医疗数据共享和合成临床数据生成 - - 最大限度地利用生物医学资源,最大限度地减少参与者重新识别的风险
Simeone Marino1, Ruth Cassidy2, Joseph Nanni2
1Statistics Online Computational Resource (SOCR), University of Michigan, Ann Arbor, MI, USA.
NPJ digital medicine
|August 16, 2025
概括
从电子健康记录 (EHR) 和可穿戴数据中生成保护隐私的数字双胞胎至关重要. DataSifter提供强大的隐私保护,同时保持数据实用性,以确保生物医学资源的安全共享.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据 隐私 数据 隐私 数据
- 健康 数据科学 数据科学
背景情况:
- 分享敏感的电子健康记录 (EHR) 和可穿戴数据带来了重大重新识别风险.
- 现有的方法努力平衡复杂数据集的数据实用性和强大的隐私保护.
研究的目的:
- 引入和评估一个端到端的管道,用于生成保护隐私的"数字双胞胎"数据集.
- 为了比较DataSifter和Synthetic Data Vault (SDV) 方法在掩盖EHR和可穿戴数据方面的有效性.
- 为了在不同的掩盖级别中对隐私-实用性权衡进行比较.
主要方法:
- 使用了DataSifter和合成数据库 (SDV) 具有不同的隐蔽级别 (DataSifter:小,中,大;SDV:CTGAN,高斯方圆).
- 从3029名参与者的电子健康记录和Apple Watch数据生成了数字双胞胎数据集.
- 通过统计准确性和机器学习性能评估实用性;使用重新识别风险和检测概率指标评估隐私.
主要成果:
- 与SDV相比,Datasifter数字双胞胎的隐私保护率最高 (0.83),与SDV相比显示出更高的隐私保护.
- DataSifter保持了显著的统计和预测信号保存 (83.1%的CI重叠在回归模型中).
- 虽然较高的模糊影响了机器学习性能,但整体数据实用性通常被保留,特别是在纵向数据中.
结论:
- 数字双胞胎数据集对于安全的生物医学数据共享至关重要.
- DataSifter提供了可适应的隐私-实用性权衡,优于SDV,特别是在纵向数据方面.
- 开发的管道支持安全共享复杂的健康数据,同时最大限度地降低重新识别风险.
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