Guideline-driven clinical decision support for colonoscopy patients using the hierarchical multi-label deep learning method.

ABSTRACT:

Background

Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style.

Methods

We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labe

SUBMITTER: Wu J 

PROVIDER: S-EPMC12537133 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature