在多机构数据中隐私保护术后死亡率的预测:开发和可用性研究
Jungyo Suh1, Garam Lee2, Jung Woo Kim2
1Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
JMIR medical informatics
|July 5, 2024
概括
同型加密 (HE) 允许安全的多机构数据集成,显著提高医学研究模型的预测能力. 这种保护隐私的方法增强了医院间的数据分析,以获得更好的健康结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗数据安全 医疗数据安全
- 计算隐私 计算机隐私
背景情况:
- 由于隐私问题,监管障碍阻碍了医疗数据交换.
- 同型加密 (HE) 允许对加密数据进行计算,增强隐私.
- 高等技术为安全的多机构数据集成提供了解决方案.
研究的目的:
- 评估将加密的多机构数据与高等教育集成是否提高了研究预测能力.
- 评估跨机构数据整合的可行性,使用HE.
- 为了确定最佳的医院数据集大小,用于增强的预测模型.
主要方法:
- 利用3个机构的341,007名接受非心脏手术的成年人的数据.
- 开发了一个安全的后勤回归模型,使用HE预测30天住院死亡率.
- 对比单一机构的纯文本数据与多机构的加密数据的预测性能.
主要成果:
- 所有3个机构的HE模型显示了接收器运行特征曲线 (0.941) 下的最高面积.
- 将阿桑医疗中心 (AMC) 和首尔国立大学医院 (SNUH) 的数据结合起来,产生了最佳的精度-召回曲线下的区域 (0.132).
- 将Ewha Womans大学医疗中心和SNUH数据纳入AMC数据,提高了个人机构的预测能力.
结论:
- 用HE处理的多机构数据集优于用于预测模型的单机构数据集.
- 一个适应的HE模型即使在较小,有限的数据集上也表现出有效性.
- HE促进了分布式健康数据的隐私保护整合,以实现强大的预测分析.
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