The Real-time Outbreak and Disease Surveillance system collects chief complaints as free text and uses a naïve Bayesian classifier called CoCo to classify the complaints into syndromic categories. CoCo 3.0 has been trained on 28,990 manually clas-sified chief complaints. The free text chief com-plaints are challenging to work with, due to problems caused by linguistic variations such as synonyms, abbreviations, acronyms, truncations, concatenations, misspellings and typographic errors. Failure to correct these word variations may result in missed cases, thereby decreasing sensitivity of detection.
Objective
To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.