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从多个站点的垂直分布数据中学习:对于Cox比例危险模型的高效隐私保护算法,具有可变选择
Guanhong Miao1, Lei Yu2, Jingyun Yang2
1Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA; Center for Genetic Epidemiology and Bioinformatics, University of Florida, Gainesville, FL, USA; Department of Biostatistics, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
Journal of biomedical informatics
|December 24, 2023
概括
本研究为规范化的考克斯模型引入了一种保护隐私的无损分布式算法,使得在联合学习环境中准确分析时间到事件数据而不会损害患者数据.
科学领域:
- 分布式计算 分布式计算
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 联合学习可以在没有直接数据共享的情况下进行协作模型培训.
- 分布式数据对传统的统计建模提出了挑战,特别是对于生存分析.
- 保护隐私的方法对于处理敏感健康数据至关重要.
研究的目的:
- 开发一个无损的分布式算法,用于调整的Cox比例危险模型,具有变量选择.
- 支持生存分析中垂直分布的数据的联合学习.
- 为了实现准确的时间到事件数据建模,而不会损害患者的隐私.
主要方法:
- 建议基于循环坐标下降的新型分布式算法.
- 中间统计数据在本地计算,并交换模型更新,保护数据隐私.
- 该算法使用模拟和现实世界阿尔茨海默氏症痴呆风险预测数据 (ROSMAP) 进行了评估.
主要成果:
- 分布式算法实现了对时间到事件数据的隐私保护变量选择,与集中式方法相比,没有精度损失.
- 模拟证实了分析高维数据集的高效率.
- 现实世界分析显示,与现有的隐私保护模型相比,阿尔茨海默氏症痴呆风险预测准确度和计算效率有所提高.
结论:
- 开发的算法是无损的,保护隐私,并且有效地将规范化的Cox模型与分布式数据相匹配.
- 它为分布式时间到事件数据建模提供了一个实用的解决方案.
- 这种方法促进了协作研究,同时保持了数据安全性和分析完整性.
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