for quick testing and diagnosis of patients during the COVID-19 pandemic. Lung ultrasound is an imaging modality that is cost-effective, widely accessible, and can be used to diagnose acute respiratory distress syndrome in patients with COVID-19. It can be used to find important characteristics in the images, including A-lines, B-lines, consolidation, and pleural effusion, which all inform the clinician in monitoring and diagnosing the disease. With the use of portable ultrasound transducers, lung ultrasound images can be easily acquired, however, the images are often of poor quality. They often require an expert clinician interpretation, which may be time-consuming and is highly subjective. We propose a method for fast and reliable interpretation of lung ultrasound images by use of deep learning, based on the Kinetics-I3D network. Our learned model can classify an entire lung ultrasound scan obtained at point-of-care, without requiring the use of preprocessing or a frame-by-frame analysis. We compare our video classifier against ground truth classification annotations provided by a set of expert radiologists and clinicians, which include A-lines, B-lines, consolidation, and pleural effusion. Our classification method achieves an accuracy of 90% and an average precision score of 95% with the use of 5-fold cross-validation. The results indicate the potential use of automated analysis of portable lung ultrasound images to assist clinicians in screening and diagnosing patients.

Erfanian Ebadi Salehe, Krishnaswamy Deepa, Bolouri Seyed Ehsan Seyed, Zonoobi Dornoosh, Greiner Russell, Meuser-Herr Nathaniel, Jaremko Jacob L, Kapur Jeevesh, Noga Michelle, Punithakumar Kumaradevan

2021

COVID-19, Convolutional neural networks, Lung ultrasound, Video classification

General General

Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach.

In Virusdisease

COVID-19 outbreak is the recently reported worldwide pandemic threat. As part of our interventions with machine learning and molecular simulation approaches, we report the inhibitory effect of thirty compounds reported from the sacred plant Aerva lanata. The predicted activity of the screened ligands are comparable with the one of the present medication, hydroxy chloroquine (HCQ), on the main protease (PDB:6YB7) of SARS-CoV-2. Our studies pointed out the effectiveness of the plant with twenty seven compounds having potential activity against the main protease compared to the reference HCQ. The robustness of some of the phytochemicals such as ervoside, which is only present in Aerva lanata computed to have very high anticoronavirus activity. The results are indicative of potential natural antivirus source, which subsidizes in thwarting the invasion of coronavirus into the human body. Many phytochemicals which are computed to be effective towards SARS-CoV-2 in this study are used as drugs for various other diseases. Perhaps these compounds could be attractive for the management of COVID-19, but clinical trials must be performed in order to validate this observation.

Sherin D R, Sharanya N, Manojkumar T K

2021-Jul-31

Aerva lanata, Docking, Machine learning, SARS-CoV-2

General General

PERSONALIZED STRATIFICATION OF HOSPITALIZATION RISK AMIDST COVID-19: A MACHINE LEARNING APPROACH.

In Health policy and technology

Objective: In the wake of COVID-19, the United States developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans deemed to be at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness, and may therefore be of limited use in decisions surrounding resource allocation to vulnerable populations. The objective of this study was to evaluate a machine learning algorithm for prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. Methods: The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S. policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. Results: This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% as identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. Conclusions: This algorithm may identify individuals likely to require hospitalization should they contract COVID-19. This information may be useful in guiding vaccine distribution, in anticipating hospital resource needs, and in assisting health care policymakers to make care decisions in a more principled manner.

Lam Carson, Calvert Jacob, Siefkas Anna, Barnes Gina, Pellegrini Emily, Green-Saxena Abigail, Hoffman Jana, Mao Qingqing, Das Ritankar

2021-Aug-04

COVID-19, algorithm, machine learning, prediction

General General

Earables for Detection of Bruxism: a Feasibility Study

ArXiv Preprint

Bruxism is a disorder characterised by teeth grinding and clenching, and many bruxism sufferers are not aware of this disorder until their dental health professional notices permanent teeth wear. Stress and anxiety are often listed among contributing factors impacting bruxism exacerbation, which may explain why the COVID-19 pandemic gave rise to a bruxism epidemic. It is essential to develop tools allowing for the early diagnosis of bruxism in an unobtrusive manner. This work explores the feasibility of detecting bruxism-related events using earables in a mimicked in-the-wild setting. Using inertial measurement unit for data collection, we utilise traditional machine learning for teeth grinding and clenching detection. We observe superior performance of models based on gyroscope data, achieving an 88% and 66% accuracy on grinding and clenching activities, respectively, in a controlled environment, and 76% and 73% on grinding and clenching, respectively, in an in-the-wild environment.

