Objective To evaluate the phenotyping performance of three major electronic health record
(EHR) components: International Classification of Disease (ICD) diagnosis codes, primary …

The national adoption of electronic health records (EHR) promises to make an
unprecedented amount of data available for clinical research, but the data are complex …

Widespread sharing of data from electronic health records and patient-reported outcomes
can strengthen the national capacity for conducting cost-effective clinical trials and allow …

Background Genetic studies require precise phenotype definitions, but electronic medical
record (EMR) phenotype data are recorded inconsistently and in a variety of formats …

Research objective To develop scalable informatics infrastructure for normalization of both
structured and unstructured electronic health record (EHR) data into a unified, concept …

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 …

ABSTRACT Background Electronic medical records (EMRs) hold a tremendous amount of
information about patients that is relevant to determining the optimal approach to patient …

With the completion of the Human Genome Project1 as well as recent advances in genomic
science and comparative biological studies, a new era of individualized medicine is evolving …

Objective Developing algorithms to extract phenotypes from electronic health records
(EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput …

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