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 …

Abstract Objective Electronic health records (EHRs) are a rich source of information on
human diseases, but the information is variably structured, fragmented, curated using …

Objective To compare three groupings of Electronic Health Record (EHR) billing codes for
their ability to represent clinically meaningful phenotypes and to replicate known genetic …

The convergence of two rapidly developing technologies-high-throughput genotyping and
electronic health records (EHRs)-gives scientists an unprecedented opportunity to utilize …

Clinical data describing the phenotypes and treatment of patients represents an underused
data source that has much greater research potential than is currently realized. Mining of …

Candidate gene and genome-wide association studies (GWAS) have identified genetic
variants that modulate risk for human disease; many of these associations require further …

The combination of improved genomic analysis methods, decreasing genotyping costs, and
increasing computing resources has led to an explosion of clinical genomic knowledge in …

Electronic health records contain patient‐level data collected during and for clinical care.
Data within the electronic health record include diagnostic billing codes, procedure codes …

Beginning in the early 2000s, the accumulation of biospecimens linked to electronic health
records (EHRs) made possible genome-phenome studies (ie, comparative analyses of …

Abstract The Electronic Medical Records and Genomics Network is a National Human
Genome Research Institute–funded consortium engaged in the development of methods …