Machine learning models to detect opioid misuse in emergency department patients at triage
ML classifiers leveraging routinely collected triage data offer a feasible and scalable alternative to manual screening in flagging opioid misuse before physician evaluation, potentially enabling early harm-reduction interventions. Prospective multi-site validation, calibration, and bias assessments are warranted.
Authors
- Niranjan S. Karnik (ORCID: https://orcid.org/0000-0001-7650-3008)
- Neeraj Chhabra (ORCID: https://orcid.org/0000-0002-0945-1233)
- Usman Shahid
- Natalie Parde
- Dillon Thorpe
- Sami Muslmani
- Huiyi Hu
- Chirag Chhablani
- Majid Afshar
Institutions
- University of Wisconsin–Madison (US)
- Illinois College (US)
- University of Illinois Chicago (US)
Publication Details
- Journal
- The American Journal of Emergency Medicine
- Published
- 2026-02-26
- DOI
- https://doi.org/10.1016/j.ajem.2026.02.037
- Primary Topic
- Opioid Use Disorder Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00