超越医疗统计:对电子健康记录中缺失数据处理的系统审查
Wenhui Ren1, Zheng Liu1, Yanqiu Wu1
1Department of Clinical Epidemiology and Biostatistics, Peking University People's Hospital, Beijing, China.
Health data science
|December 5, 2024
概括
机器学习方法显示出解决电子健康记录 (EHR) 中缺少数据的希望. 然而,性能因缺失类型和数据结构而异,需要标准化评估.
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
- 生物统计学 生物统计学
背景情况:
- 电子健康记录 (EHR) 中缺少的数据给医学研究带来了挑战.
- 现有的处理缺失EHR数据的方法在不同环境中缺乏明确的绩效指南.
研究的目的:
- 审查和比较处理电子健康记录中缺少数据的方法.
- 在各种失踪情景下评估不同策略的表现.
主要方法:
- 在MEDLINE,EMBASE和DBLP数据库中进行系统的文献搜索,直到2024年3月.
- 提取研究特征和缺失数据方法的性能评估.
- 模拟完全随机丢失,随机丢失和不随机丢失的机制.
主要成果:
- 包括46项研究 (2010-2024年),分析了3种失踪机制.
- 链式方程 (MICE) 的多重归算是最常用的统计方法.
- 机器学习 (ML) 方法,包括深度学习和k-最近邻居 (KNN),通常优于统计方法.
- 在纵向数据中,Med.KNN和时间序列归算优于纵向数据;PCA和MICE对于横截面数据是最佳的.
结论:
- 机器学习方法为管理缺失的EHR数据提供了巨大的潜力.
- 没有一种单一的方法是普遍最佳的;特定环境的评估至关重要.
- 需要标准化的基准测试来评估在各种失踪情景中使用ML方法.
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