Open AccessUsing Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS
Author(s) -
Lisa Bastarache
Publication year - 2021
Publication title -
annual review of biomedical data science
Language(s) - English
Resource type - Journals
ISSN - 2574-3414
DOI - 10.1146/annurev-biodatasci-122320-112352
Subject(s) - phenome , health records , data science , computer science , electronic health record , data mining , computational biology , phenotype , biology , genetics , health care , political science , gene , law
Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.