对医疗合成数据中的隐私和实用性指标进行范围审查
Bayrem Kaabachi1, Jérémie Despraz2, Thierry Meurers3
1Biomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland. mohamed-beyrem.kaabachi@chuv.ch.
NPJ digital medicine
|January 27, 2025
概括
评估合成健康数据需要标准化的方法. 目前的方法缺乏关于最佳隐私和实用性评估的共识,这阻碍了这种数据共享解决方案的更广泛采用.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 隐私工程 隐私工程 隐私工程
背景情况:
- 合成数据为健康数据共享提供了一个保护隐私的替代方案.
- 缺乏标准化的评估方法阻碍了合成健康数据的采用.
研究的目的:
- 审查和系统化目前评估合成健康数据的方法.
- 在评估合成健康数据的隐私和实用性方面发现差距和挑战.
主要方法:
- 综合数据评估技术的综合文献综述.
- 方法的系统化,重点关注隐私和公用事业指标.
- 对现有关于综合健康数据评估研究的分析.
主要成果:
- 为了评估合成数据的实用性,存在多种方法,但没有一个单一的最佳方法达成一致.
- 大多数研究都忽略了隐私评估;当进行时,风险往往被低估.
- 在研究中的评估方法中存在显著的异质性.
结论:
- 需要一个标准化的框架来评估合成的健康数据.
- 需要进行进一步的研究,以开发强大且基于共识的隐私和实用性评估方法.
- 解决评估标准化问题对于推进合成健康数据的利用至关重要.
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