Ontology-enhanced automatic chief complaint classification for syndromic surveillance

CASIA OpenIR > 09年以前成果

	Ontology-enhanced automatic chief complaint classification for syndromic surveillance
	Lu, Hsin-Min 1; Zeng, Daniel1,3 ; Trujillo, Lea 2; Komatsu, Ken 2; Chen, Hsinchun 1
发表期刊	JOURNAL OF BIOMEDICAL INFORMATICS
	2008-04-01
卷号	41 期号:2 页码:340-356
文章类型	Article
摘要	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. (c) 2007 Elsevier Inc. All rights reserved.
关键词	Medical Ontology Umls Free-text Chief Complaints Chief Complaint Classification Syndromic Surveillance Bootstrapping Statistical Evaluation
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
关键词[WOS]	FREE-TEXT COMPLAINTS ; EMERGENCY-DEPARTMENT ; SYSTEM ; TERMINOLOGY ; CATEGORIES ; OUTBREAK ; RECORDS ; RULE ; SET
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Medical Informatics
WOS记录号	WOS:000255360000012
引用统计	被引频次：26[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9550
专题	09年以前成果
作者单位	1.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
推荐引用方式 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.