在共享儿童健康记录中提高患者识别准确性:对于老语言背景的混合方法
Thepphouthone Sorsavanh1, Chang Liu2, Goshiro Yamamoto3
1Medical Informatics Division, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan. thepsorsavan@kuhp.kyoto-u.ac.jp.
Journal of medical systems
|September 26, 2025
概括
混合和概率患者匹配方法显示出改善老人民民主共和国健康数据交换的希望,尽管非拉丁文字的挑战. 这些方法有助于减少共享儿童健康记录系统中的重复记录.
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
- 公共卫生 公共卫生
背景情况:
- 在老民主共和国的共享儿童健康记录 (SCHR) 项目旨在通过数据交换改善儿科医疗保健.
- 由于非拉丁文字的复杂性 (音色变化,音色字母,临时名称),患者识别和重复记录的挑战很重要.
- 现有的针对拉丁字母设计的患者匹配算法在这种情况下表现不佳.
研究的目的:
- 评估和调整患者匹配算法,以适应老人民共和国独特的语言和基础设施环境.
- 在老SCHR数据集上评估确定性,概率和混合匹配方法的性能.
主要方法:
- 使用了20433个老SCHR记录的数据集.
- 评估了确定性,概率 (具有Jaro-Winkler相似性的Fellegi-Sunter模型) 和混合匹配方法.
- 一个手动的黄金标准审查3,191匹配验证了算法性能.
主要成果:
- 混合和概率方法的性能优于确定性匹配,达到90%的回忆率.
- 混合方法在SCHR数据集中解决了大约2872个重复.
- 挑战包括双胞胎记录和临时到永久名称的过渡,影响准确性.
结论:
- 这项研究是首次将患者匹配方法调整为老人民民主共和国的特定环境.
- 混合方法显示出在资源有限的环境中改善健康信息交换 (HIE) 系统的潜力.
- 与以拉丁语为基础的系统相比,绩效差距仍然存在,突出了未来发展的领域.
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