Ontology-enhanced automatic chief complaint classification for syndromic surveillance

Hsin Min Lu, Daniel Zeng, Lea Trujillo, Ken Komatsu, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)340-356
Number of pages17
JournalJournal of Biomedical Informatics
Issue number2
StatePublished - Apr 2008


  • Bootstrapping
  • Chief complaint classification
  • Free-text chief complaints
  • Medical ontology
  • Statistical evaluation
  • Syndromic surveillance
  • UMLS

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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