A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries

Journal of the American Medical Informatics Association

Volumn 16, Issue 4, 2009, Pages 596-600

  Yang, Hui ^a   Spasic, Irena ^a   Keane, John A ^a   Nenadic, Goran ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUTOMATION; CLINICAL STUDY; COMORBIDITY; CONTROLLED STUDY; HEALTH STATUS; HOSPITAL DISCHARGE; HUMAN; INFORMATION PROCESSING; MACHINE LEARNING; MEDICAL INFORMATION; OBESITY;

COMORBIDITY; HUMANS; INFORMATION STORAGE AND RETRIEVAL; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; NATURAL LANGUAGE PROCESSING; OBESITY; PATIENT DISCHARGE; SOFTWARE;

EID: 67649342013     PISSN: 10675027     EISSN: None     Source Type: Journal    
DOI: 10.1197/jamia.M3096     Document Type: Article

References (5)

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2. th Annual Text Retrieval Conference (2004)
3. Tsuruoka Y., Tateishi Y., Kim J., et al. Developing a robust part-of-speech tagger for biomedical text. Adv Inform (2005) 382-392
4. UMLS Knowledge Base. http://www.nlm.nih.gov/research/umls Accessed: Nov 23, 2008
5. Chapman W., Bridewell W., Hanbury P., Cooper G., and Buchanan B. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 34 (2001) 301-310