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in category health informatics

481 results found. For more information, click each entry to expand.

1: Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app
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Posted 16 Jun 2020

Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app
45,275 downloads medRxiv health informatics

Carole H Sudre, Karla Lee, Mary Ni Lochlainn, Thomas Varsavsky, Benjamin Murray, Mark S Graham, Cristina Menni, Marc Modat, Ruth C.E. Bowyer, Long H Nguyen, David Alden Drew, Amit D. Joshi, Wenjie Ma, Chuan Guo Guo, Chun Han Lo, Sajaysurya Ganesh, Abubakar Buwe, Joan Capdevila Pujol, Julien Lavigne du Cadet, Alessia Visconti, Maxim Freidin, Julia S. El Sayed Moustafa, Mario Falchi, Richard Davies, Maria F Gomez, Tove Fall, M. Jorge Cardoso, Jonathan Wolf, Paul W Franks, Andrew T Chan, Timothy D. Spector, Claire J Steves, Sebastien Ourselin

As no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between May 1- May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

2: On the Generation of Medical Dialogues for COVID-19
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Posted 15 May 2020

On the Generation of Medical Dialogues for COVID-19
27,345 downloads medRxiv health informatics

Wenmian Yang, Guangtao Zeng, Bowen Tan, Zeqian Ju, Subrato Chakravorty, Xuehai He, Shu Chen, Xingyi Yang, Qingyang Wu, Zhou Yu, Eric Xing, Pengtao Xie

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogue about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue

3: Correcting under-reported COVID-19 case numbers
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Posted 18 Mar 2020

Correcting under-reported COVID-19 case numbers
25,312 downloads medRxiv health informatics

Kathleen M. Jagodnik, Forest Ray, Federico M. Giorgi, Alexander Lachmann

The COVID-19 virus has spread worldwide in a matter of a few months, while healthcare systems struggle to monitor and report current cases. Testing results have struggled with the relative capabilities, testing policies and preparedness of each affected country, making their comparison a non-trivial task. Since severe cases, which more likely lead to fatal outcomes, are detected at a higher rate than mild cases, the reported virus mortality is likely inflated in most countries. Lockdowns and changes in human behavior modulate the underlying growth rate of the virus. Under-sampling of infection cases may lead to the under-estimation of total cases, resulting in systematic mortality estimation biases. For healthcare systems worldwide it is important to know the expected number of cases that will need treatment. In this manuscript, we identify a generalizable growth rate decay reflecting behavioral change. We propose a method to correct the reported COVID-19 cases and death numbers by using a benchmark country (South Korea) with near-optimal testing coverage, with considerations on population demographics. We extrapolate expected deaths and hospitalizations with respect to observations in countries that passed the exponential growth curve. By applying our correction, we predict that the number of cases is highly under-reported in most countries and a significant burden on worldwide hospital capacity. The full analysis workflow and data is available at: https://github.com/lachmann12/covid19

4: Connecting BCG Vaccination and COVID-19: Additional Data
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Posted 08 Apr 2020

Connecting BCG Vaccination and COVID-19: Additional Data
13,223 downloads medRxiv health informatics

Devi Dayal, Saniya Gupta

The reasons for a wide variation in severity of coronavirus disease 2019 (COVID-19) across the affected countries of the world are not known. Two recent studies have suggested a link between the BCG vaccination policy and the morbidity and mortality due to COVID-19. In the present study we compared the impact of COVID-19 in terms of case fatality rates (CFR) between countries with high disease burden and those with BCG revaccination policies presuming that revaccination practices would have provided added protection to the population against severe COVID-19. We found a significant difference in the CFR between the two groups of countries. Our data further supports the view that universal BCG vaccination has a protective effect on the course of COVID-19 probably preventing progression to severe disease and death. Clinical trials of BCG vaccine are urgently needed to establish its beneficial role in COVID-19 as suggested by the epidemiological data, especially in countries without a universal BCG vaccination policy.

