从死亡记录中识别家庭结构,并将其与电子健康记录中的患者进行匹配
bioRxiv : the preprint server for biology
|December 16, 2024
概括
可以使用NLP挖掘遗嘱,从老一代中提取家庭数据,增强生物信息学研究和遗传研究的电子健康记录.
科学领域:
- 生物信息学是一种生物信息学.
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
背景情况:
- 由于共同的遗传和环境暴露,家庭数据对生物信息学至关重要.
- 电子健康记录 (EHR) 能够识别血统,但往往错过了老一代.
- 遗忘录为老一代的家庭信息提供了丰富的数据来源.
研究的目的:
- 开发和展示新的方法,将死记数据与EHR集成在一起.
- 通过包括老一代来克服现有的e-pedigree方法的局限性.
- 利用自然语言处理 (NLP) 来从告中提取家庭关系.
主要方法:
- 利用NLP从威斯康星州的大量告中提取家庭成员.
- 在EHR中匹配了来自告的家庭成员和死者与患者.
- 通过将个人与死亡记录和EHR数据联系起来来构建血统.
主要成果:
- 从567,279份告中成功抽取了8,166,534名家庭成员.
- 在34,158个血统中确定了109,365名独特的患者.
- 成年人身高的量化遗传性 (H2 = 0.51 ± 0.04),证实了数据在遗传研究中的有用性.
结论:
- 将告数据与EHR集成,大大扩大了生物信息学的谱系资源.
- 遗嘱书提供了宝贵的家族史数据,特别是对于年长一代.
- 开发的方法在定义家庭关系方面表现出高准确度 (80% - 90%).
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