评估围产期情绪和焦虑障碍的偏见减轻的预测模型
Emily F Wong1, Anil K Saini1, Eynav E Accortt2
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California.
JAMA network open
|December 3, 2024
概括
对于围产期情绪和焦虑障碍 (PMADs) 查的机器学习模型显示出适度的表现,但重新权衡数据减少了差异. 评估模型偏差对于尽量减少PMAD诊断和治疗中的健康不平等至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 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:
- 使用机器学习 (ML) 进行围产期情绪和焦虑障碍 (PMAD) 的查,需要在电子健康记录 (EHR) 中解决临床偏见.
- 严格评估ML模型的性能是必不可少的,以确保所有患者群体的公平结果.
研究的目的:
- 为了减轻PMAD的预测模型中的偏差,这些PMAD是根据普遍可用的EHR数据进行训练的.
- 评估数据重权对模型公平性和性能的影响.
主要方法:
- 一项诊断研究利用了Cedars-Sinai医疗中心19430名分娩患者 (2020-2023) 的数据.
- 训练了后勤回归,随机森林和极端梯度提升模型,以预测PHQ-9和EPDS得分,无论数据是否重权.
- 用AUROC,人口平价 (DP) 和跨种族和族群虚假负数差异来评估模型性能.
主要成果:
- 种族和少数民族患者更有可能对PMADs进行查 (PHQ-9 OR 1.47,EPDS OR 1.38).
- 基线模型表现出显著的偏差,在非西班牙裔白人患者中预测的患病率更高,假阴性率更低.
- 数据重新权衡大大减少了人口平价差异 (DP从0.238降至0.022) 和假负率 (从-0.184降至0.018).
结论:
- 在EHR上训练的PMADs的临床预测模型具有适度的性能,并可能加剧现有的健康差异.
- 数据重权是缓解偏差和减少基于ML的PMAD查中的健康不平等的关键技术.
- 研究人员和临床医生必须优先考虑模型设计和偏见评估,以确保为所有患者提供公平的护理.
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