5: A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study
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Posted 22 Apr 2020

A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study
8,209 downloads medRxiv health informatics

Haipeng Zhang, Dimitar Dimitrov, Lynn Simpson, Balaji Singh, Nina Plaks, Stephen Penney, Jo Charles, Rosemary Sheehan, Steven Flammini, Shawn Murphy, Adam Landman

Background: The COVID-19 pandemic has impacted over 1 million people across the globe, with over 330,000 cases in the United States. To help limit the spread in Massachusetts, the Department of Public Health required that all healthcare workers must be screened for symptoms daily - individuals with symptoms may not work. We rapidly created a digital COVID-19 symptom screening tool for a large, academic, integrated healthcare delivery system, Partners HealthCare, in Boston, Massachusetts. Objective: We describe the design and development of the COVID-19 symptom screening application and report on aggregate usage data from the first week of use across the organization. Methods: Using agile principles, we designed, tested and implemented a solution over the span of a week using progressively custom development approaches as the requirements and use case become more solidified. We developed the minimum viable product (MVP) of a mobile responsive, web-based self-service application using REDCap (Research Electronic Data Capture). For employees without access to a computer or mobile device to use the self-service application, we established a manual process where in-person, socially distanced screeners asked employees entering the site if they have symptoms and then manually recorded the responses in an Office 365 Form. A custom .NET Framework application was developed solution as COVID Pass was scaled. We collected log data from the .NET application, REDCap and Office 365 from the first week of full enterprise deployment (March 30, 2020 - April 5, 2020). Aggregate descriptive statistics including overall employee attestations by day and site, employee attestations by application method (COVID Pass automatic screening vs. manual screening), employee attestations by time of day, and percentage of employees reporting COVID-19 symptoms Results: We rapidly created the MVP and gradually deployed it across the hospitals in our organization. By the end of the first week of enterprise deployment, the screening application was being used by over 25,000 employees each weekday. Over the first full week of deployment, 154,730 employee attestation logs were processed across the system. Over this 7-day period, 558 (0.36%) employees reported positive symptoms. In most clinical locations, the majority of employees (~80-90%) used the self-service application, with a smaller percentage (~10-20%) using manual attestation. Hospital staff continued to work around the clock, but as expected, staff attestations peaked during shift changes between 7-8am, 2-3pm, 4-6pm, and 11pm-midnight. Conclusions: Using rapid, agile development, we quickly created and deployed a dedicated employee attestation application that gained widespread adoption and use within our health system. Further, we have identified over 500 symptomatic employees that otherwise would have possibly come to work, potentially putting others at risk. We share the story of our implementation, lessons learned, and source code (via GitHub) for other institutions who may want to implement similar solutions.

6: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
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Posted 01 Apr 2020

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
7,621 downloads medRxiv health informatics

Asmaa Abbas, Mohammed Abdelsamea, Mohamed Gaber

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adopt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

7: COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms
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Posted 11 Apr 2020

COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms
7,335 downloads medRxiv health informatics

Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal

The catastrophic outbreak of Severe Acute Respiratory Syndrome - Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.

8: Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
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Posted 17 Apr 2020

Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
6,966 downloads medRxiv health informatics

Xuehai He, Xingyi Yang, Shanghang Zhang, Jinyu Zhao, Yichen Zhang, Eric Xing, Pengtao Xie

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based on CT scans. However, these works are difficult to reproduce and adopt since the CT data used in their studies are not publicly available. Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain. In this paper, we aim to address these two problems. We build a publicly-available dataset containing hundreds of CT scans positive for COVID-19 and develop sample-efficient deep learning methods that can achieve high diagnosis accuracy of COVID-19 from CT scans even when the number of training CT images are limited. Specifically, we propose an Self-Trans approach, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting. Extensive experiments demonstrate the superior performance of our proposed Self-Trans approach compared with several state-of-the-art baselines. Our approach achieves an F1 of 0.85 and an AUC of 0.94 in diagnosing COVID-19 from CT scans, even though the number of training CTs is just a few hundred.

9: COVID-19 and Mental Health: A Study of its Impact on Students
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Posted 11 Aug 2020

COVID-19 and Mental Health: A Study of its Impact on Students
6,445 downloads medRxiv health informatics

Kshipra Moghe, Disha Kotecha, Manjusha Patil

The purpose of this study was to identify and analyse the personal, social and psychological impact of COVID - 19 on the mental health of students of age group 16 to 25. A response from N= 351 students (from the most affected state in India), provided a comparative analysis based on the gender, and background to understand the pattern in issues related to mental health during the pandemic. The results show that female students are more concerned about health, and future, and are more prone to psychological issues like feelings of uncertainty, helplessness and outbursts than male students. Urban students population is more mentally affected than their rural counterparts, however time spent on the internet is almost the same despite the difference in infrastructure and resources. Also, there is an increase in need for solitude, being withdrawn and self-harm in male students require attention. A shift in perception from seeing family as a source of support to that of a restriction is indicated, although the benefits of a collectivistic society are undisputed. The results indicate that there is overall increased awareness about mental health among the student population and with programs/strategies focusing on background and gender, a significant improvement is attainable.

