文章基本信息

标题：A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
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作者：Vivek Singh ; Rishikesan Kamaleswaran ; Donald Chalfin 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：12
页码：1-21
DOI：10.1016/j.isci.2021.103523
语种：English
出版社：Elsevier
摘要：SummaryThe SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality.Graphical abstractDisplay OmittedHighlights•Algorithm using 9 laboratory markers & age may predict severity in patients with COVID-19•Model was trained and tested on a multicenter sample of 10,937 patients•Algorithm can predict ventilator use (NPV, 0.86) and mortality (NPV, 0.94)•High NPV suggests utility as an adjunct to aid in triaging of patients with COVID-19Classification Description: Virology; Diagnostics; Artificial intelligence; Machine learning