通过联合机器学习从EHR安全提取个人信息
Mohamed El Azzouzi1, Reda Bellafqira2, Gouenou Coatrieux2
1Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France.
Studies in health technology and informatics
|August 23, 2024
概括
联合学习 (FL) 增强了从法国电子健康记录 (EHR) 中安全提取个人身份信息 (PII). FL模型的性能优于单个方法,确保数据保密,同时实现与集中方法相比的高性能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 从电子健康记录 (EHR) 中提取个人身份信息 (PII) 带来了重大隐私和安全风险.
- 现有的方法往往难以平衡数据实用性与强有力的保密措施.
研究的目的:
- 评估联邦学习 (FL) 的有效性,以便从法国电子健康记录中安全地提取PII.
- 为了比较FL模型的性能与单独的,非联合的模型.
主要方法:
- 一个模拟涉及20家医院使用多语言BERT模型在联邦学习框架内.
- 医院之间协同训练的联合模型与仅在本地数据上训练的单个模型的比较.
主要成果:
- 与单个模型相比,联合学习模型显示出更高的性能.
- 全球FL模型实现了75.7%的F1得分,接近集中式方法 (78.5%) 的表现.
- 在整个PII提取过程中,FL成功地保持了数据保密性.
结论:
- 联合学习为从敏感的EHR数据中安全有效地提取PII提供了一个有前途的解决方案.
- FL促进了协作健康数据分析,同时缓解了隐私问题.
- 该研究倡导在健康数据研究和应用中更广泛地采用FL.
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