Language Models for Multilabel Document Classification of Surgical Concepts in Exploratory Laparotomy Operative Notes: Algorithm Development Study.
Journal:
JMIR medical informatics
Published Date:
Jul 9, 2025
Abstract
BACKGROUND: Operative notes are frequently mined for surgical concepts in clinical care, research, quality improvement, and billing, often requiring hours of manual extraction. These notes are typically analyzed at the document level to determine the presence or absence of specific procedures or findings (eg, whether a hand-sewn anastomosis was performed or contamination occurred). Extracting several binary classification labels simultaneously is a multilabel classification problem. Traditional natural language processing approaches-bag-of-words (BoW) and term frequency-inverse document frequency (tf-idf) with linear classifiers-have been used previously for this task but are now being augmented or replaced by large language models (LLMs). However, few studies have examined their utility in surgery.