Erika Bondareva, Elín Rós Hauksdóttir, Cecilia Mascolo

2021-08-09

General General

CoLe-CNN+: Context learning - Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation.

In Computers in biology and medicine

BACKGROUND AND OBJECTIVE : The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19.

METHODS : In the workflow proposed, the input CT image initially goes through lung delineation, then COVID-19 detection and finally lesion segmentation. The chosen neural network has a U-shaped architecture using a newly introduced Multiple Convolutional Layers structure, that produces a lung segmentation mask within a novel pipeline for direct COVID-19 detection and segmentation. In addition, we propose a customized loss function that guarantees an optimal balance on average between sensitivity and precision.

RESULTS : Lungs' segmentation results show a sensitivity near 99% and Dice-score of 97%. No false positives were observed in the detection network after 10 different runs with an average accuracy of 97.1%. The average accuracy for lesion segmentation was approximately 99%. Using UNet as a benchmark, we compared our results with several other techniques proposed in the literature, obtaining the largest improvement over the UNet outcomes.

CONCLUSIONS : The method proposed in this paper outperformed the state-of-the-art methods for COVID-19 lesion segmentation from CT images, and improved by 38.2% the results for F1-score of UNet. The high accuracy observed in this work opens up a wide range of possible applications of our algorithm in other fields related to medical image segmentation.

Pezzano Giuseppe, Díaz Oliver, Ripoll Vicent Ribas, Radeva Petia

2021-Jul-31

COVID-19, Convolutional neural network, Detection, SARS-CoV-2, Segmentation

General General

[Application of AI Technology in Diagnosis and Treatment of COVID-19].

In Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation

OBJECTIVE : To analyze the role of artificial intelligence technology in the diagnosis and treatment of the COVID-19.

METHODS : To study the application progress and characteristics of artificial intelligence technology in CT image diagnosis, routine outpatient data diagnosis and complication prediction of COVID-19, and analyze the performance of the algorithm and the clinical benefits obtained in the process of diagnosis and treatment.

RESULTS : The performance of artificial intelligence technology in assisted diagnosis of the diagnosis and prediction of complications is relatively satisfactory.

CONCLUSIONS : Artificial intelligence technology can help medical institutions effectively alleviate the shortage of medical resources, improve diagnosis efficiency and treatment quality in the COVID-19 epidemic. Related models have good clinical application value.

Zhang Chenguang

2021-Jul-30

COVID-19, artificial intelligence, assistant diagnosis

General General

Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants.

In Science advances

The COVID-19 pandemic highlights the need for diagnostics that can be rapidly adapted and deployed in a variety of settings. Several SARS-CoV-2 variants have shown worrisome effects on vaccine and treatment efficacy, but no current point-of-care (POC) testing modality allows their specific identification. We have developed miSHERLOCK, a low-cost, CRISPR-based POC diagnostic platform that takes unprocessed patient saliva; extracts, purifies, and concentrates viral RNA; performs amplification and detection reactions; and provides fluorescent visual output with only three user actions and 1 hour from sample input to answer out. miSHERLOCK achieves highly sensitive multiplexed detection of SARS-CoV-2 and mutations associated with variants B.1.1.7, B.1.351, and P.1. Our modular system enables easy exchange of assays to address diverse user needs and can be rapidly reconfigured to detect different viruses and variants of concern. An adjunctive smartphone application enables output quantification, automated interpretation, and the possibility of remote, distributed result reporting.

de Puig Helena, Lee Rose A, Najjar Devora, Tan Xiao, Soekensen Luis R, Angenent-Mari Nicolaas M, Donghia Nina M, Weckman Nicole E, Ory Audrey, Ng Carlos F, Nguyen Peter Q, Mao Angelo S, Ferrante Thomas C, Lansberry Geoffrey, Sallum Hani, Niemi James, Collins James J

2021-Aug

Public Health Public Health

Correlation between the Level of Social Distancing and Activity of Influenza Epidemic or COVID-19 Pandemic: A Subway Use-Based Assessment.

