在电子健康记录中分类多种先天性异常病例的系统方法
Elly Brokamp1, Tyne Miller-Fleming1, Alexandra Scalici1
1Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
概括
一种新方法可以在电子健康记录 (EHR) 中准确识别患有多种先天性异常 (MCA) 的个体. 这种方法可以提高对MCA的理解.
科学领域:
- 医疗信息学医学信息学
- 遗传学 是一个遗传学.
- 儿科 儿科 儿科
背景情况:
- 先天性异常 (CAs) 影响3%的出生,导致显著的婴儿发病率和死亡率.
- 多重先天性异常 (MCA) 在一些人中发生,但电子健康记录 (EHR) 中缺乏系统的识别.
- 了解MCA的遗传和流行病学因素至关重要,但受到识别挑战的阻碍.
研究的目的:
- 开发一种可扩展和准确的方法,用于在电子健康记录 (EHR) 中识别患有多种先天性异常 (MCA) 的个人.
- 为了改善对MCA的遗传和流行病学基础的研究.
- 建立一个标准化的方法,在不同的电子健康数据库中对MCA进行表征.
主要方法:
- 使用匿名的EHR数据库对三种不同的MCA分类方法的评估.
- 实施一种新的方法,绕过小与大CA差异化.
- 利用全现象关联研究来分析与以前分类的小CA相关的现象.
主要成果:
- 在EHR中开发的通用MCA识别方法显示了高精度 (97.1% PPV).
- 确定的MCA病例显示医院利用率增加,其中41%接受住院护理.
- 该方法有效地捕获了先天性异常的详细模式,并在两个额外的队列中得到了验证.
结论:
- 已经成功开发了一种全面的方法来识别MCA在EHR中的个体.
- 这种方法有助于对MCA的遗传原因进行更深入的研究.
- 该方法是可转移的,适用于使用计费代码的EHR系统.
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