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Related Experiment Video

Updated: Sep 17, 2025

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Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods.

Ling Zhu1, Shan He2, Wanting Zheng3

  • 1Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China. jjzhuling@163.com.

Scientific Reports
|July 1, 2025
PubMed
Summary

This study introduces explainable AI for Traditional Chinese Medicine (TCM) syndrome differentiation in Ulcerative Colitis (UC). Ensemble models with SHAP and LIME improve accuracy and provide clinical insights for better patient outcomes.

Keywords:
Ensemble machine learningInterpretabilityLIMESHAPSyndrome differentiationUlcerative colitis

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Area of Science:

  • Integrative Medicine
  • Artificial Intelligence in Healthcare
  • Traditional Chinese Medicine (TCM)

Background:

  • Rising prevalence of chronic diseases like Ulcerative Colitis (UC) poses a significant healthcare burden.
  • Traditional Chinese Medicine (TCM) offers cost-effective and efficient treatment options for UC.
  • Syndrome differentiation in TCM for UC is challenging due to chronicity and varied symptoms, with limited research on explainability in AI models.

Purpose of the Study:

  • To develop and evaluate an explainable ensemble prediction model for TCM syndrome differentiation in UC.
  • To enhance the interpretability and clinical utility of AI models in TCM practice.
  • To identify key features contributing to different UC syndromes within the TCM framework.

Main Methods:

  • Utilized a dataset of 8078 electronic medical records from Dongfang Hospital (2006-2019).
  • Developed an ensemble prediction model incorporating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations).
  • Evaluated model performance against individual deep learning approaches and analyzed feature importance for syndrome differentiation.

Main Results:

  • Ensemble models outperformed individual deep learning methods.
  • The Gradient Boosting (GB) model achieved an 83% F1 score for syndrome differentiation.
  • SHAP and LIME identified key differentiating features, such as frequent stool (spleen-kidney yang deficiency) and lower abdominal coldness (spleen yang deficiency).

Conclusions:

  • The proposed explainable AI models significantly improve TCM syndrome differentiation for UC.
  • SHAP and LIME provide valuable clinical insights, aiding intelligent syndrome differentiation and decision-making.
  • These findings support the advancement of TCM-based UC management and improved patient outcomes.