在电子健康记录中推导和验证一种母婴链接算法
Colin M Rogerson1,2, Christopher W Bartlett3,4, John Price2
1Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, 46202, United States.
Journal of the American Medical Informatics Association : JAMIA
|October 15, 2025
概括
一个新的算法使用电子健康数据将母亲和儿童的健康记录联系起来. 这种概率模型通过从电子健康记录 (EHR) 中准确识别母婴对来加强临床研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 母婴健康研究至关重要,但经常受到碎片化数据的阻碍.
- 连接母婴健康记录对于综合性研究至关重要.
- 电子健康记录 (EHR) 为链接提供了丰富的数据来源.
研究的目的:
- 开发和验证一种概率算法,用于链接母婴电子健康记录 (EHR).
- 促进和加强对母婴健康的临床研究.
- 为了利用例行收集的EHR数据来改进患者队列识别.
主要方法:
- 利用了从1994年到2024年的EHR数据.
- 开发了一个XGBoost模型,包含标准的EHR元素 (名称,DOB,地址等). ) 的情况.
- 使用嵌入EHR的母婴指标进行确定性结果和病例/对照抽样.
主要成果:
- 该模型实现了高性能指标:92%的准确性,98%的精度,87%的回忆和92%的F1分数.
- 成功处理了超过8200万个独特的记录.
- 连接了33364674个具有高信心的母婴对.
结论:
- 通过使用EHR数据成功推导出一个经过验证的概率链接算法.
- 该算法可以显著有利于未来的母婴健康观测研究.
- 常规的EHR数据元素对于概率性的母婴链接是有效的.
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