In Journal of clinical medicine

Social distancing is an effective measure to mitigate the spread of novel viral infections in the absence of antiviral agents and insufficient vaccine supplies. Subway utilization density may reflect social activity and the degree of social distancing in the general population.; This study aimed to evaluate the correlations between subway use density and the activity of the influenza epidemic or coronavirus disease 2019 (COVID-19) pandemic using a time-series regression method. The subway use-based social distancing score (S-SDS) was calculated using the weekly ridership of 11 major subway stations. The temporal association of S-SDS with influenza-like illness (ILI) rates or the COVID-19 pandemic activity was analyzed using structural vector autoregressive modeling and the Granger causality (GC) test. During three influenza seasons (2017-2020), the time-series regression presented a significant causality from S-SDS to ILI (p = 0.0484). During the COVID-19 pandemic in January 2020, S-SDS had been suppressed at a level similar to or below the average of the previous four years. In contrast to the ILI rate, there was a negative correlation between COVID-19 activity and S-SDS. GC analysis revealed a negative causal relationship between COVID-19 and S-SDS (p = 0.0098).; S-SDS showed a significant time-series association with the ILI rate but not with COVID-19 activity. When public transportation use is sufficiently suppressed, additional social mobility restrictions are unlikely to significantly affect COVID-19 pandemic activity. It would be more important to strengthen universal mask-wearing and detailed public health measures focused on risk activities, particularly in enclosed spaces.

Seong Hye, Hong Jin-Wook, Hyun Hak-Jun, Yoon Jin-Gu, Noh Ji-Yun, Cheong Hee-Jin, Kim Woo-Joo, Jung Jae-Hun, Song Joon-Young

2021-Jul-29

COVID-19, public health, severe acute respiratory syndrome coronavirus 2, social distancing, subway

Public Health Public Health

Impact of COVID-19 on the Health of the General and More Vulnerable Population and Its Determinants: Health Care and Social Survey-ESSOC, Study Protocol.

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

This manuscript describes the rationale and protocol of a real-world data (RWD) study entitled Health Care and Social Survey (ESSOC, Encuesta Sanitaria y Social). The study's objective is to determine the magnitude, characteristics, and evolution of the COVID-19 impact on overall health as well as the socioeconomic, psychosocial, behavioural, occupational, environmental, and clinical determinants of both the general and more vulnerable population. The study integrates observational data collected through a survey using a probabilistic, overlapping panel design, and data from clinical, epidemiological, demographic, and environmental registries. The data will be analysed using advanced statistical, sampling, and machine learning techniques. The study is based on several measurements obtained from three random samples of the Andalusian (Spain) population: general population aged 16 years and over, residents in disadvantaged areas, and people over the age of 55. Given the current characteristics of this pandemic and its future repercussions, this project will generate relevant information on a regular basis, commencing from the beginning of the State of Alarm. It will also establish institutional alliances of great social value, explore and apply powerful and novel methodologies, and produce large, integrated, high-quality and open-access databases. The information described here will be vital for health systems in order to design tailor-made interventions aimed at improving the health care, health, and quality of life of the populations most affected by the COVID-19 pandemic.

Sánchez-Cantalejo Carmen, Rueda María Del Mar, Saez Marc, Enrique Iria, Ferri Ramón, Fuente Miguel de La, Villegas Román, Castro Luis, Barceló Maria Antònia, Daponte-Codina Antonio, Lorusso Nicola, Cabrera-León Andrés

2021-Jul-31

COVID-19, SARS-CoV-2, health determinants, health inequalities, machine learning, population registries, public health, real-world data, surveys, vulnerable populations

General General

COVID-19 Misinformation Online and Health Literacy: A Brief Overview.

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

Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and 'viral' nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.

Bin Naeem Salman, Kamel Boulos Maged N

2021-Jul-30

COVID-19, digital health literacy, disinformation, health literacy, infodemic, misinformation, social media

General General

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images.

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

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

Barua Prabal Datta, Muhammad Gowdh Nadia Fareeda, Rahmat Kartini, Ramli Norlisah, Ng Wei Lin, Chan Wai Yee, Kuluozturk Mutlu, Dogan Sengul, Baygin Mehmet, Yaman Orhan, Tuncer Turker, Wen Tao, Cheong Kang Hao, Acharya U Rajendra

2021-Jul-29

COVID-19 detection, Exemplar COVID-19FclNet9, deep feature generation, iterative NCA, transfer learning

General General

Animal Transmission of SARS-CoV-2 and the Welfare of Animals during the COVID-19 Pandemic.

