Research Summary: Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder

A research study published on arXiv explores the critical issue of algorithmic fairness in machine learning (ML) models designed to predict treatment retention and premature discontinuation in medication for opioid use disorder (MOUD). The persistent challenge of low retention rates in MOUD treatment has led to the adoption of ML models, but concerns about their equitable application across diverse patient populations remain. This study systematically evaluates fairness in these models and investigates the efficacy of bias mitigation strategies, using the U.S. Treatment Episode Data Set-Discharges (TEDS-D).

Why it matters

The ethical deployment of advanced analytical tools, such as machine learning, in critical healthcare contexts is paramount. Ensuring fairness in predictive models for treatment outcomes mitigates risks of exacerbating existing health disparities and fosters trust in data-driven decision-making systems. This directly impacts the effectiveness and equity of public health interventions and resource allocation.

Key insights

• Machine learning models are employed to predict MOUD retention and identify individuals at risk of prematurely discontinuing treatment.
• The fairness of these predictive ML models across varied patient populations has not been extensively investigated.
• Unexplored fairness raises significant concerns regarding the appropriate application of these models in supporting treatment decisions.
• The study systematically assesses algorithmic fairness in ML models specifically for MOUD retention and discontinuation predictions.
• The research investigates the effectiveness of various bias mitigation techniques.
• The analysis utilizes the cross-sectional Treatment Episode Data Set-Discharges (TEDS-D), encompassing treatment episodes in the U.S.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.22113