保护隐私的患者集群为个性化的联合学习提供了个性化的联合学习
Ahmed Elhussein1, Gamze Gürsoy2
1Department of Biomedical Informatics, Columbia University, New York Genome Center, New York City, NY, U.S.A.
Proceedings of machine learning research
|September 6, 2024
概括
维护隐私的基于社区的联合机器学习 (PCBFL) 安全地聚集患者,以便在医院中获得更好的模型培训. 这种新的方法通过克服医疗保健中的数据分布挑战,提高了死亡率预测的准确性.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 培养没有中央数据共享的模型,但在医疗保健中常见的非相同独立分布 (非IID) 数据方面扎.
- 个性化FL和集群FL的变体旨在通过将模型定制为特定站点的分布来处理非IID数据.
- 现有的集群FL方法面临隐私风险或性能下降,原因是患者集群期间的数据聚合.
研究的目的:
- 引入保护隐私的基于社区的联合机器学习 (PCBFL),这是一个新的框架,用于在佛罗里达州保护隐私的患者集群.
- 通过安全多方计算实现使用患者级数据的安全聚类,而不会损害隐私.
- 评估PCBFL在改善医疗应用的联合模型性能方面的有效性.
主要方法:
- 开发了PCBFL,一种集群FL变体,利用安全多方计算来计算跨机构的患者级别相似性得分.
- 实施PCBFL以训练使用eICU数据集中的20个站点数据的联合死亡率预测模型.
- 将PCBFL的性能与传统和现有的集群FL框架进行了比较.
主要成果:
- PCBFL成功形成了临床相关的患者队列 (低,中,高风险).
- 该框架显示了与基线方法相比显著的性能改进.
- 实现了4.3%的平均曲线下面积 (AUC) 和7.8%的平均精度回忆曲线 (AUPRC) 的改善.
结论:
- PCBFL提供了一个强大的解决方案,用于在联合学习环境中保护隐私的患者集群.
- 拟议的方法有效地解决了医疗保健中的非IID数据挑战,提高了模型的准确性.
- PCBFL有助于创建有意义的患者子组,从而在医疗预测任务中获得优越的联合模型性能.
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