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

Decision Support for Health Professionals (1185) Clinical Information and Decision Making (30) Machine Learning (49) Safety and Error Prevention in Health (202) Machine Learning (1465) Opioid and Related Substance Misuse (147)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53625, first published .
A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study

Authors of this article:

Tewodros Eguale1, 2 Author Orcid Image ;   François Bastardot3, 4 Author Orcid Image ;   Wenyu Song2, 5 Author Orcid Image ;   Daniel Motta-Calderon6 Author Orcid Image ;   Yasmin Elsobky2, 7 Author Orcid Image ;   Angela Rui2 Author Orcid Image ;   Marlika Marceau8 Author Orcid Image ;   Clark Davis2 Author Orcid Image ;   Sandya Ganesan2 Author Orcid Image ;   Ava Alsubai2 Author Orcid Image ;   Michele Matthews1, 9 Author Orcid Image ;   Lynn A Volk8 Author Orcid Image ;   David W Bates2, 5, 8, 10 Author Orcid Image ;   Ronen Rozenblum2, 5 Author Orcid Image
Article Authors Cited by (1) Tweetations (4) Metrics

Journals

  1. Jantzen A, Hall S, Lai B, Cunningham J, Odell L. Identification of patients on chronic prescription opioids at risk for opioid use disorder using pharmacy claims data. Journal of Healthcare Risk Management 2024;44(2):14 View