通过扩散模型可靠地生成保护隐私的合成电子健康记录时间序列
Muhang Tian1, Bernie Chen2, Allan Guo1
1Department of Computer Science, Duke University, Durham, NC 27708, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 2, 2024
概括
这项研究引入了一种新的扩散模型,用于生成现实的,保护隐私的合成电子健康记录 (EHR). 该方法提高了医疗研究的数据实用性,同时减轻了与传统身份删除技术相关的隐私风险.
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:
- 电子健康记录 (EHR) 对医疗数据分析有价值,但面临隐私限制.
- 现有的去识别方法有缺陷,导致隐私泄露和有限的数据可用性.
- 推进医学研究需要克服EHR访问障碍.
研究的目的:
- 开发一种方法来生成现实的和保护隐私的合成EHR时间序列数据.
- 为了解决当前EHR去识别和研究数据可用性的局限性.
- 通过改善数据访问,促进医疗保健中的机器学习应用.
主要方法:
- 利用无声扩散概率模型生成合成EHR时间序列.
- 在多个医疗保健数据库 (MIMIC-III,MIMIC-IV,eICU) 和非EHR数据集上进行了实验.
- 将拟议的方法与八种现有的非识别和数据生成技术进行了比较.
主要成果:
- 拟议的扩散模型在数据保真方面明显优于现有方法.
- 生成的合成数据的分辨准确度较低,表明隐私风险降低.
- 与基线方法相比,该方法需要较少的培训工作.
结论:
- 扩散模型产生现实的合成电子健康记录,保护患者的隐私.
- 这种方法可以缓解医疗保健中的数据可用性问题,减少EHR访问的障碍.
- 该方法支持用于健康研究的机器学习的进步.
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