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

Q: What type of study is this?

This is a Validation Study study.

Jabed Al Faysal; Weihsuan Lo-Ciganic; Walid F. Gellad; Yuan Wu; Christopher A. Harle; Khoa Nguyen; James L. Huang; Gerald Cochran; Debbie L. Wilson; Stephanie AS Staras; Siegfried OF Schmidt; Eric I Rosenberg; David D. Nelson; Shunhua Yan; Gary M. Reisfield; William M Greene; Courtney C. Kuza; Md Mahmudul Hasan

doi:10.2196/79482

Key Points

To develop a machine learning algorithm for predicting incident opioid use disorder (OUD) among OneFlorida+ patients.
Developed a machine learning algorithm using OneFlorida+ EHR data.
Performed external validation of the algorithm with UPMC data.
Assessed algorithm's performance for risk prediction and stratification.
The algorithm showed strong performance in external validation.
It has potential for effective risk prediction of incident OUD.
Findings could support early intervention strategies across health systems.

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Implication

Prognostic modeling demonstrates risk prediction for opioid use disorder, suggesting early intervention may be beneficial.

Abstract

An ML algorithm predicting incident OUD derived from OneFlorida+ EHR data performed well in external validation with data using UPMC. The algorithm might be valuable for incident OUD risk prediction and stratification across health systems, with potential to inform early intervention.

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Cite This Study

Faysal et al. (2026) studied this question.

synapsesocial.com/papers/69b4ad9a18185d8a39801173 https://doi.org/10.2196/79482

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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