利用社区层面的信息来提高预测产前抑郁症的模型公平性
medRxiv : the preprint server for health sciences
|June 4, 2025
概括
将社区数据整合到围产期抑郁症 (PND) 预测模型中,可以提高公平性,并确定影响跨种族群体偏见的关键因素. 这种方法有助于减少母亲心理健康护理方面的差异.
科学领域:
- 公共卫生 公共卫生
- 医疗保健中的机器学习
- 健康差距 研究 研究 研究 研究
背景情况:
- 围产期抑郁症 (PND) 影响10-20%的孕妇,在患病率,查和治疗方面表现出显著的种族差异.
- 众所周知,社区层面的因素会影响PND风险,特别是在有色人种女性中,但在使用电子医疗记录 (EMR) 的机器学习模型中经常被遗漏.
研究的目的:
- 评估将社区级数据与EMR集成是否提高了PND预测模型中的公平性.
- 确定影响不同种族和种族群体模型偏见的特定社区因素.
主要方法:
- 2010-2019年6137名孕妇 (58%非西班牙裔黑人,10%非西班牙裔白人,28%西班牙裔) 的研究.
- 合并了来自芝加哥健康图谱的125个社区因素,并基于住宅位置的61个EMR特征.
- 评估模型性能 (ROCAUC,PRAUC) 和公平性 (不平等的影响,平等机会差异,均等赔率),使用Shapley值来确定特征的重要性.
主要成果:
- 与仅使用EMR模型相比,结合邻近数据的模型显示了适度的预测性能 (ROCAUC:NHB 55%,NHW 57%,H 58%) 和显著改善的公平性指标 (p<0.05).
- 像自杀死亡率和安全率这样的邻里因素有助于减少预测偏差.
- 非西班牙裔黑人妇女在PND风险和邻居变量之间显示出更强的相关性;因素在不同群体之间产生了差异性的影响,在西班牙裔妇女中减少了偏见,而在NHB妇女中增加了偏见.
结论:
- 将社区信息集成到PND预测模型中可以提高公平性,而不会影响预测能力.
- 邻居因素的差异性影响强调了在临床风险评估中考虑社会环境背景的必要性,以减轻产前抑郁症护理方面的差异.
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