Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.

Journal: Critical care explorations
PMID: 40138535

Abstract

BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not tested in important subgroups.

Authors

  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Kyle A Carey
    Department of Medicine, University of Chicago, Chicago IL, United States.
  • Ashley Snyder
    AgileMD, San Francisco, CA.
  • Christopher J Winslow
    Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA.
  • Emily Gilbert
    Department of Medicine, Loyola University Medical Center, Maywood, IL.
  • Nirav S Shah
    Department of Medicine, Northshore Hospital, Chicago, IL.
  • Brian W Patterson
    UW Health, Madison, USA.
  • Majid Afshar
    Loyola University Chicago, Chicago, IL.
  • Alan Weiss
    BayCare, Clearwater, FL.
  • Devendra N Amin
    BayCare, Clearwater, FL.
  • Deborah J Rhodes
    Mayo Clinic, Rochester MN.
  • Dana P Edelson

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