In Animals : an open access journal from MDPI

The accelerated pace of research into Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) necessitates periodic summaries of current research. The present paper reviews virus susceptibilities in species with frequent human contact, and factors that are best predictors of virus susceptibility. Species reviewed were those in contact with humans through entertainment, pet, or agricultural trades, and for whom reports (either anecdotal or published) exist regarding the SARS-CoV-2 virus and/or the resulting disease state COVID-19. Available literature was searched using an artificial intelligence (AI)-assisted engine, as well as via common databases, such as Web of Science and Medline. The present review focuses on susceptibility and transmissibility of SARS-CoV-2, and polymorphisms in transmembrane protease serine 2 (TMPRSS2) and angiotensin-converting enzyme 2 (ACE2) that contribute to species differences. Dogs and pigs appear to have low susceptibility, while ferrets, mink, some hamster species, cats, and nonhuman primates (particularly Old World species) have high susceptibility. Precautions may therefore be warranted in interactions with such species, and more selectivity practiced when choosing appropriate species to serve as models for research.

Ekstrand Kimberly, Flanagan Amanda J, Lin Ilyan E, Vejseli Brendon, Cole Allicyn, Lally Anna P, Morris Robert L, Morgan Kathleen N

2021-Jul-08

COVID-19, SARS-CoV-2, animal model, animal transmission, animal welfare

General General

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

bioRxiv Preprint

The COVID-19 pandemic has emphasized the importance of detecting known and emerging pathogens from clinical and environmental samples. However, robust characterization of pathogenic sequences remains an open challenge. To this end, we developed SeqScreen, which can accurately characterize short nucleotide sequences using taxonomic and functional labels, and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed pathogen characterization and is available for download at: www.gitlab.com/treangenlab/seqscreen

Balaji, A.; Kille, B.; Kappell, A. D.; Godbold, G. D.; Diep, M.; Elworth, L. R. A.; Qian, Z.; Albin, D.; Nasko, D. J.; Shah, N.; Pop, M.; Segarra, S.; Ternus, K. L.; Treangen, T. J.

2021-08-08

General General

Illness duration and symptom profile in symptomatic UK school-aged children tested for SARS-CoV-2.

In The Lancet. Child & adolescent health

BACKGROUND : In children, SARS-CoV-2 infection is usually asymptomatic or causes a mild illness of short duration. Persistent illness has been reported; however, its prevalence and characteristics are unclear. We aimed to determine illness duration and characteristics in symptomatic UK school-aged children tested for SARS-CoV-2 using data from the COVID Symptom Study, one of the largest UK citizen participatory epidemiological studies to date.

METHODS : In this prospective cohort study, data from UK school-aged children (age 5-17 years) were reported by an adult proxy. Participants were voluntary, and used a mobile application (app) launched jointly by Zoe Limited and King's College London. Illness duration and symptom prevalence, duration, and burden were analysed for children testing positive for SARS-CoV-2 for whom illness duration could be determined, and were assessed overall and for younger (age 5-11 years) and older (age 12-17 years) groups. Children with longer than 1 week between symptomatic reports on the app were excluded from analysis. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed.

FINDINGS : 258 790 children aged 5-17 years were reported by an adult proxy between March 24, 2020, and Feb 22, 2021, of whom 75 529 had valid test results for SARS-CoV-2. 1734 children (588 younger and 1146 older children) had a positive SARS-CoV-2 test result and calculable illness duration within the study timeframe (illness onset between Sept 1, 2021, and Jan 24, 2021). The most common symptoms were headache (1079 [62·2%] of 1734 children), and fatigue (954 [55·0%] of 1734 children). Median illness duration was 6 days (IQR 3-11) versus 3 days (2-7) in children testing negative, and was positively associated with age (Spearman's rank-order rs 0·19, p<0·0001). Median illness duration was longer for older children (7 days, IQR 3-12) than younger children (5 days, 2-9). 77 (4·4%) of 1734 children had illness duration of at least 28 days, more commonly in older than younger children (59 [5·1%] of 1146 older children vs 18 [3·1%] of 588 younger children; p=0·046). The commonest symptoms experienced by these children during the first 4 weeks of illness were fatigue (65 [84·4%] of 77), headache (60 [77·9%] of 77), and anosmia (60 [77·9%] of 77); however, after day 28 the symptom burden was low (median 2 symptoms, IQR 1-4) compared with the first week of illness (median 6 symptoms, 4-8). Only 25 (1·8%) of 1379 children experienced symptoms for at least 56 days. Few children (15 children, 0·9%) in the negatively tested cohort had symptoms for at least 28 days; however, these children experienced greater symptom burden throughout their illness (9 symptoms, IQR 7·7-11·0 vs 8, 6-9) and after day 28 (5 symptoms, IQR 1·5-6·5 vs 2, 1-4) than did children who tested positive for SARS-CoV-2.

