CASIA OpenIR  > 09年以前成果
Ontology-enhanced automatic chief complaint classification for syndromic surveillance
Lu, Hsin-Min1; Zeng, Daniel1,3; Trujillo, Lea2; Komatsu, Ken2; Chen, Hsinchun1
Source PublicationJOURNAL OF BIOMEDICAL INFORMATICS
2008-04-01
Volume41Issue:2Pages:340-356
SubtypeArticle
AbstractEmergency 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. (c) 2007 Elsevier Inc. All rights reserved.
KeywordMedical Ontology Umls Free-text Chief Complaints Chief Complaint Classification Syndromic Surveillance Bootstrapping Statistical Evaluation
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
WOS KeywordFREE-TEXT COMPLAINTS ; EMERGENCY-DEPARTMENT ; SYSTEM ; TERMINOLOGY ; CATEGORIES ; OUTBREAK ; RECORDS ; RULE ; SET
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Medical Informatics
WOS SubjectComputer Science, Interdisciplinary Applications ; Medical Informatics
WOS IDWOS:000255360000012
Citation statistics
Cited Times:19[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9550
Collection09年以前成果
Affiliation1.Univ Arizona, Eller Coll Management, Dept Management Informat Syst, Tucson, AZ 85721 USA
2.Arizona Dept Hlth Serv, Phoenix, AZ 85007 USA
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Lu, Hsin-Min,Zeng, Daniel,Trujillo, Lea,et al. Ontology-enhanced automatic chief complaint classification for syndromic surveillance[J]. JOURNAL OF BIOMEDICAL INFORMATICS,2008,41(2):340-356.
APA Lu, Hsin-Min,Zeng, Daniel,Trujillo, Lea,Komatsu, Ken,&Chen, Hsinchun.(2008).Ontology-enhanced automatic chief complaint classification for syndromic surveillance.JOURNAL OF BIOMEDICAL INFORMATICS,41(2),340-356.
MLA Lu, Hsin-Min,et al."Ontology-enhanced automatic chief complaint classification for syndromic surveillance".JOURNAL OF BIOMEDICAL INFORMATICS 41.2(2008):340-356.
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