一个公平的机器学习模型来预测系统性红斑狼的爆发
Yongqiu Li1, Lixia Yao2, Yao An Lee3
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, 32611, United States.
JAMIA open
|July 28, 2025
概括
机器学习模型FLAME使用临床数据和健康的社会决定因素 (SDoH) 预测系统性红斑狼 (SLE) 爆发. 它提供了个性化SLE护理的公平和可解释的方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 自身免疫性疾病研究研究
背景情况:
- 系统性红斑狼 (SLE) 不成比例地影响女性和少数群体.
- 预测SLE爆发对于患者的结果至关重要,但往往缺乏整合健康的社会决定因素 (SDoH).
研究的目的:
- 开发FLAME (FLAre机器学习预测SLE),一个可解释和公平的机器学习管道.
- 使用电子健康记录 (EHR) 和上下文级 SDoH 预测 3 个月的 SLE 爆发风险.
主要方法:
- 对28433名SLE患者 (2011-2022) 的回顾性队列研究.
- 整合了675个上下文级SDoH变量与EHR数据.
- 采用了XGBoost和逻辑回归模型,SHapley添加式扩展 (SHAP) 和因果结构学习.
- 使用机会平等指标评估的公平性.
主要成果:
- FLAME模型实现了0.66的AUROC;仅用于临床的模型的AUROC为0.67.
- 由SHAP确定的关键预测因素包括头痛,有机大脑综合征和发烧.
- 因果学习揭示了临床因素和SDoH之间的相互作用;公平性评估显示没有显著的偏见.
结论:
- FLAME为预测SLE爆发提供了一个公平和可解释的方法.
- 该模型为指导临床干预和支持个性化,公平的SLE护理提供了洞察力.
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