Free text chief complaints (CCs), which may be recorded in different languages, are an important data source for syndromic surveillance systems. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, CCs in different languages pose technical challenges for the development of multilingual CC classifiers. We addressed the technical challenges by first developing a ontology-enhanced CC classifier which exploits semantic relations in the Unified Medical Language System (UMLS) to expand the knowledge of a rule-based CC classifier. Based on the ontologyenhanced English CC classifier, a translation module was incorporated to extract symptom-related information in Chinese CCs and translate it into English. This design thus enables the processing of CCs in both English and Chinese.
Objective
This paper describes the effort to design and implement a chief complaint (CC) classification system that is capable of processing CCs in both English and Chinese.