| Autor: |
Henderson J; The University of Texas at Austin, Austin, TX., Bridges R; Epic Systems, Verona, WI., Ho JC; Emory University, Atlanta, GA., Wallace BC; Northeastern University, Boston, MA., Ghosh J; The University of Texas at Austin, Austin, TX. |
| Jazyk: |
angličtina |
| Zdroj: |
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2017 Jul 26; Vol. 2017, pp. 149-157. Date of Electronic Publication: 2017 Jul 26 (Print Publication: 2017). |
| Abstrakt: |
As the adoption of Electronic Healthcare Records has grown, the need to transform manual processes that extract and characterize medical data into automatic and high-throughput processes has also grown. Recently, researchers have tackled the problem of automatically extracting candidate phenotypes from EHR data. Since these phenotypes are usually generated using unsupervised or semi-supervised methods, it is necessary to examine and validate the clinical relevance of the generated "candidate" phenotypes. We present PheKnow-Cloud, a framework that uses co-occurrence analysis on the publicly available, online repository ofjournal articles, PubMed, to build sets of evidence for user-supplied candidate phenotypes. PheKnow-Cloud works in an interactive manner to present the results of the candidate phenotype analysis. This tool seeks to help researchers and clinical professionals evaluate the automatically generated phenotypes so they may tune their processes and understand the candidate phenotypes. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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