Knowledge Commons of Institute of Automation,CAS
Ontology-enhanced automatic chief complaint classification for syndromic surveillance | |
Lu, Hsin-Min1; Zeng, Daniel1,3; Trujillo, Lea2; Komatsu, Ken2; Chen, Hsinchun1 | |
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 | |
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. |
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