INTERPRETATION : Although COVID-19 in children is usually of short duration with low symptom burden, some children with COVID-19 experience prolonged illness duration. Reassuringly, symptom burden in these children did not increase with time, and most recovered by day 56. Some children who tested negative for SARS-CoV-2 also had persistent and burdensome illness. A holistic approach for all children with persistent illness during the pandemic is appropriate.

FUNDING : Zoe Limited, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, and Alzheimer's Society.

Molteni Erika, Sudre Carole H, Canas Liane S, Bhopal Sunil S, Hughes Robert C, Antonelli Michela, Murray Benjamin, Kläser Kerstin, Kerfoot Eric, Chen Liyuan, Deng Jie, Hu Christina, Selvachandran Somesh, Read Kenneth, Capdevila Pujol Joan, Hammers Alexander, Spector Tim D, Ourselin Sebastien, Steves Claire J, Modat Marc, Absoud Michael, Duncan Emma L

2021-Aug-03

General General

Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses.

In Interdisciplinary sciences, computational life sciences

Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population's health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus's origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses' genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively.

Ahmed Imran, Jeon Gwanggil

2021-Aug-06

Artificial intelligence, COVID-19, Genome sequence analysis, Machine learning, SVM

General General

Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams.

In Entropy (Basel, Switzerland)

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data's underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances' continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms' efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

AlQabbany Abdulaziz O, Azmi Aqil M

2021-Jul-04

Poisson distribution, adaptive random forest, concept drift, data stream, online learning, resampling

General General

Longitudinal Change of Mental Health among Active Social Media Users in China during the COVID-19 Outbreak.

In Healthcare (Basel, Switzerland)

During the COVID-19 pandemic, every day, updated case numbers and the lasting time of the pandemic became major concerns of people. We collected the online data (28 January to 7 March 2020 during the COVID-19 outbreak) of 16,453 social media users living in mainland China. Computerized machine learning models were developed to estimate their daily scores of the nine dimensions of the Symptom Checklist-90 (SCL-90). Repeated measures analysis of variance (ANOVA) was used to compare the SCL-90 dimension scores between Wuhan and non-Wuhan residents. Fixed effect models were used to analyze the relation of the estimated SCL-90 scores with the daily reported cumulative case numbers and lasting time of the epidemic among Wuhan and non-Wuhan users. In non-Wuhan users, the estimated scores for all the SCL-90 dimensions significantly increased with the lasting time of the epidemic and the accumulation of cases, except for the interpersonal sensitivity dimension. In Wuhan users, although the estimated scores for all nine SCL-90 dimensions significantly increased with the cumulative case numbers, the magnitude of the changes was generally smaller than that in non-Wuhan users. The mental health of Chinese Weibo users was affected by the daily updated information on case numbers and the lasting time of the COVID-19 outbreak.

Liu Tianli, Li Sijia, Qiao Xiaochun, Song Xinming

2021-Jul-01

COVID-19, SCL-90, Sina Weibo, computerized machine learning models, longitudinal analysis, mental health

Radiology Radiology

AI-Assisted CT as a Clinical and Research Tool for COVID-19.

In Frontiers in artificial intelligence

There is compelling support for widening the role of computed tomography (CT) for COVID-19 in clinical and research scenarios. Reverse transcription polymerase chain reaction (RT-PCR) testing, the gold standard for COVID-19 diagnosis, has two potential weaknesses: the delay in obtaining results and the possibility of RT-PCR test kits running out when demand spikes or being unavailable altogether. This perspective article discusses the potential use of CT in conjunction with RT-PCR in hospitals lacking sufficient access to RT-PCR test kits. The precedent for this approach is discussed based on the use of CT for COVID-19 diagnosis and screening in the United Kingdom and China. The hurdles and challenges are presented, which need addressing prior to realization of the potential roles for CT artificial intelligence (AI). The potential roles include a more accurate clinical classification, characterization for research roles and mechanisms, and informing clinical trial response criteria as a surrogate for clinical outcomes.

