公平的患者模型:从电子健康记录中学到的患者代表性的缓解偏见
Sonish Sivarajkumar1, Yufei Huang2, Yanshan Wang3
1Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States.
Journal of biomedical informatics
|November 23, 2023
概括
一个新的公平患者模型 (FPM) 使用加权损失函数从电子健康记录 (EHR) 创建公正的患者表示. 这种方法提高了公平性,而不会牺牲临床结果的预测准确性.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 健康 公平 卫生 公平
背景情况:
- 电子健康记录 (EHR) 包含有价值的患者数据,但可以延续偏见.
- 在EHR上训练的深度代表性学习模型可能会在不同患者群体中表现出不公平.
- 确保人工智能模型的公平性对于公平的医疗保健应用至关重要.
研究的目的:
- 开发一种新的加权损失函数,用于从EHR中预先培训公平和公正的患者表现.
- 提高医疗保健中深度代表性学习模型的公平性.
- 评估拟议的公平患者模式 (FPM) 的有效性.
主要方法:
- 定义了一种新的加权损失函数,以平衡深度表示学习中的患者群体和特征.
- 开发了包含加权损失函数的公平患者模型 (FPM).
- 将FPM应用于MIMIC-III数据集中的34,739名患者,用于临床结果预测.
主要成果:
- 与基线模型相比,FPM在公平度指标 (人口平价,机会平等差异,均等赔率比率) 上表现出色.
- 与基线模型相比,FPM实现了可比的预测准确性 (平均0.7912).
- 功能分析表明,FPM从临床特征中获取了更丰富的信息.
结论:
- FPM提供了一种新的方法,可以从EHR数据中预先培训公平和公正的患者表现.
- 学习的表征适用于各种下游医疗保健任务.
- 这种方法可以扩展到需要公平考虑的其他领域.
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