Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries
Na Hong(Digital China Health (China)), Guoqian Jiang(Mayo Clinic), Paul Kingsbury(Mayo Clinic in Arizona), Shintaro Tsuji(Mayo Clinic in Arizona), Andrew Wen(Mayo Clinic in Arizona), Daniel J. Stone(Mayo Clinic in Arizona), Luke V. Rasmussen(Northwestern University), Prakash Adekkanattu(Cornell University), Hongfang Liu(Southern Medical University), Jyotishman Pathak(Mayo Clinic), Jennifer A. Pacheco(University of Arizona)
Cited by 71
Related Papers
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data
|Nature Biotechnology|2013|1.1k
Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network
|Journal of the American Medical Informatics Association|2013|437
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
|Journal of the American Medical Informatics Association|2016|393
Quality Control Procedures for Genome‐Wide Association Studies
|Current Protocols in Human Genetics|2011|379