Tse Zion Tsz Ho, Hovet Sierra, Ren Hongliang, Barrett Tristan, Xu Sheng, Turkbey Baris, Wood Bradford J

2021

COVID-19, RT-PCR, artificial intelligence, computed tomography, diagnosis

General General

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning.

In Journal of multidisciplinary healthcare

Background : Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type.

Objective : The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history).

Methods : Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively.

Results : In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only.

Conclusion : The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.

Aljouie Abdulrhman Fahad, Almazroa Ahmed, Bokhari Yahya, Alawad Mohammed, Mahmoud Ebrahim, Alawad Eman, Alsehawi Ali, Rashid Mamoon, Alomair Lamya, Almozaai Shahad, Albesher Bedoor, Alomaish Hassan, Daghistani Rayyan, Alharbi Naif Khalaf, Alaamery Manal, Bosaeed Mohammad, Alshaalan Hesham

2021

CBC, COVID-19, NIV, SMOTE; machine learning, X-rays, mortality, random forest

General General

Deep learning of contagion dynamics on complex networks.

In Nature communications ; h5-index 260.0

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

Murphy Charles, Laurence Edward, Allard Antoine

2021-Aug-05

Cardiology Cardiology

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

In Mayo Clinic proceedings

OBJECTIVE : To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).

METHODS : A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.

RESULTS : The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.

CONCLUSION : Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

Attia Zachi I, Kapa Suraj, Dugan Jennifer, Pereira Naveen, Noseworthy Peter A, Jimenez Francisco Lopez, Cruz Jessica, Carter Rickey E, DeSimone Daniel C, Signorino John, Halamka John, Chennaiah Gari Nikhita R, Madathala Raja Sekhar, Platonov Pyotr G, Gul Fahad, Janssens Stefan P, Narayan Sanjiv, Upadhyay Gaurav A, Alenghat Francis J, Lahiri Marc K, Dujardin Karl, Hermel Melody, Dominic Paari, Turk-Adawi Karam, Asaad Nidal, Svensson Anneli, Fernandez-Aviles Francisco, Esakof Darryl D, Bartunek Jozef, Noheria Amit, Sridhar Arun R, Lanza Gaetano A, Cohoon Kevin, Padmanabhan Deepak, Pardo Gutierrez Jose Alberto, Sinagra Gianfranco, Merlo Marco, Zagari Domenico, Rodriguez Escenaro Brenda D, Pahlajani Dev B, Loncar Goran, Vukomanovic Vladan, Jensen Henrik K, Farkouh Michael E, Luescher Thomas F, Su Ping Carolyn Lam, Peters Nicholas S, Friedman Paul A

2021-Aug

General General

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images.

In Computers in biology and medicine

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.

Momeny Mohammad, Neshat Ali Asghar, Hussain Mohammad Arafat, Kia Solmaz, Marhamati Mahmoud, Jahanbakhshi Ahmad, Hamarneh Ghassan

2021-Jul-29

COVID-19, Classification, Data augmentation, Deep learning, Learning-to-augment, Noise, X-ray images

Dermatology Dermatology

Can artificial intelligence be used for accurate remote scoring of the Psoriasis Area and Severity Index (PASI) in adult patients with plaque psoriasis? A Critically Appraised Topic.

In The British journal of dermatology

A 47-year-old man with chronic plaque psoriasis and type II diabetes mellitus on ustekinumab 45mg 12 weekly injections, his first biologic therapy, was switched to adalimumab 40mg alternate weeks (10/02/2020) when his Psoriasis Area and Severity Index (PASI) was 11.4 due to loss of effectiveness. Due to the COVID-19 outbreak, the U.K Prime Minister advised clinically vulnerable people, including people on immunosuppressive medication with comorbidities including diabetes, to stay home avoiding face-to-face contact (23/03/2020). We performed a Critically Appraised Topic review to understand whether it was possible to remotely assess his response to adalimumab with AI assistance.

de Brito M, Stevens B R, Yiu Z Z N

2021-Aug-05

Pathology Pathology

Near-Complete Genome Sequences of Nine SARS-CoV-2 Strains Harboring the D614G Mutation in Malaysia.

