title: Ontology-enhanced automatic chief complaint classification for syndromic surveillance.
abstract: 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.
authors.affiliation: 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
authors.forename: Hsin-Min
authors.initials: HM
authors.lastname: Lu
authors.forename: Daniel
authors.initials: D
authors.lastname: Zeng
authors.forename: Lea
authors.initials: L
authors.lastname: Trujillo
authors.forename: Ken
authors.initials: K
authors.lastname: Komatsu
authors.forename: Hsinchun
authors.initials: H
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D004196:Disease Outbreaks / Q000517:prevention & control*; D004632:Emergency Medical Services / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*