A Machine Learning Trauma Triage Model for Critical Care Transport.

Journal: JAMA network open
Published Date: Jun 2, 2025

Abstract

IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocation of limited resources. These triage processes are hindered by the limited diagnostic resources and modalities available in the prehospital environment.

Authors

  • Aaron C Weidman
    Department of Psychology.
  • Salim Malakouti
    NOMA AI Inc, Pittsburgh, Pennsylvania.
  • David D Salcido
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Chase Zikmund
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Ravi Patel
    Department of Internal Medicine, Methodist Health System Dallas, Dallas, TX, USA.
  • Leonard S Weiss
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Michael R Pinsky
    Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
  • Gilles Clermont
    Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
  • Jonathan Elmer
    Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Neurology Division, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: elmerjp@upmc.edu.
  • Ronald K Poropatich
    Center for Military Medicine Research, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Joshua B Brown
    Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Francis X Guyette

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