The variability of free text emergency department (ED) data is problematic for biosurveillance, and current methods of identifying search terms for symptoms of interest are inefficient as well as time- and labor-intensive. Our ad hoc approach to term identification for the North Carolina Disease and Epidemiologic Collection Tool (NC DETECT) begins with development of clinical case definitions from which we build automated syndrome queries in standard query language. The queries are used to search free text clinical data from EDs, with the goal of identifying free text terms to match the case definitions. The free text search terms were initially collected from epidemiologists and clinical and technical staff at NC DETECT through informal review of ED data. Over time, we reviewed individual cases missed by our queries and identified additional search terms. We also manually reviewed records to find misspellings, abbreviations and acronyms for known search terms (e.g., dypnea, diff. br. and SHOB for dyspnea), and developed a pre-processor to clean text prior to syndromic classification. The purpose of this project was to develop and test a more standardized approach to search term identification.
Objective
This paper describes and applies a new method for identifying biosurveillance search terms using the Semantic Network of the Unified Medical Language System.