Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning

Journal of the American Medical Informatics Association

Volumn 23, Issue 6, 2016, Pages 1077-1084

  Osborne, John D ^a   Wyatt, Matthew ^a   Westfall, Andrew O ^a   Willig, James ^b   Bethard, Steven ^a   Gordon, Geoff ^c  

^a UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^b University of Alabama at Birmingham   (United States)

^c University of Alabama at Birmingham   (United States)

Author keywords

Electronic health records; Information extraction; Machine learning; Natural language processing; Neoplasms; User computer interface

Indexed keywords

CANCER REGISTRY; COMPUTER INTERFACE; ELECTRONIC HEALTH RECORD; EXTRACTION; HUMAN; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING; PATHOLOGY; RECALL; SOFTWARE; VALIDATION PROCESS; VELOCITY; DATA MINING; INTERNATIONAL CLASSIFICATION OF DISEASES; MANDATORY REPORTING; NEOPLASM; PROCEDURES; UNITED STATES;

DATA MINING; ELECTRONIC HEALTH RECORDS; HUMANS; INTERNATIONAL CLASSIFICATION OF DISEASES; MACHINE LEARNING; MANDATORY REPORTING; NATURAL LANGUAGE PROCESSING; NEOPLASMS; PATHOLOGY, CLINICAL; UNITED STATES;

EID: 84994756904     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocw006     Document Type: Article

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