了解临床预测的算法公平性,从子组净收益和健康公平性方面来看
Jose Benitez-Aurioles1, Alice Joules2, Irene Brusini2
1Centre for Health Informatics, University of Manchester, Manchester, United Kingdom.
Epidemiology (Cambridge, Mass.)
|January 9, 2026
概括
这项研究引入了一种新的方法,通过扩大净益来评估临床预测模型的公平性. 它有助于确保模型在不同患者子组中改善健康公平.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 医学伦理 医学伦理
背景情况:
- 有关临床预测模型的公平性存在担忧,特别是它们在种族和性别等受保护属性的表现.
- 当前的算法公平性方法可能无意中降低了服务良好的子组的模型性能,而不是改善服务不足的子组.
研究的目的:
- 为评估临床预测模型的公平性提出并展示一个扩展的净益框架.
- 评估模型如何分配好处,影响健康不平等,并为健康公平做出贡献.
主要方法:
- 扩大了净益的概念,以量化和比较不同人口子组的预测模型的临床影响.
- 将这种方法应用于两个案例研究:一种2型糖尿病预后模型和一个肺癌查算法.
主要成果:
- 拟议的方法允许对预测模型如何影响不同子组的细微了解.
- 由于资源限制,证明了健康公平和其他医疗保健系统目标之间的潜在权衡.
结论:
- 通过净收益分配的视角来评估公平性,为模型的临床和社会背景提供了关键的见解.
- 这种方法有助于开发人员创建更好地维护健康公平的模型.
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