Ontology-enhanced automatic chief complaint classification for syndromic surveillance

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.

Chief Complaint–Based Performance Measures: A New Focus for Acute Care Quality Measurement

“…26-28 Determining the right number of chief complaint categories to capture most ED patients cases while providing the best level of specificity is challenging. 29 The inability to create a mutually exclusive chief complaint set that can capture all ED visits should not hinder the development of chief complaint performance measures, however, as measures typically require methods for reliably capturing patients with common, undifferentiated chief complaints.…”

Filtering big data from social media – Building an early warning system for adverse drug reactions

“…For example, Lu et al [14] proposed an ontology-enhanced approach for classifying free-text chief complaints (CCs) from the emergency department. Botsis et al [15] employed a multi-level text mining approach for automated text classification of VAERS (Vaccine Adverse Event Reporting System) reports.…”

Semantic-enhanced models to support timely admission prediction at emergency departments

“…Examples of CC terms are: fv (fever), nvd (nausea, vomiting, and diarrhea), and sob (shortness of breath). Without a standard lexicon, word variations such as synonyms and acronyms, misspelling, and the institution-specific use of expressions are quite common in CCs, which was often regarded as a major challenge for the use of free-text CCs (Haas et al 2008;Lu et al 2008). Moreover, multiple CCs can be assigned to an individual patient to describe his/her different symptoms or conditions, which presents a critical challenge for the representation and modeling of data.…”