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基于电子健康记录的运输转移学习:用于检测治疗差异

Wanxin Li¹, Saad Ahmed², Yongjin P Park^3,4,5

¹Department of Computer Science, University of British Columbia, Vancouver V6T 1Z4, Canada.

Journal of the American Medical Informatics Association : JAMIA

|September 3, 2025

概括

我们开发了一种使用电子健康记录 (EHR) 进行最佳传输的新方法,

科学领域:

计算医疗信息学
医疗保健中的机器学习
医疗服务研究

背景情况:

由于固有的偏见,来自不同群体的电子健康记录 (EHR) 在数据共享和模型可转移方面存在挑战.
现有的方法难以在不同患者群体之间有效地传递知识,限制了更广泛的应用.
在EHR数据中量化治疗差异需要能够处理人口异质性的强有力的方法.

研究的目的:

为不同人口群体设计有效的电子健康记录数据转移方法.
为转移模型的适用性提供可计算的保证.
使用电子健康记录数据量化待遇差异.

主要方法:

开发了以运输为基础的最佳转移学习 (OTTEHR) 框架.
组合特征嵌入和不平衡的最佳传输 (OT) 进行域调整.
使用MIMIC-III和MIMIC-IV数据库,按ICD代码和标签进行细分,用于方法验证.

主要成果:

使用瓦瑟斯坦距离,不平衡性和标记分歧来得出概括错误的理论边界.
与标准转移学习方法相比,OTTEHR显示出更高的准确性和计算效率.
在不同保险计划的患者群体中发现了住院时间的显著差异.

结论:

通过使用最佳运输理论,OTTEHR框架有效地比较不同电子健康记录数据群的统计模型.
患者群体之间的量化治疗差异,为临床决策提供潜在支持.
未来的工作包括将OTTEHR扩展到更广泛的任务和纳入半监督学习.

关键词:

电子健康记录最好的运输转移学习对待的差异

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