A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

Journal: Journal of medical Internet research
PMID: 33476281

Abstract

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease.

Authors

  • Siavash Bolourani
    Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA.
  • Max Brenner
    Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Thomas McGinn
    Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States.
  • Jamie S Hirsch
    Department of Medicine, Norwell Health, Manhasset, NY, USA.
  • Douglas Barnaby
    Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States.
  • Theodoros P Zanos
    Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY.

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