A Machine Learning Trauma Triage Model for Critical Care Transport
In this cohort study of critically ill patients with trauma in the prehospital setting, an ML-based triage model using physiological features provided accurate predictions of lifesaving intervention delivery to single patients. Modeling approaches could be deployed in the field to help streamline and augment prehospital triage.
Authors
- Jonathan Elmer (ORCID: https://orcid.org/0000-0002-6648-5456)
- Leonard Weiss (ORCID: https://orcid.org/0000-0001-6857-6573)
- Ronald K. Poropatich
- Aaron C. Weidman (ORCID: https://orcid.org/0000-0002-9395-4051)
- Francis Xavier Guyette (ORCID: https://orcid.org/0000-0002-9151-4896)
- Michael R. Pinsky (ORCID: https://orcid.org/0000-0001-6166-700X)
- Joshua B. Brown (ORCID: https://orcid.org/0000-0002-6936-035X)
- Salim Malakouti (ORCID: https://orcid.org/0000-0002-7481-5523)
- David D. Salcido (ORCID: https://orcid.org/0000-0002-3735-8167)
- Ravi M. Patel (ORCID: https://orcid.org/0000-0002-7444-5550)
- Gilles Clermont (ORCID: https://orcid.org/0000-0002-0163-1379)
- Chase Zikmund
Institutions
- University of Pittsburgh (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.9639
- Citations
- 12
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 27.04
Funders
- National Science Foundation
- U.S. Department of Defense
- University of Pittsburgh
- National Institutes of Health
- U.S. Army
- Advanced Research Projects Agency
- U.S. Army Medical Research Acquisition Activity