评估数据偏差对算法公平性和机器学习模型在长期阿片类药物使用预测中的临床实用性的影响
Behzad Naderalvojoud1, Catherine Curtin2, Steven M Asch1,3
1Department of Medicine, Stanford University, Stanford, CA 94305, United States.
JAMIA open
|October 2, 2025
概括
机器学习模型中的特定人群偏差可能会影响临床效用,即使最初满足了公平性. 评估跨不同患者群体的偏见对于公平的医疗保健福利至关重要.
科学领域:
- 医疗保健 机器学习 机器学习
- 算法公平性 算法公平性
- 临床实用性评估的临床实用性评估
背景情况:
- 机器学习 (ML) 模型在医疗保健中越来越多地使用,这引发了对影响现实世界性能的数据偏差的担忧.
- 现有的公平性框架往往忽视了偏见对通用性和临床实用性的影响,阻碍了安全部署.
研究的目的:
- 将偏见分析扩展到包括临床实用性,弥合公平性评估和临床决策之间的差距.
- 评估一个预测长期阿片类药物使用 (POU) 的模型,以公平性,概括性和临床实用性.
主要方法:
- 使用退伍军人卫生管理局 (VHA) 数据对POU预测模型进行了三阶段评估.
- 方法包括内部/外部验证,模型再培训,子组分析,AUROC,校准,决策曲线分析和标准化净收益.
主要成果:
- POU模型的AUROC从0.74 (内部) 降至0.70 (外部),子组业绩平均为0.69.
- 在VHA数据上进行重新训练,AUROC提高到0.82.
- 临床效用分析揭示了跨值概率的净效益的系统变化,表明了特定于人群的偏差效应.
结论:
- 外部验证和再培训突出显示了各子组的性能和效用转变,尽管最初的内部概括性和公平性.
- 人口特异性偏见显著影响临床效用,需要对不同患者群体的公平利益进行仔细评估.
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