低出生体重预测模型中的公平性:排除种族/民族的含义
Clare C Brown1, Michael Thomsen2, Benjamin C Amick3
1Department of Health Policy and Management, Fay W Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 W Markham St Slot #820-12, Little Rock, AR, 72205, USA. ccbrown@uams.edu.
Journal of racial and ethnic health disparities
|January 29, 2025
概括
算法模型预测低出生体重,当种族被排除在外时,黑人个体表现不佳. 种族盲模型可以通过减少对弱势群体的资源分配来加剧健康不平等.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 在公共卫生预测建模中,算法公平性至关重要.
- 低出生体重 (LBW) 是婴儿健康的重要指标,需要准确的预测.
研究的目的:
- 在LBW预测模型中评估算法公平性.
- 评估包括或排除种族/民族信息对模型性能的影响.
主要方法:
- 从阿肯色州所有付款人索赔数据库 (2013-2021) 分析保险索赔和出生证明.
- 开发和评估四个LBW预测模型 (物流,弹性网,线性差异分析,梯度增强机) 有或没有种族/种族数据.
- 使用AUC,校准,灵敏度和不同种族/族群的特异性来评估模型性能.
主要成果:
- 接收器运行特征曲线 (AUC) 下的面积在黑人和亚洲人群中较低,而在整个模型中与白人人群相比.
- 梯度增强机器模型显示,黑人 (0.718) 和亚裔 (0.655) 种群的AUC较低,而白人 (0.764).
- 排除种族/种族数据导致敏感性降低和校准较弱,表明黑人个体的预测不足.
结论:
- 种族盲 LBW 预测模型导致黑人人口的预测不足和性能降低.
- 预测不足可能导致资源分配不公平,可能加剧围产期健康不平等.
- 人口健康计划必须仔细考虑算法公平性及其对资源分配决策的影响.
关键词:
公平性;算法公平性;低出生体重更多相关视频
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