Ontology-enhanced automatic chief complaint classification for syndromic surveillance

	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
刊名	JOURNAL OF BIOMEDICAL INFORMATICS
	2008-04-01
卷号	41 期号:2 页码:340-356
关键词	medical ontology UMLS free-text chief complaints chief complaint classification syndromic surveillance bootstrapping statistical evaluation
英文摘要	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.
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
类目[WOS]	Computer Science, Interdisciplinary Applications ; Medical Informatics
研究领域[WOS]	Computer Science ; Medical Informatics
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记录号	WOS:000255360000012
公开日期	2015-12-24
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/9550]
专题	自动化研究所_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.