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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
PheKB: A catalog and workflow for creating electronic phenotype algorithms for transportability
Journal of the American Medical Informatics Association, Volume 23, No. 6, Year 2016
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Description
Objective Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites.Results As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%).Discussion These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others.Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
Authors & Co-Authors
Speltz, Peter J.
United States, Nashville
Vanderbilt University Medical Center
Rasmussen, Luke V.
United States, Evanston
Northwestern University
Gottesman, Omri
United States, New York
Icahn School of Medicine at Mount Sinai
Peissig, Peggy L.
United States, Marshfield
Marshfield Clinic
Pacheco, Jennifer A.
United States, Evanston
Northwestern University
Tromp, Gerard C.
United States
Geisinger Health System
Pathak, Jyotishman
United States, Rochester
Mayo Clinic
Carrell, David S.
United States, Seattle
Group Health Research Institute
Ellis, Stephen B.
United States, New York
Icahn School of Medicine at Mount Sinai
Lingren, Todd
United States, Cincinnati
Cincinnati Children's Hospital Medical Center
Thompson, William K.
United States, Evanston
Northwestern University
Savova, Guergana K.
United States, Boston
Harvard Medical School
Haines, Jonathan L.
United States, Cleveland
Case Western Reserve University
Roden, Dan M.L.
United States, Nashville
Vanderbilt University Medical Center
Harris, Paul A.
United States, Nashville
Vanderbilt University Medical Center
Denny, Joshua C.
United States, Nashville
Vanderbilt University Medical Center
Statistics
Citations: 201
Authors: 16
Affiliations: 10
Identifiers
Doi:
10.1093/jamia/ocv202
ISSN:
10675027