评估隐私和实用性在合成EHR数据生成中,用于检测不良药物事件
1Department of Computer and Systems Sciences (DSV), Stockholm University, Sweden.
Studies in health technology and informatics
|October 3, 2025
概括
合成数据库 (SDV) 工具可以生成电子健康记录 (EHR) 数据用于药物不良事件 (ADE) 检测. 模型选择影响性能,TVAE对数据大小和平衡敏感,GaussianCopula提供稳定的实用性和隐私,CTGAN显示不一致的结果.
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) 包含有价值的信息,用于检测药物不良事件 (ADEs).
- 生成现实的合成EHR数据对于研究和开发至关重要,同时保护患者的隐私.
- 合成数据库 (SDV) 工具为合成数据生成提供了各种模型.
研究的目的:
- 评估不同SDV模型在生成用于ADE检测的合成EHR数据方面的有效性.
- 评估GaussianCopula,CTGAN和TVAE模型生成的合成数据的实用性,保真性和隐私性.
- 根据数据集特征和应用要求,确定最佳的合成数据生成策略.
主要方法:
- 使用了三种SDV模型:高斯式Copula,条件表式生成对抗网络 (CTGAN) 和表式变异自编码器 (TVAE).
- 在实验中使用了一个结构化的瑞典EHR数据集.
- 使用SynthEval指标评估的合成数据以及使用随机森林分类器的"合成训练,实验测试" (TSTR) 方法.
主要成果:
- TVAE的表现取决于数据集大小和类平衡,大数据集的表现有所改善.
- 高斯科普拉表现出稳定的实用性和增强的隐私,但忠诚度较低.
- CTGAN产生了现实的数据,但在TSTR评估中显示了可变的性能.
结论:
- 合成数据生成模型的选择显著影响了ADE检测性能.
- 选择模型时应考虑医疗保健应用的具体需求和可用的数据集的特点.
- 在生成合成EHR数据时,平衡数据实用性,真实性和隐私是必不可少的.
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