Zeng Daniel

Free-text emergency department triage chief complaints (CCs) are a popular data source used by many syndromic surveillance systems because of their timeliness, availability, and relevance. The lack of standardization of CC vocabulary poses a major technical challenge to any automatic CC classification approach. This challenge can be partially addressed by several methods, for example, medical thesaurus, spelling check, manually-created synonym list, and supervised machine learning techniques that directly operate on free text. Current approaches, however, ignore the fact that medical terms appearing in CCs are often semantically related. Our research exploits such semantic relations through a medical ontology in the context of automatic CC classification for syndromic surveillance.

Objective

This paper presents a novel approach of using a medical ontology to classify free-text CCs into syndrome categories.

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.