为公平的联合模型进行分布式交叉学习 - - 基于加利福尼亚州五家医院的数据进行隐私保护预测
Tsung-Ting Kuo1,2,3, Rodney A Gabriel4,5,6, Jejo Koola4,5
1Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, Connecticut, United States of America. tsung-ting.kuo@yale.edu.
Nature communications
|February 5, 2025
概括
公平联合模型的分布式交叉学习 (D-CLEF) 使医疗保健系统能够在不损害隐私的情况下,使用各种患者数据进行医学预测分析. 这种方法与集中式模型的性能相匹配,同时保护敏感的患者信息.
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:
- 医学预测分析增强了质量改善,临床研究和患者护理.
- 整合多种患者记录 (水平或垂直) 可以提高预测模型的性能.
- 现有的数据集成方法往往会危及患者的隐私.
研究的目的:
- 引入用于保护隐私的医疗数据集成的公平联合模型 (D-CLEF) 的分布式交叉学习.
- 评估D-CLEF与集中式,分离式和联合式学习方法的性能.
- 在现实世界医疗保健场景中展示D-CLEF的实用性.
主要方法:
- 开发了D-CLEF以整合水平或垂直分区的数据,而无需传播患者级记录.
- 在水平和垂直分区场景中比较D-CLEF与集中式,隔离式和联合式学习.
- 利用多中心数据集,包括COVID-19,手术和心脏病数据.
主要成果:
- 在不同的数据分区策略中,D-CLEF的性能与集中解决方案非常接近.
- D-CLEF显著优于孤立学习方法.
- D-CLEF实现了与联合学习相当的性能,在同步时间方面进行了权衡.
结论:
- 在不影响患者数据隐私的情况下,D-CLEF为协作医疗分析提供了可行的解决方案.
- D-CLEF框架促进了跨机构的安全数据共享和模型构建.
- 对于寻求利用集体数据的医疗保健系统,D-CLEF充当了一个有前途的加速器.
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