• Foster R Goss, Joseph M Plasek, Jason J Lau, Diane L Seger, Frank Y Chang, and Li Zhou.
• Tufts Medical Center, Department of Emergency Medicine and Clinical Decision Making, Boston, MA.
• AMIA Annu Symp Proc. 2014 Jan 1;2014:580-8.

AbstractEmergency department (ED) visits due to allergic reactions are common. Allergy information is often recorded in free-text provider notes; however, this domain has not yet been widely studied by the natural language processing (NLP) community. We developed an allergy module built on the MTERMS NLP system to identify and encode food, drug, and environmental allergies and allergic reactions. The module included updates to our lexicon using standard terminologies, and novel disambiguation algorithms. We developed an annotation schema and annotated 400 ED notes that served as a gold standard for comparison to MTERMS output. MTERMS achieved an F-measure of 87.6% for the detection of allergen names and no known allergies, 90% for identifying true reactions in each allergy statement where true allergens were also identified, and 69% for linking reactions to their allergen. These preliminary results demonstrate the feasibility using NLP to extract and encode allergy information from clinical notes.

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