从死亡记录中识别家庭结构,并在电子健康记录中将其与患者进行匹配
John Mayer1, Brooke Delgoffe1, Scott Hebbring2
1Office of Research Computing and Analytics, Marshfield Clinic Research Institute, Marshfield, WI 54449, United States.
概括
遗忘录为生物信息学研究提供了有价值的家庭数据,特别是对于老一代. 将告数据与电子健康记录 (EHR) 结合起来,可以为遗传研究创建信息化的电子血统.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- 由于共同的遗传和环境因素,家庭数据对生物信息学研究至关重要.
- 电子健康记录 (EHR) 能够创建电子血统,但往往错过了老年人.
- 遗忘录为家庭关系提供了丰富的数据来源,特别是在老一代.
研究的目的:
- 为生物信息学研究开发和演示一种将死记数据与电子健康记录集成的方法.
- 通过包括来自老年人群的数据来克服基于EHR的电子学历的局限性.
- 利用自然语言处理 (NLP) 来从告文本中提取家庭关系.
主要方法:
- 利用NLP从567,279份威斯康星州的告中提取了8,166,534名家庭成员.
- 将告数据与电子健康记录相匹配,在53640个血统中识别了200,033名独特的患者.
- 在GitHub上开发和共享用于告数据处理的代码.
主要成果:
- 成功构建了53640个血统,其中最大的包含21个个体.
- 量化了成人身高的遗传性 (H2=0.51±0.04,P<1.00e-07),验证了数据的实用性.
- 通过重叠的生物库数据,在家庭关系定义中显示了80%-90%的准确性.
结论:
- 遗忘录是生物信息学研究的高度信息化资源,特别是从最年长的社会成员获取数据.
- 遗传数据与电子健康记录的整合提高了电子遗传记录的完整性和实用性.
- 这种方法为扩大基于家庭的遗传研究提供了一种新方法.
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