NLP-based classification of bowel preparation quality in colonoscopy reports
May 4, 2026·
Besim Fazil Agargun
Abstract
Poster presentation (Mo2176) of an NLP pipeline that classifies bowel-preparation quality from free-text colonoscopy reports (11,374 reports; TF-IDF + logistic regression; ~92% cross-validated accuracy). Selected for the AGA Innovation Themed Walking Tour.
Date
May 4, 2026 —
Event
Location
Chicago, IL, United States
Authors
Gastroenterology Fellow
Gastroenterology and hepatology fellow at Istanbul University, Istanbul Faculty of Medicine.
My clinical work centres on inflammatory bowel disease and diagnostic and therapeutic endoscopy;
my research combines real-world cohorts with natural-language processing and AI-assisted analytics —
including an NLP programme over more than 11,000 colonoscopy reports and the ELASTIBD transient
elastography study of liver fibrosis and steatosis in IBD. I am also a PhD candidate in clinical trials.