In Microbiology resource announcements

Here, we report the nearly complete genome sequences of nine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the D614G mutation. These viruses were detected from various infected individuals with different levels of severity from Pahang, Malaysia. In addition, this study described the presence of lineage B.1.351 as a type of variant of concern (VOC) and lineages B.1.466.2 and B.1.524 as local variants.

Zainulabid Ummu Afeera, Kamarudin Norhidayah, Zulkifly Ahmad Hafiz, Gan Han Ming, Tay Darren Dean, Siew Shing Wei, Mat Yassim Aini Syahida, Soffian Sharmeen Nellisa, Mohd Faudzi Ahmad Afif, Gazali Ahmad Mahfuz, Maniam Gaanty Pragas, Ahmad Hajar Fauzan

2021-Aug-05

Pathology Pathology

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

In eLife

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

Barone Sierra M, Paul Alberta Ga, Muehling Lyndsey M, Lannigan Joanne A, Kwok William W, Turner Ronald B, Woodfolk Judith A, Irish Jonathan M

2021-Aug-05

computational biology, human, immunology, inflammation, systems biology

General General

Novel coronavirus pneumonia detection and segmentation based on the deep-learning method.

In Annals of translational medicine

Background : Segmentation of coronavirus disease 2019 (COVID-19) lesions is a difficult task due to high uncertainty in the shape, size and location of the lesions. CT scan image is an important means of diagnosing COVID-19, but it requires doctors to observe a large number of scan images repeatedly to determine the patient's condition. Moreover, the low contrast of CT scan and the presence of tissues such as blood vessels in the background increase the difficulty of diagnosis. To solve this problem, we proposed an improved segmentation model called the residual attention U-shaped network (ResAU-Net).

Methods : A novel method to detect and segment coronavirus pneumonia was established based on the deep-learning algorithm. Firstly, the CT scan image was input, and lung segmentation was then realized by U-net. Then, the region of interest was selected by the minimum circumscribed rectangle clipping method. Finally, the proposed ResAU-Net, which includes attention module (AMB), residual module (RBM) and sub-pixel convolution module (SPCBM), was used to segment the infected area and generate the segmentation results.

Results : We evaluated our model using cross-validation on 100 chest CT scans test images. The experimental results showed that our method achieved start-of-the-art performance on the pneumonia dataset. The mIoU and Dice cofficients of Lesion segmentation were 73.40%±2.24% and 84.5%±2.46%, and realize fast real-time processing.

Conclusions : Our model can effectively solve the problems of poor segmentation accuracy in the segmentation of COVID-19 lesions, and the segmentation result image can effectively assist medical staff in the diagnosis and quantitative analysis of infection degree, and improve the screening and diagnosis efficiency of pneumonia.

Zhang Zhiliang, Ni Xinye, Huo Guanying, Li Qingwu, Qi Fei

2021-Jun

Novel coronavirus pneumonia diagnosis, attention mechanism, deep learning, lesion segmentation, sub-pixel convolution

General General

Machine Learning Algorithms are Superior to Conventional Regression Models in Predicting Risk Stratification of COVID-19 Patients.

In Risk management and healthcare policy

Background : It is very important to determine the risk of patients developing severe or critical COVID-19, but most of the existing risk prediction models are established using conventional regression models. We aim to use machine learning algorithms to develop predictive models and compare predictive performance with logistic regression models.

Methods : The medical record of 161 COVID-19 patients who were diagnosed January-April 2020 were retrospectively analyzed. The patients were divided into two groups: asymptomatic-moderate group (132 cases) and severe or above group (29 cases). The clinical features and laboratory biomarkers of these two groups were compared. Machine learning algorithms and multivariate logistic regression analysis were used to construct two COVID-19 risk stratification prediction models, and the area under the curve (AUC) was used to compare the predictive efficacy of these two models.

Results : A machine learning model was constructed based on seven characteristic variables: high sensitivity C-reactive protein (hs-CRP), procalcitonin (PCT), age, neutrophil count (Neuc), hemoglobin (HGB), percentage of neutrophils (Neur), and platelet distribution width (PDW). The AUC of the model was 0.978 (95% CI: 0.960-0.996), which was significantly higher than that of the logistic regression model (0.827; 95% CI: 0.724-0.930) (P=0.002). Moreover, the machine learning model's sensitivity, specificity, and accuracy were better than those of the logistic regression model.

Conclusion : Machine learning algorithms improve the accuracy of risk stratification in patients with COVID-19. Using detection algorithms derived from these techniques can enhance the identification of critically ill patients.

