对于具有成本效益的参与者选择异质电子健康记录系统的联合学习
Jiyoun Kim1, Junu Kim1, Kyunghoon Hur1
1KAIST, Kim Jaechul Graduate School of AI, Daejeon, 34141, Republic of Korea.
Scientific reports
|February 1, 2026
概括
针对临床预测模型的联合学习面临电子健康记录 (EHR) 系统兼容性和成本的挑战. EHRFL为特定机构的模型提供了一个具有成本效益的框架,减少了成本和时间.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 电子健康记录 (EHR) 正在增长,为改进的临床预测模型提供了潜力.
- 联合学习 (FL) 允许多机构模型培训,同时保护患者的隐私和监管合规性.
- 医疗机构在构建FL模型方面面临挑战,原因是异质的EHR系统和高计算成本.
研究的目的:
- 提出EHRFL,一个联合的学习框架,用于使用EHR数据的成本效益高,主机特定的预测模型.
- 在不需要昂贵的数据标准化的情况下,解决EHR系统中的跨机构兼容性问题.
- 通过减少参与者数量,同时保持模型性能来管理FL成本.
主要方法:
- EHRFL使用基于文本的EHR建模来实现跨机构的兼容性.
- 使用基于平均患者嵌入相似性的差异性私人参与者选择策略.
- 该框架旨在在不降低性能的情况下降低FL成本和参与者数量.
主要成果:
- 对开源EHR数据集的实验证明了EHRFL组件的有效性.
- 基于文本的EHR建模增强了异构系统之间的兼容性.
- 参与者选择策略有效地降低了成本和参与者数量,同时保持了模型准确性.
结论:
- 在预算限制下,EHRFL使医疗机构能够构建具有成本效益的,特定于机构的预测模型.
- 该框架减少了使用EHR数据开发FL模型的成本和时间.
- 在联合学习环境中,EHRFL促进了兼容的建模和保护隐私的参与者选择.
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