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<p>In recent years, the prevalence of chronic diseases such as Ulcerative Colitis (UC) has increased, bringing a heavy burden to healthcare systems. Traditional Chinese Medicine (TCM) stands out for its cost-effective and efficient treatment modalities, providing unique advantages in healthcare. But syndrome differentiation of UC presents a longstanding challenge in TCM due to its chronic nature and varied manifestations. While existing research has primarily explored machine learning applications for diagnosis and prognosis prediction, the critical issue of explainability in syndrome differentiation remains underexamined. To bridge this gap, we propose an ensemble prediction model enhanced with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve interpretability and clinical utility. Our study utilizes a dataset of 8078 electronic medical records from Dongfang Hospital, Beijing University of Chinese Medicine, collected between 2006 and 2019. Comprehensive evaluations demonstrate that our ensemble models outperform individual deep learning approaches, with the Gradient Boosting (GB) model achieving 83% F1 in syndrome differentiation. Furthermore, SHAP and LIME reveal key features associated with different syndromes, such as frequent stool in spleen-kidney yang deficiency and lower abdominal coldness in spleen yang deficiency, offering valuable insights for intelligent syndrome differentiation. These findings hold significant promise for advancing TCM-based UC management, enhancing clinical decision-making, and improving patient outcomes.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Ling Zhu,
  • Shan He,
  • Wanting Zheng,
  • Yuanyuan Tong,
  • Feng Yang

摘要

In recent years, the prevalence of chronic diseases such as Ulcerative Colitis (UC) has increased, bringing a heavy burden to healthcare systems. Traditional Chinese Medicine (TCM) stands out for its cost-effective and efficient treatment modalities, providing unique advantages in healthcare. But syndrome differentiation of UC presents a longstanding challenge in TCM due to its chronic nature and varied manifestations. While existing research has primarily explored machine learning applications for diagnosis and prognosis prediction, the critical issue of explainability in syndrome differentiation remains underexamined. To bridge this gap, we propose an ensemble prediction model enhanced with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve interpretability and clinical utility. Our study utilizes a dataset of 8078 electronic medical records from Dongfang Hospital, Beijing University of Chinese Medicine, collected between 2006 and 2019. Comprehensive evaluations demonstrate that our ensemble models outperform individual deep learning approaches, with the Gradient Boosting (GB) model achieving 83% F1 in syndrome differentiation. Furthermore, SHAP and LIME reveal key features associated with different syndromes, such as frequent stool in spleen-kidney yang deficiency and lower abdominal coldness in spleen yang deficiency, offering valuable insights for intelligent syndrome differentiation. These findings hold significant promise for advancing TCM-based UC management, enhancing clinical decision-making, and improving patient outcomes.