This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (1074) Opioid and Related Substance Misuse (208) Machine Learning (2196) Decision Support for Health Professionals (1609) Electronic Health Records (1363) Clinical Informatics (1406) Clinical Information and Decision Making (2033)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79482, first published .
Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study

Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study

Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study

Authors of this article:

Jabed Al Faysal1, 2 Author Orcid Image ;   Weihsuan Lo-Ciganic3, 4, 5 Author Orcid Image ;   Walid F Gellad3, 4 Author Orcid Image ;   Yonghui Wu6 Author Orcid Image ;   Christopher A Harle7, 8 Author Orcid Image ;   Khoa Nguyen9 Author Orcid Image ;   James L Huang1 Author Orcid Image ;   Gerald Cochran10 Author Orcid Image ;   Debbie L Wilson1 Author Orcid Image ;   Stephanie AS Staras6 Author Orcid Image ;   Siegfried OF Schmidt11 Author Orcid Image ;   Eric I Rosenberg1, 12 Author Orcid Image ;   Danielle Nelson11 Author Orcid Image ;   Shunhua Yan1 Author Orcid Image ;   Gary M Reisfield1 Author Orcid Image ;   William M Greene13 Author Orcid Image ;   Courtney Kuza4 Author Orcid Image ;   Md Mahmudul Hasan1, 14 Author Orcid Image
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