| Evaluation of preprocessing techniques for chief complaint classification. | |
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MedLine Citation:
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PMID: 18166502 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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OBJECTIVE: To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance. METHODS: We preprocessed chief complaints using two preprocessors (CCP and EMT-P) and evaluated whether classification performance increased for a probabilistic classifier (CoCo) or for a keyword-based classifier (modification of the NYC Department of Health and Mental Hygiene chief complaint coder (KC)). RESULTS: CCP exhibited high accuracy (85%) in preprocessing chief complaints but only slightly improved CoCo's classification performance for a few syndromes. EMT-P, which splits chief complaints into multiple problems, substantially increased CoCo's sensitivity for all syndromes. Preprocessing with CCP or EMT-P only improved KC's sensitivity for the Constitutional syndrome. CONCLUSION: Evaluation of preprocessing systems should not be limited to accuracy of the preprocessor but should include the effect of preprocessing on syndromic classification. Splitting chief complaints into multiple problems before classification is important for CoCo, but other preprocessing steps only slightly improved classification performance for CoCo and a keyword-based classifier. |
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Authors:
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Jagan Dara; John N Dowling; Debbie Travers; Gregory F Cooper; Wendy W Chapman |
Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. Date: 2007-11-29 |
Journal Detail:
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Title: Journal of biomedical informatics Volume: 41 ISSN: 1532-0480 ISO Abbreviation: J Biomed Inform Publication Date: 2008 Aug |
Date Detail:
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Created Date: 2008-07-14 Completed Date: 2008-09-23 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 100970413 Medline TA: J Biomed Inform Country: United States |
Other Details:
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Languages: eng Pagination: 613-23 Citation Subset: IM |
Affiliation:
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Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue, VALE M-183, Pittsburgh, PA 15260, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Artificial Intelligence* Diagnosis, Computer-Assisted / methods* Natural Language Processing* Pattern Recognition, Automated / methods* Population Surveillance / methods* Syndrome Terminology as Topic* |
| Grant Support | |
ID/Acronym/Agency:
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K22LM008301/LM/NLM NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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