面向阿片类药物使用障碍治疗保留与提前终止预测的机器学习模型公平性研究

中文摘要 AI 辅助

阿片类药物使用障碍（MOUD）药物治疗中持续存在的低保留率和低完成率，推动了使用机器学习（ML）模型来预测保留情况并识别有提前终止风险的患者。然而，这些模型在不同患者群体中的公平性在很大程度上仍未得到探索，这引发了对其在治疗决策支持中应用的担忧。本研究系统评估了用于预测MOUD保留和提前终止的机器学习模型中的算法公平性，并调查了偏差缓解技术的有效性。利用横断面治疗事件数据集-出院记录（TEDS-D），该数据集包含2015年至2019年间美国出院个体的治疗事件，我们训练了四个机器学习模型，以预测接受门诊MOUD治疗的个体中提前终止治疗和保留超过180天的情况。我们评估了按种族、民族、年龄和性别定义的患者亚组的整体性能和亚组级错误率，并辅以模型解释分析。我们进一步评估了偏差缓解技术及其对公平性和预测性能的影响。我们的研究结果表明，用于MOUD结果预测的机器学习模型即使在整体预测性能看似可接受的情况下，也可能表现出亚组级性能差距，而偏差缓解可以减少但不能完全消除这些差距，且不会产生权衡。通过展示公平性感知评估和亚组性能透明报告的重要性，本研究为在MOUD治疗环境中负责任且情境敏感地使用机器学习模型进行风险分层和护理优先级排序提供了实用见解。

英文摘要

Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predict retention and identify patients at risk of premature discontinuation. However, the fairness of these models across patient populations remains largely unexplored, raising concerns about their application in treatment decision support. This study systematically assesses algorithmic fairness in ML models for predicting MOUD retention and premature discontinuation and investigates the effectiveness of bias mitigation techniques. Using the cross-sectional Treatment Episode Data Set-Discharges (TEDS-D), which includes treatment episodes for individuals in the U.S. discharged between 2015 and 2019, we trained four ML models to predict premature treatment discontinuation and retention beyond 180 days among individuals receiving outpatient MOUD. We evaluated overall performance and subgroup-level error rates across patient subgroups defined by race, ethnicity, age, and sex, complemented by model explanation analyses. We further assessed bias mitigation techniques and their effects on both fairness and predictive performance. Our findings demonstrate that ML models for MOUD outcome prediction can exhibit subgroup-level performance gaps even when overall predictive performance appears acceptable and that bias mitigation can reduce, but not fully eliminate, these gaps without trade-offs. By demonstrating the importance of fairness-aware evaluation and transparent reporting of subgroup performance, this study provides practical insights for the responsible and context-sensitive use of ML models for risk stratification and care prioritization in MOUD treatment settings.

发表机构

• Gary W. Rollins College of Business, The University of Tennessee at Chattanooga（田纳西大学查塔努加分校加里·W·罗林斯商学院）
• The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
• The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
• Texas A&M University（德克萨斯A&M大学）

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