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arXiv Machine Learning By Tongnian Wang, Carolina Vivas-Valencia, Cici Bauer, Yanmin Gong, Kim-Kwang Raymond Choo, Yuanxiong Guo

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

Read the original on arXiv Machine Learning →

The paper evaluates machine learning models that predict retention and premature discontinuation in medication for opioid use disorder (MOUD). Using the Treatment Episode Data Set-Discharges (TEDS‑D) from 2015‑2019, the authors trained four models and examined overall performance as well as subgroup error rates by race, ethnicity, age, and sex. They also tested bias‑mitigation techniques, finding that these can reduce but not eliminate performance gaps without harming predictive accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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