Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

Journal: Computers in biology and medicine
Published Date: Aug 6, 2020

Abstract

BACKGROUND: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.

Authors

  • Hoyt Burdick
    Cabell Huntington Hospital, Huntington, WV, USA; Marshall University School of Medicine, Huntington, WV, USA.
  • Carson Lam
    Department of Biomedical Data Science, Stanford University, Stanford, California, United States.
  • Samson Mataraso
    Dascena, Inc., San Francisco, CA, USA.
  • Anna Siefkas
    Dascena, Inc., San Francisco, CA, USA. Electronic address: anna@dascena.com.
  • Gregory Braden
    Kidney Care and Transplant Associates of New England, Springfield, MA, USA.
  • R Phillip Dellinger
    Division of Critical Care Medicine, Cooper University Hospital/Cooper Medical School of Rowan University, Camden, NJ, USA.
  • Andrea McCoy
    Cape Regional Medical Center, Cape May Court House, New Jersey, USA.
  • Jean-Louis Vincent
    Department of Intensive Care, Erasme Hospital, Université libre de Bruxelles, Route de Lennik 808, 1070, Brussels, Belgium. jlvincent@intensive.org.
  • Abigail Green-Saxena
    Dascena, Inc., USA.
  • Gina Barnes
    Dascena, Inc., San Francisco, CA, USA.
  • Jana Hoffman
    Dascena Inc., San Francisco, CA, United States.
  • Jacob Calvert
    Dascena Inc., Hayward, California, USA.
  • Emily Pellegrini
  • Ritankar Das
    Dascena, Inc, Hayward, California, USA.

Keywords

External Resources

Popular Topics
Recent Journals