| Ontology-enhanced automatic chief complaint classification for syndromic surveillance. | |
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MedLine Citation:
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PMID: 17928273 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Emergency department free-text chief complaints (CCs) are a major data source for syndromic surveillance. CCs need to be classified into syndromic categories for subsequent automatic analysis. However, the lack of a standard vocabulary and high-quality encodings of CCs hinder effective classification. This paper presents a new ontology-enhanced automatic CC classification approach. Exploiting semantic relations in a medical ontology, this approach is motivated to address the CC vocabulary variation problem in general and to meet the specific need for a classification approach capable of handling multiple sets of syndromic categories. We report an experimental study comparing our approach with two popular CC classification methods using a real-world dataset. This study indicates that our ontology-enhanced approach performs significantly better than the benchmark methods in terms of sensitivity, F measure, and F2 measure. |
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Authors:
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Hsin-Min Lu; Daniel Zeng; Lea Trujillo; Ken Komatsu; Hsinchun Chen |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. Date: 2007-09-06 |
Journal Detail:
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Title: Journal of biomedical informatics Volume: 41 ISSN: 1532-0480 ISO Abbreviation: J Biomed Inform Publication Date: 2008 Apr |
Date Detail:
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Created Date: 2008-03-10 Completed Date: 2008-04-16 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: 340-56 Citation Subset: IM |
Affiliation:
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Management Information Systems Department, The Eller College of Management, University of Arizona, 1130 E. Helen Street, Room 430, P.O. Box 210108, Tucson, AZ 85721-0108, USA. hmlu@email.arizona.edu |
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence* Decision Support Systems, Clinical* Disease Outbreaks / prevention & control* Emergency Medical Services / methods* Pattern Recognition, Automated / methods* Population Surveillance / methods* |
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