Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.

Journal: Critical care (London, England)
Published Date:

Abstract

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.

Authors

  • Mohammad M Banoei
    Department of Critical Care Medicine, University of Calgary, Alberta, Canada.
  • Roshan Dinparastisaleh
    Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Ali Vaeli Zadeh
    Division of Pulmonary and Critical Care, Miami VA Medical Center, Miami, FL, USA.
  • Mehdi Mirsaeidi
    Division of Pulmonary and Critical Care, Department of Medicine, University of Miami, Miami, FL, USA. msm249@med.miami.edu.