电子健康记录安全:生成高保真性和保护隐私的合成电子健康记录
Jinsung Yoon1, Michel Mizrahi2, Nahid Farhady Ghalaty2
1Google Cloud, 1155 Borregas Ave, Sunnyvale, CA, USA. jinsungyoon@google.com.
NPJ digital medicine
|August 11, 2023
概括
使用EHR-Safe生成现实的合成电子健康记录 (EHR) 数据可以提高数据隐私. 这一框架保留了分析数据的实用性,同时保护了敏感的患者信息.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 隐私问题是分享敏感电子健康记录 (EHR) 的主要障碍.
- 这种数据共享限制阻碍了数据分析和机器学习 (ML) 的进步.
- 合成数据生成为隐私挑战提供了一个有希望的解决方案.
研究的目的:
- 提出EHR-Safe,一个生成建模框架,用于创建现实的和保护隐私的合成EHR数据.
- 解决现实世界EHR数据的复杂性,包括异质性,稀疏性,混合数据类型和可变序列长度.
主要方法:
- EHR-Safe采用一个两阶段的生成模型.
- 该模型将顺序编码器-解码器网络与生成对抗网络集成在一起.
- 该框架专门设计用于处理异质,稀疏和时间变化的EHR数据.
主要成果:
- EHR-Safe生成高保真度的合成EHR数据,几乎与真实数据相同.
- 在合成数据上训练的模型与在真实数据上训练的模型相比,在3%的精度差距内实现性能.
- 该框架在实际隐私指标上显示了近乎理想的性能.
结论:
- EHR-Safe有效地生成现实的合成EHR数据,保持数据的实用性.
- 拟议的框架大大提高了对敏感健康信息的隐私保护.
- 这种方法促进了安全的数据分析和医疗保健中的ML创新.
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