10: Investigating the likely association between genetic ancestry and COVID-19 manifestation
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Posted 07 Apr 2020

Investigating the likely association between genetic ancestry and COVID-19 manifestation
5,719 downloads medRxiv health informatics

Ranajit Das, Sudeep D Ghate

The novel coronavirus 2019-nCoV/SARS-CoV-2 infection has shown discernible variability across the globe. While in some countries people are recovering relatively quicker, in others, recovery times have been comparatively longer and numbers of those succumbing to it high. In this study, we aimed to evaluate the likely association between an individuals ancestry and the extent of COVID-19 manifestation employing Europeans as the case study. We employed 10,215 ancient and modern genomes across the globe assessing 597,573 single nucleotide polymorphisms (SNPs). Pearsons correlation coefficient (r) between various ancestry proportions of European genomes and COVID-19 death/recovery ratio was calculated and its significance was statistically evaluated. We found significant positive correlation (p=0.03) between European Mesolithic hunter gatherers (WHG) ancestral fractions and COVID-19 death/recovery ratio and a marginally significant negative correlation (p=0.06) between Neolithic Iranian ancestry fractions and COVID-19 death/recovery ratio. We further identified 404 immune response related single nucleotide polymorphisms (SNPs) by comparing publicly available 753 genomes from various European countries against 838 genomes from various Eastern Asian countries in a genome wide association study (GWAS). Prominently we identified that SNPs associated with Interferon stimulated antiviral response, Interferon-stimulated gene 15 mediated antiviral mechanism and 2'-5' oligoadenylate synthase mediated antiviral response show large differences in allele frequencies between Europeans and East Asians. Overall, to the best of our knowledge, this is the first study evaluating the likely association between genetic ancestry and COVID-19 manifestation. While our current findings improve our overall understanding of the COVID-19, we note that the development of effective therapeutics will benefit immensely from a more detailed analyses of individual genomic sequence data from COVID-19 patients of varied ancestries.

11: Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine
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Posted 06 Apr 2020

Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine
4,633 downloads medRxiv health informatics

Lamia Nabil Mahdy, Kadry Ali Ezzat, Haytham H. Elmousalami, Hassan Aboul Ella, Aboul Ella Hassanien

The early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for the clinical practitioners. The COVID-19 spread is announced as pandemic outbreak between people worldwide by WHO since 11/ March/ 2020. In this consequence, it is top critical priority to become aware of the infected people so that prevention procedures can be processed to minimize the COVID-19 spread and to begin early medical health care of those infected persons. In this paper, the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images. The help vector gadget classifies the corona affected X-ray images from others through usage of the deep features. The technique is useful for the clinical practitioners for early detection of COVID-19 infected patients. The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19. All images were of the same size and stored in JPEG format with 512 * 512 pixels. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76%, 99.7%, and 97.48%, respectively.

12: COVID-19 Outbreak Prediction with Machine Learning
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Posted 22 Apr 2020

COVID-19 Outbreak Prediction with Machine Learning
4,609 downloads medRxiv health informatics

Sina Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R. Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, Peter M. Atkinson

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.

13: Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator
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Posted 08 May 2020

Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator
4,509 downloads medRxiv health informatics

Salomon Wollenstein-Betech, Christos G. Cassandras, Ioannis Ch. Paschalidis

Background: The rapid global spread of the virus SARS-CoV-2 has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. Objective: To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient's basic preconditions, which can be easily gathered without the need to be at a hospital. For the remaining models, different versions developed include different sets of a patient's features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). Materials and Methods: Data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. Results: Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 61%, 76%, 83%, and 84% for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization: age, gender, chronic renal insufficiency, diabetes, immunosuppression; (2) for mortality: age, SARS-CoV-2 test status, immunosuppression and pregnancy; (3) for ICU need: development of pneumonia (if available), cardiovascular disease, asthma, and SARS-CoV-2 test status; and (4) for ventilator need: ICU and pneumonia (if available), age, gender, cardiovascular disease, obesity, pregnancy, and SARS-CoV-2 test result.

