纵向和时间序列健康数据的综合数据生成方法:系统性审查
Marko Miletic1, Murat Sariyar2
1Bern University of Applied Sciences, Höheweg 80, Bern, Biel/Bienne, CH-2502, Switzerland.
BMC medical informatics and decision making
|December 24, 2025
概括
对于时间健康数据的合成数据生成 (SDG) 正在推进,但目前的方法缺乏标准化的评估和强大的隐私保护措施. 未来的研究需要一个统一的框架来负责任地将人工智能整合到医疗保健中.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 合成数据生成 (SDG) 为医疗保健研究提供了保护隐私的替代方案.
- 时间健康数据 (例如,电子健康记录,生理信号) 由于其复杂性和敏感性而带来了独特的可持续发展目标挑战.
研究的目的:
- 系统地审查对纵向和时间序列健康数据的SDG方法.
- 为SDG景观提出一个分类学.
- 描述合成技术,评估策略和隐私措施.
主要方法:
- 按照PRISMA指导方针进行系统的文献审查 (2017-2025年).
- 使用结构化数据提取和主题分析分析了115项研究.
- 对时间健康数据的SDG方法的比较综合.
主要成果:
- 深度生成模型 (GAN,AE,扩散) 主导着SDG,越来越多地使用自回归和混合方法.
- 基于事件的EHR数据是常见的目标;连续/不规则的时间序列未被充分探索.
- 实用性评估侧重于统计/预测;隐私评估很少,差异性隐私 (DP) 实施有限.
结论:
- 合成时间数据对于临床预测,公共卫生和人工智能至关重要.
- 可持续发展目标研究在术语,评估和隐私方面是分散的.
- 对于负责任的人工智能,需要一个统一的框架,解决公平,透明和临床采用问题.
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