评估30天医院再入院模型中的算法偏差:回顾性分析
H Echo Wang1, Jonathan P Weiner1,2, Suchi Saria3
1Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Journal of medical Internet research
|February 29, 2024
概括
医疗保健中的算法偏见可能会加剧差距. 公平度指标可以检测不平等的模型性能,但解释它们需要仔细考虑数据和卫生系统因素.
科学领域:
- 医疗信息学 医疗信息学
- 健康 公平 卫生 公平
- 预测建模预测建模
背景情况:
- 医疗保健中的预测算法可能会因算法偏差而加剧现有的差异.
- 现有的偏见测量公平度指标在现实世界中应用有限.
研究的目的:
- 在常见的30天医院再入院模型中评估算法偏差.
- 评估所选公平度指标的实用性和可解释性.
主要方法:
- 对1060万住院患者出院的回顾性分析 (马里兰州和佛罗里达州,2016-2019年).
- 评估的LACE指数,修改的HOSPITAL得分和修改的CMS再接收措施 (现状和再培训).
- 在种族和收入群体中评估预测性表现和偏差 (虚假负率,虚假阳性率,0-1损失,通用指数).
主要成果:
- 重新训练的CMS模型显示出最好的预测性能;修改后的HOSPITAL得分具有最好的校准.
- 校准有利于白人和高收入群体;AUC在黑人群体中更高/相似.
- 重新训练的CMS和修改的HOSPITAL得分在马里兰州表现出最低的偏见;修改的HOSPITAL得分在佛罗里达州显示出最低的种族偏见.
- 在白人/高收入群体中观察到更高的虚假阴性率;在黑人/低收入群体中观察到更高的虚假阳性率和0-1损失.
- 模型在不同背景和不同人群中展示了异构的算法偏差.
结论:
- 公平度指标可以检测不同的模型性能,但需要谨慎的解释.
- 单靠统计偏见的措施可能会掩盖健康差异的根本原因.
- 解决偏见需要考虑不完美的数据,分析框架和卫生系统;公平度量是关键的第一步.
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