PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.
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 algor...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 23; no. 6; pp. 1046 - 1053 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118960384&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118960384 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Nov2016 vid: 23 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 118960384 118960384 NLM27026615 118960384 10.1093/jamia/ocv202 NLM27026615 118960384 ppf: 1046 ppct: 7 formats: tig: atl: PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. aug: au: Kirby, Jacqueline C. Speltz, Peter Rasmussen, Luke V. Basford, Melissa Gottesman, Omri Peissig, Peggy L. Pacheco, Jennifer A. Tromp, Gerard Pathak, Jyotishman Carrell, David S. Ellis, Stephen B. Lingren, Todd Thompson, Will K. Savova, Guergana Haines, Jonathan Roden, Dan M. Harris, Paul A. Denny, Joshua C. affil: Vanderbilt University Medical Center, Nashville, TN, USA sug: subj: Knowledge Bases Phenotype Algorithms Natural Language Processing Genomics Data Mining Methods International Classification of Diseases Human Funding Source ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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