Medical Ontology-Enhanced Text Processing for Infectious Disease Informatics

Infectious disease informatics, as a sub-field of security informatics, is concerned with development of the science and technologies needed for collecting, sharing, reporting, analyzing, and visualizing infectious disease data; and for providing data and decision-making support for infectious disease prevention, detection, and management. Syndromic surveillance is a major study area of infectious disease informatics, focusing on identifying in a timely manner possible infectious disease outbreaks based on pre-diagnostic data. Free-text chief complaints (CCs), short phrases describing reasons for patients´ emergency department visits, are a major source of data for syndromic surveillance. For surveillance purposes, CCs need to be classified into syndromic categories. However, the lack of standard vocabulary and high-quality encoding of CCs hinder effective classification. To meet this challenge, we have developed an ontology-enhanced automatic CC classification approach. Exploiting semantic relations in the UMLS, a medical ontology, this approach is motivated to address the CC word variation problem in general and to meet the specific need for a flexible classification approach capable of handling multiple sets of syndrome categories.