Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.

Journal: JAMA network open
Published Date:

Abstract

IMPORTANCE: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable. Similarly, few have been implemented, and none have been evaluated prospectively and externally validated.

Authors

  • Nathan Brajer
    Duke Institute for Health Innovation, Durham, North Carolina.
  • Brian Cozzi
    Duke Institute for Health Innovation, Durham, North Carolina.
  • Michael Gao
    Duke Institute for Health Innovation.
  • Marshall Nichols
    Duke Institute for Health Innovation, Durham, North Carolina.
  • Mike Revoir
    Duke Institute for Health Innovation, Durham, North Carolina.
  • Suresh Balu
    Duke Institute for Health Innovation.
  • Joseph Futoma
    Department of Statistical Science, Duke University, Durham, North Carolina.
  • Jonathan Bae
    Department of Medicine, Duke University School of Medicine, Durham, North Carolina.
  • Noppon Setji
    Department of Medicine Duke University School of Medicine.
  • Adrian Hernandez
    Duke University School of Medicine, Durham, North Carolina.
  • Mark Sendak
    Duke Institute for Health Innovation.