在单个和多个组织中归算缺失的综合EHR审计日志数据
Huan Li1, Nate C Apathy2, A Jay Holmgren3
1Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, United States; Computational Biology and Bioinformatics, Yale Graduate School of Arts and Sciences, New Haven, CT, United States; Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States.
Journal of biomedical informatics
|February 20, 2025
概括
一种系统的方法来分类变量,并使用定制的归算方法,如滚动平均时间序列,证明有效的缺少电子健康记录数据. 这种方法的性能优于机器学习的归算,特别是在较小的组织中.
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
- 电子健康记录 (EHR) 是一种电子医疗记录.
背景情况:
- 在EHR系统中缺少的数据对准确分析提出了挑战.
- 计算策略对于处理缺失的电子健康记录数据至关重要.
- 评估不同的归算方法对于可靠的医疗分析至关重要.
研究的目的:
- 为了比较EHR数据的天真和机器学习归算策略.
- 探索用于优化归算的分组标准.
- 评估内部归算实施的性能和可行性.
主要方法:
- 对来自不同组织的EHR审计日志数据进行了归算实验.
- 基于变化系数和缺失百分比的分类变量.
- 使用R2值评估归算性能,选择最强大的模型.
主要成果:
- 滚动平均时间序列归算显示了跨组织规模更一致的R2.
- 在大型组织中,XGBoost (机器学习) 的表现略有改善.
- 与合并数据相比,单个网站组织使用自己的数据进行归算,实现了更高的R2.
结论:
- 系统的变量分类和定制的归算策略对EHR数据是有效的.
- 滚动平均时间序列方法的表现优于非时间序列机器学习归算.
- 组织规模没有显著影响归算过程;合并不同的站点数据没有改善归算.
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