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Related Experiment Video

Updated: Aug 14, 2025

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Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine

Xiaojun Li1, Lamei Yan1,2, Xuehong Wang1

  • 1Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China.

Frontiers in Medicine
|January 9, 2023
PubMed
Summary

Machine learning models can predict ulcerative colitis (UC) endoscopic activity non-invasively, using clinical and lab data. This approach may reduce the need for frequent colonoscopies in UC patients.

Keywords:
SHAPendoscopic disease activitymachine learningmayo endoscopic scorepredictive modelsulcerative colitisulcerative colitis endoscopic index of severity

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Area of Science:

  • Gastroenterology
  • Medical Informatics
  • Computational Biology

Background:

  • Endoscopic monitoring is crucial for ulcerative colitis (UC) management.
  • Current non-invasive methods lack effectiveness in predicting endoscopic disease activity.
  • Reducing endoscopic procedures and associated costs is a clinical need.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting endoscopic disease activity in UC patients.
  • To establish a non-invasive tool for assessing UC severity.
  • To potentially decrease the frequency of invasive endoscopic examinations.

Main Methods:

  • Retrospective study of 420 UC patients (January 2016 - January 2021).
  • Collected 39 clinical and laboratory variables, categorized by Mayo Endoscopic Score (MES) or Ulcerative Colitis Endoscopic Index of Severity (UCEIS).
  • Applied logistic regression and four ML algorithms (Random Forests, XGBoost), with feature selection and SMOTE, evaluated using AUC, accuracy, sensitivity, precision, and F1 score. SHAP for interpretability.

Main Results:

  • Random Forests (23 variables) achieved AUC 0.8192 for MES prediction.
  • XGBoost (21 variables) achieved AUC 0.8006 for UCEIS prediction.
  • Albumin, rectal bleeding, and CRP/ALB ratio were key predictors identified by SHAP analysis.

Conclusions:

  • ML models offer a promising non-invasive method for predicting endoscopic disease activity in UC.
  • Random Forests and XGBoost demonstrate suitability for data-driven endoscopic activity prediction in UC.
  • This approach could aid in optimizing patient management and reducing healthcare burdens.