Ye Jiru, Hua Meng, Zhu Feng

2021

COVID-19, high sensitivity C-reactive protein, machine learning, prediction model, procalcitonin

General General

Applications of 2D and 3D-Dynamic Representations of DNA/RNA Sequences for a description of genome sequences of viruses.

In Combinatorial chemistry & high throughput screening

** : The aim of the studies is to show that graphical bioinformatics methods are good tools for the description of genome sequences of viruses. A new approach to the identification of unknown virus strains is proposed.

METHODS : Biological sequences have been represented graphically through 2D and 3D-Dynamic Representations of DNA/RNA Sequences - theoretical methods for the graphical representation of the sequences developed by us earlier. In these approaches, some ideas of the classical dynamics have been introduced to bioinformatics. The sequences are represented by sets of material points in 2D or 3D spaces. The distribution of the points in space is characteristic of the sequence. The numerical parameters (descriptors) characterizing the sequences correspond to the quantities typical for classical dynamics.

RESULTS : Some applications of the theoretical methods have been reviewed briefly. 2D-dynamic graphs representing the complete genome sequences of SARS-CoV-2 are shown.

CONCLUSION : It is proved that the 3D-Dynamic Representation of DNA/RNA Sequences, coupled with the random forest algorithm, classifies successfully the subtypes of influenza A virus strains.

Bielińska-Wąż Dorota, Wąż Piotr, Panas Damian

2021-Aug-04

Graphical bioinformatics; 2D and 3D-Dynamic Representations of DNA/RNA Sequences; supervised learning; machine learning; Random Forest; Boruta algorithm.

General General

Development and validation of a machine-learning ALS survival model lacking vital capacity (VC-Free) for use in clinical trials during the COVID-19 pandemic.

In Amyotrophic lateral sclerosis & frontotemporal degeneration

Introduction: Vital capacity (VC) is routinely used for ALS clinical trial eligibility determinations, often to exclude patients unlikely to survive trial duration. However, spirometry has been limited by the COVID-19 pandemic. We developed a machine-learning survival model without the use of baseline VC and asked whether it could stratify clinical trial participants and a wider ALS clinic population. Methods. A gradient boosting machine survival model lacking baseline VC (VC-Free) was trained using the PRO-ACT ALS database and compared to a multivariable model that included VC (VCI) and a univariable baseline %VC model (UNI). Discrimination, calibration-in-the-large and calibration slope were quantified. Models were validated using 10-fold internal cross validation, the VITALITY-ALS clinical trial placebo arm and data from the Emory University tertiary care clinic. Simulations were performed using each model to estimate survival of patients predicted to have a > 50% one year survival probability. Results. The VC-Free model suffered a minor performance decline compared to the VCI model yet retained strong discrimination for stratifying ALS patients. Both models outperformed the UNI model. The proportion of excluded vs. included patients who died through one year was on average 27% vs. 6% (VCI), 31% vs. 7% (VC-Free), and 13% vs. 10% (UNI). Conclusions. The VC-Free model offers an alternative to the use of VC for eligibility determinations during the COVID-19 pandemic. The observation that the VC-Free model outperforms the use of VC in a broad ALS patient population suggests the use of prognostic strata in future, post-pandemic ALS clinical trial eligibility screening determinations.

Beaulieu Danielle, Berry James D, Paganoni Sabrina, Glass Jonathan D, Fournier Christina, Cuerdo Jonavelle, Schactman Mark, Ennist David L

2021

COVID-19, Vital capacity, clinical trials, machine learning prognostic model, survival model

General General

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

ArXiv Preprint

Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem. A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives. Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues. Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, do not yield explicit optimal policies, or do not consider strategic behavior, for example. Hence, it remains challenging to optimize policy in real-world scenarios. Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations. We validate our framework on optimizing the stringency of US state policies and Federal subsidies during a pandemic, e.g., COVID-19, using a simulation fitted to real data. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes. Their behavior can be explained, e.g., well-performing policies respond strongly to changes in recovery and vaccination rates. They are also robust to calibration errors, e.g., infection rates that are over or underestimated. As of yet, real-world policymaking has not seen adoption of machine learning methods at large, including RL and AI-driven simulations. Our results show the potential of AI to guide policy design and improve social welfare amidst the complexity of the real world.

Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng

2021-08-06

General General

Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors.

In Journal of chemical information and modeling

Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided c