Applying active learning to high-throughput phenotyping algorithms for electronic health records data

Journal of the American Medical Informatics Association

Volumn 20, Issue E2, 2013, Pages

  Chen, Yukun ^a   Carroll, Robert J ^a   Hinz, Eugenia R McPeek ^b   Shah, Anushi ^a   Eyler, Anne E ^a   Denny, Joshua C ^a   Xu, Hua ^a,c  

^a VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

^c UNIVERSITY OF TEXAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; COLORECTAL CANCER; CONTROLLED STUDY; CURRENT PROCEDURAL TERMINOLOGY; ELECTRONIC MEDICAL RECORD; FEMALE; GENETIC PROCEDURES; HIGH THROUGHPUT PHENOTYPING; HUMAN; ICD-9; LEARNING ALGORITHM; LEARNING CURVE; MAJOR CLINICAL STUDY; MALE; PHENOTYPE; RHEUMATOID ARTHRITIS; SAMPLING; SUPPORT VECTOR MACHINE; UNCERTAINTY; UNIFIED MEDICAL LANGUAGE SYSTEM; VENOUS THROMBOEMBOLISM;

ACTIVE LEARNING; ELECTRONIC HEALTH RECORDS; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING; PHENOTYPING ALGORITHM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTRONIC HEALTH RECORDS; GENETIC ASSOCIATION STUDIES; HUMANS; PHENOTYPE; SUPPORT VECTOR MACHINES;

EID: 84888200992     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-001945     Document Type: Article

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