Ontology-enhanced automatic chief complaint classification for syndromic surveillance

  • Authors:

  • Hsin-Min Lu;Daniel Zeng;Lea Trujillo;Ken Komatsu;Hsinchun Chen

  • Affiliations:

  • 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;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 and The Institute of ...;Arizona Department of Health Services, Phoenix, AZ 85007, USA;Arizona Department of Health Services, Phoenix, AZ 85007, USA;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

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2008

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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.