Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review.

Journal: Studies in health technology and informatics
Published Date: May 27, 2021

Abstract

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic's impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.

Authors

  • Mahanazuddin Syed
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • Shorabuddin Syed
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • Kevin Sexton
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • Melody L Greer
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
  • Meredith Zozus
    University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Sudeepa Bhattacharyya
    Department of Biological Sciences and Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA.
  • Farhanuddin Syed
    College of Medicine, Shadan Institute of Medical Sciences, Hyderabad, TS, IN.
  • Fred Prior
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.

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