14: Estimating Survival of Hospitalized COVID-19 Patients from Admission Information
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Posted 27 Apr 2020

Estimating Survival of Hospitalized COVID-19 Patients from Admission Information
4,157 downloads medRxiv health informatics

Todd J. Levy, Safiya Richardson, Kevin Coppa, Douglas P. Barnaby, Thomas McGinn, Lance B. Becker, Karina W. Davidson, Stuart L. Cohen, Jamie S. Hirsch, Theodoros Zanos, Northwell & Maimonides COVID-19 Research Consortium

Background: Chinese studies reported predictors of severe disease and mortality associated with coronavirus disease 2019 (COVID-19). A generalizable and simple survival calculator based on data from US patients hospitalized with COVID-19 has not yet been introduced. Objective: Develop and validate a clinical tool to predict 7-day survival in patients hospitalized with COVID-19. Design: Retrospective and prospective cohort study. Setting: Thirteen acute care hospitals in the New York City area. Participants: Adult patients hospitalized with a confirmed diagnosis of COVID-19. The development and internal validation cohort included patients hospitalized between March 1 and May 6, 2020. The external validation cohort included patients hospitalized between March 1 and May 5, 2020. Measurements: Demographic, laboratory, clinical, and outcome data were extracted from the electronic health record. Optimal predictors and performance were identified using least absolute shrinkage and selection operator (LASSO) regression with receiver operating characteristic curves and measurements of area under the curve (AUC). Results: The development and internal validation cohort included 11 095 patients with a median age of 65 years [interquartile range (IQR) 54-77]. Overall 7-day survival was 89%. Serum blood urea nitrogen, age, absolute neutrophil count, red cell distribution width, oxygen saturation, and serum sodium were identified as the 6 optimal of 42 possible predictors of survival. These factors constitute the NOCOS (Northwell COVID-19 Survival) Calculator. Performance in the internal validation, prospective validation, and external validation were marked by AUCs of 0.86, 0.82, and 0.82, respectively. Limitations: All participants were hospitalized within the New York City area. Conclusions: The NOCOS Calculator uses 6 factors routinely available at hospital admission to predict 7-day survival for patients hospitalized with COVID-19. The calculator is publicly available at https://feinstein.northwell.edu/NOCOS.

15: Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making
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Posted 01 Apr 2020

Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making
4,118 downloads medRxiv health informatics

Mohammad Pourhomayoun, Mahdi Shakibi

In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

16: Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients
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Posted 29 Apr 2020

Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients
4,100 downloads medRxiv health informatics

Karandeep Singh, Thomas S. Valley, Shengpu Tang, Benjamin Y. Li, Fahad Kamran, Michael W. Sjoding, Jenna Wiens, Erkin Otles, John P. Donnelly, Melissa Y. Wei, Jonathon P McBride, Jie Cao, Carleen Penoza, John Z. Ayanian, Brahmajee K. Nallamothu

Introduction: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the COVID-19 pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Methods: We studied adult patients admitted with COVID-19 to non-ICU care at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of ICU-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. Median age of the cohort was 64 (IQR 53-75) with 168 (43%) African Americans and 169 (43%) women. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.79 (95% CI 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically-relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusion: We found the EDI identifies small subsets of high- and low-risk COVID-19 patients with fair discrimination. We did not find evidence of bias by race or sex. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among COVID-19 patients.

17: Risks and Benefits of Antibiotics vs. COVID-19 Morbidity and Mortality
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Posted 20 Oct 2020

Risks and Benefits of Antibiotics vs. COVID-19 Morbidity and Mortality
4,051 downloads medRxiv health informatics

Hisaya Tanioka, Sayaka Tanioka

ObjectivesThe purpose is to analyze the potential association of each antibiotic consumption rate and use ratio with COVID-19 morbidity and mortality, and to investigate the efficacy and safe use of antibiotics against COVID-19. DesignRetrospective statistical analysis study of antibiotic use compared with COVID-19 morbidity and mortality. MethodsEach antibiotic defined daily dose (DDD) per 1000 inhabitants per day as each antibiotic consumption rate was available in the official reports and each antibacterial use ratio data was calculated from them. Coronavirus Disease data were obtained from the WHO Coronavirus Disease Dashboard. The relationships between the sum of DDD, each antibacterial DDD, each antibiotic use ratio, and COVID-19 morbidity and mortality were examined. The statistical correlation was calculated by univariate linear regression analysis and expressed by Pearsons correlation coefficient. ResultsThe sum of DDD had no statistical correlation with mortality and morbidity. Cephalosporins were a negative correlation with them. Penicillins had a weak positive correlation with them. Macrolides, quinolones, and sulfonates showed a slightly negative correlation tendency with mortality. ConclusionsCephalosporins may affect less COVID-19 morbidity and mortality. Penicillins suggest to accelerate them. The combination of cephalosporins with macrolides or quinolones may be a helpful treatment. The difference in antibiotic use between Japan and EU/EEA countries will suggest an explanation for the reduction in morbidity and mortality caused by COVID-19.

