Ontology-enhanced automatic chief complaint classification for syndromic surveillance

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.

• Type: Journal
• Year: 2008
• Where: JBI
• Authors: Hsin-Min Lu, Daniel Zeng, Lea Trujillo, Ken Komatsu, Hsinchun Chen
• Keywords: CC Classification, Classification Approach, JBI 2004, JBI 2008, Syndromic Categories