评估机器学习预测应急诊所延长等待时间的公平性,使用可解释的eXtreme梯度增强
Hao Wang1, Nethra Sambamoorthi2, Nathan Hoot1
1Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, United States of America.
PLOS digital health
|March 20, 2025
概括
人工智能 (AI) 和机器学习 (ML) 模型可以预测急诊室 (ED) 的等待时间. 然而,公平性评估显示患者人口统计学预测的差异,需要在临床使用前进行仔细评估.
科学领域:
- 临床信息学 临床信息学
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的人工智能
背景情况:
- 在临床实践中实施人工智能 (AI) 和机器学习 (ML) 需要严格的性能和质量评估.
- 预测急诊室 (ED) 患者等待时间对于资源配置和患者流量管理至关重要.
研究的目的:
- 用ML来预测ED中的患者等待时间.
- 评估ML模型在预测延长等待时间方面的性能和公平性,特别是检查种族差异.
- 评估不同患者群体的ML模型的准确性和潜在偏差.
主要方法:
- 一项回顾性观察性研究包括173,856名成年ED访问 (ESI3级).
- 使用极端梯度增强 (XGBoost) 来预测长时间的等待时间 (≥30分钟).
- 用准确性,回忆力,精度,F1分数和假负率 (FNR) 来评估模型性能. 莎普利添加式解释 (SHAP) 用于解释. 在性别,种族/种族和保险状况方面评估了公平性.
主要成果:
- 几乎一半 (48.43%) 的ED访问经历了长时间的等待时间.
- 该XGBoost模型实现了适度的预测性能 (AUROC=0.81).
- 公平性评估揭示了人口群体间虚假负率 (FNR) 的差异,女性,西班牙裔和无保险患者的FNR较低.
结论:
- XGBoost模型在预测延长ED等待时间方面表现出可接受的性能.
- 在性别,种族/种族和保险状况方面,预测公平性存在显著差异.
- 彻底的性能和公平性评估对于负责任的ML模型临床实施至关重要.
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