PS2-21: Extracting Signs and Symptoms from Colonoscopy Reports Using NLP

Abstract

Background/Aims The overarching goal of the Studying Colorectal Cancer: Effectiveness of Screening Strategies (SuCCESS) project at Group Health (GH) is to develop evidence to inform personalized colorectal cancer (CRC) screening recommendations. Specifically, we aim to study the comparative effectiveness of screening as practiced, evaluate the potential for personalizing screening and surveillance recommendations, and model the long-term comparative effectiveness of screening in a cohort of GH members enrolled between 1993 and 2015. To accomplish these goals, we used Natural Language Processing (NLP) to collect detailed information from colonoscopy reports in GH’s electronic medical record (EMR). Specifically, we extended an existing NLP system to identify whether signs or symptoms related to CRC were reported at the time of colonoscopy.