Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.

Saved in:
Bibliographic Details
Title: Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.
Authors: Sharp A; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America. Electronic address: asharp@challiance.org., Parry GJ; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Pérez GR; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America., Mullin BO; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America., Yang X; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Kumar A; Institute for Behavioral Health, Heller School for Social Policy & Management Brandeis University, United States of America., Creedon T; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America., Flores M; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Fischer CM; Department of Emergency Medicine, Mount Auburn Hospital, United States of America., Schuman-Olivier Z; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Moyer M; John Snow, Inc., United States of America., Tran NM; Department of Health Policy, Vanderbilt University, United States of America., Cook BL; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America.
Source: Journal of substance use and addiction treatment [J Subst Use Addict Treat] 2025 Aug; Vol. 175, pp. 209718. Date of Electronic Publication: 2025 May 03.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918541186406676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2949-8759 (Electronic) Linking ISSN: 29498759 NLM ISO Abbreviation: J Subst Use Addict Treat Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2949-8759
DOI:10.1016/j.josat.2025.209718