Name
Title | ||
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Phemap: A Multi-Resource Knowledge Base For High-Throughput Phenotyping Within Electronic Health Records |
Abstract | ||
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Objective: 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.Materials and Methods: 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.Results: 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, Phe-Mapderived 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.Conclusions: PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/jamia/ocaa104 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | DocType | Volume |
electronic health records, high-throughput phenotyping, natural language processing | Journal | 27 |
Issue | ISSN | Citations |
11 | 1067-5027 | 1 |
PageRank | References | Authors |
0.35 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neil S Zheng | 1 | 1 | 0.35 |
| QiPing Feng | 2 | 4 | 1.39 |
| Vern Eric Kerchberger | 3 | 2 | 0.70 |
| Juan Zhao | 4 | 2 | 1.72 |
| Todd L. Edwards | 5 | 74 | 5.50 |
| Nancy J Cox | 6 | 1 | 0.35 |
| C Michael Stein | 7 | 1 | 0.35 |
| Dan M. Roden | 8 | 177 | 18.14 |
| Joshua C. Denny | 9 | 932 | 97.43 |
| Wei-Qi Wei | 10 | 137 | 16.54 |