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Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
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fairnessbias mitigationpredictive modelinghealthcare
2609.22113
Builder Relevance
1h ago70%
Abstract
This study assesses algorithmic fairness in ML models for predicting retention in MOUD and identifies bias mitigation techniques.
Reality Card
Core Claim
ML models for predicting MOUD retention exhibit subgroup-level performance gaps, which can be reduced but not fully eliminated by bias mitigation techniques.
Method / Result
Four ML models were trained and evaluated, revealing subgroup-level performance gaps despite acceptable overall predictive performance.
Limitations
The study highlights that bias mitigation can reduce performance gaps but may introduce trade-offs, raising concerns about reproducibility in diverse patient populations.
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