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Posted 29 Apr 2020

3,686 downloads medRxiv health informatics

Eduardo Soares, Plamen Angelov, Sarah Biaso, Michele Higa Froes, Daniel Kanda Abe

The infection by SARS-CoV-2 which causes the COVID-19 disease has widely spread all over the world since the beginning of2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies to control the spread of this new disease. Recent findings have observed imaging patterns on computed tomography (CT) for patients infected by SARS-CoV-2. In this paper, we build a public available SARS-CoV-2 CTscan dataset, containing 1252 CT scans that are positive for SARS-CoV-2 infection (COVID-19) and 1230 CT scans for patientsnon-infected by SARS-CoV-2, 2482 CT scans in total. These data have been collected from real patients in hospitals from Sao Paulo, Brazil. The aim of this dataset is to encourage the research and development of artificial intelligent methods which are able to identify if a person is infected by SARS-CoV-2 through the analysis of his/her CT scans. As a baseline result for this dataset we used an eXplainable Deep Learning approach (xDNN) which we could achieve anF1score of 97.31% which is very promising. The proposed dataset is available www.kaggle.com/plameneduardo/sarscov2-ctscan-datasetand xDNN code is available at https://github.com/Plamen-Eduardo/xDNN-SARS-CoV-2-CT-Scan.

19: How the COVID-19 pandemic is favoring the adoption of digital technologies in healthcare: a rapid literature review
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Posted 01 May 2020

How the COVID-19 pandemic is favoring the adoption of digital technologies in healthcare: a rapid literature review
3,579 downloads medRxiv health informatics

Davide Golinelli, Erik Boetto, Gherardo Carullo, Andrea Giovanni Nuzzolese, Maria Paola Landini, Maria Pia Fantini

Background. Healthcare is responding to the COVID-19 pandemic through the fast adoption of digital solutions and advanced technology tools. The aim of this study is to describe which digital solutions have been reported in the scientific literature and to investigate their potential impact in the fight against the COVID-19 pandemic. Methods. We conducted a literature review searching PubMed and MedrXiv with terms considered adequate to find relevant literature on the use of digital technologies in response to COVID-19. We developed an impact score to evaluate the potential impact on COVID-19 pandemic of all the digital solutions addressed in the selected papers. Results. The search identified 269 articles, of which 145 full-text articles were assessed and 124 included in the review after screening and impact evaluation. Of selected articles, most of them addressed the use of digital technologies for diagnosis, surveillance and prevention. We report that digital solutions and innovative technologies have mainly been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles we identified numerous suggestions on the use of artificial-intelligence-powered tools for the diagnosis and screening of COVID-19. Digital technologies are useful also for prevention and surveillance measures, for example through contact-tracing apps or monitoring of internet searches and social media usage. Discussion. It is worth taking advantage of the push given by the crisis, and mandatory to keep track of the digital solutions proposed today to implement tomorrow's best practices and models of care, and to be ready for any new moments of emergency.

20: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks
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Posted 23 Mar 2020

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks
3,306 downloads medRxiv health informatics

Shuo Jin, Bo Wang, Haibo Xu, Chuan Luo, Lai Wei, Wei Zhao, Xuexue Hou, Wenshuo Ma, Zhengqing Xu, Zhuozhao Zheng, Wenbo Sun, Lan Lan, Wei Zhang, Xiangdong Mu, Chenxin Shi, Zhongxiao Wang, Jihae Lee, Zijian Jin, Minggui Lin, Hongbo Jin, Liang Zhang, Jun Guo, Benqi Zhao, Zhizhong Ren, Shuhao Wang, Zheng You, Jiahong Dong, Xinghuan Wang, Jianming Wang, Wei Xu

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day.

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