A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

Journal of the American Medical Informatics Association

Volumn 22, Issue 1, 2015, Pages 155-165

  Rochefort, Christian M ^a,b   Verma, Aman D ^a,b   Eguale, Tewodros ^b,c   Lee, Todd C ^d   Buckeridge, David L ^a,b  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

^d MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

Author keywords

Acute care hospital; Automated text classification; Deep vein thrombosis; Natural language processing; Pulmonary embolism; Support vector machines

Indexed keywords

AGED; AREA UNDER THE CURVE; ARTICLE; DEEP VEIN THROMBOSIS; ELECTRONIC MEDICAL RECORD; FEMALE; HUMAN; LUNG EMBOLISM; MAJOR CLINICAL STUDY; MALE; NATURAL LANGUAGE PROCESSING; PREDICTIVE VALUE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VENOUS THROMBOEMBOLISM; HOSPITALIZATION; VALIDATION STUDY;

ELECTRONIC HEALTH RECORDS; HOSPITALIZATION; HUMANS; NATURAL LANGUAGE PROCESSING; PULMONARY EMBOLISM; SUPPORT VECTOR MACHINES; VENOUS THROMBOEMBOLISM;

EID: 84929501877     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2014-002768     Document Type: Article

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