临床机器学习模型中的社会人口统计偏差:对算法偏差实例和机制的范围审查
Michael Colacci1, Yu Qing Huang1, Gemma Postill2
1St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
大多数临床机器学习 (ML) 模型显示偏差,不成比例地影响弱势群体. 偏差评估对于所有ML模型至关重要,以确保健康公平,防止加剧健康差异.
科学领域:
- 健康 公平 卫生 公平
- 医疗保健中的人工智能
- 算法偏见是一种算法偏见.
背景情况:
- 临床机器学习 (ML) 模型可以延续和加剧健康差异.
- 了解临床ML偏差的程度,受影响群体和机制至关重要.
研究的目的:
- 检查临床ML模型中偏差的情况.
- 确定经历偏见的社会人口学子组.
- 确定偏差引入的报告机制.
主要方法:
- 对多个数据库 (MEDLINE,EMBASE,PsycINFO,Web of Science) 的综合范围审查.
- 在临床护理的ML算法中包括评估偏差的研究.
- 遵守乔安娜·布里格斯研究所的指导方针和用于范围审查的PRISMA扩展.
主要成果:
- 在临床ML模型上的760篇文章中,有91篇符合偏差评估的纳入标准.
- 在74.7%的研究中报告了偏见.
- 种族,性别和年龄是最常被评估的;社会经济劣势最常与偏见有关 (87%的研究).
结论:
- 大多数评估的临床ML模型都表现出与社会人口统计学因素相关的偏差.
- 目前的偏见评估是有限的,经常忽视交叉因素.
- 对于所有临床ML模型来说,全面的偏差评估对于促进健康公平至关重要.
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