PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.
PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.
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DOI：
10.1093/jamia/ocaa104
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发表时间：
2020-11-01
期刊：
影响因子：
--
通讯作者：
Wei WQ
中科院分区：
文献类型：
--
作者：
Zheng NS;Feng Q;Kerchberger VE;Zhao J;Edwards TL;Cox NJ;Stein CM;Roden DM;Denny JC;Wei WQ
开发从电子健康记录（EHR）中提取表型的算法可能具有挑战性且耗时。我们开发了PheMap，这是一种高通量表型分析方法，利用多个独立的在线资源来简化EHR中的表型分析过程。PheMap是一个医学概念的知识库，它与表型有量化的关系，这些表型是通过自然语言处理从公共资源中提取的。PheMap在EHR中搜索每个表型的量化概念，并使用它们来计算个体具有该表型的概率。我们使用来自范德比尔特大学医学中心BioVU DNA生物库的84821名个体，将PheMap与来自电子病历和基因组学（eMERGE）网络的临床医生验证的2型糖尿病（T2 DM）、痴呆和甲状腺功能减退症表型算法进行比较。我们实施了基于PheMap的表型，用于T2 DM、痴呆和甲状腺功能减退症的全基因组关联研究（GWAS），以及FTO、HLA-DRB 1和TCF 7 L2变体的全表型关联研究（PheWAS）。 在这个初始迭代中，PheMap知识库包含841种疾病表型的量化概念。对于T2 DM、痴呆和甲状腺功能减退症，使用50%阈值和eMERGE病例对照状态作为参考标准，PheMap表型的准确性>97%。在GWAS分析中，PheMap衍生的表型概率复制了先前报告的3种表型的51种疾病相关变异中的43种。对于11个最大关联中的9个，PheMap提供了与基于eMERGE的表型相当或更显著的P值。基于PheMap的PheWAS显示出与传统的基于phecode的PheWAS相当或更好的性能。PheMap可在线公开获取。PheMap显著简化了从EHR中提取研究质量表型信息的过程，具有与当前表型分析方法相当或更好的性能。
Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs. PheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. In this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online. PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.
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DOI：
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发表时间：
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影响因子：
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