Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus

2025-07-31

AI Summary

Accurately predicting all-cause mortality risk in type 2 diabetes mellitus (T2DM) patients remains challenging due to the need for both high predictive performance and clinical interpretability. Method: We propose the Explainable Survival Tree (EST), a novel interpretable survival analysis model trained on a long-term prospective cohort. EST identifies clinically meaningful survival-associated features and integrates SHAP (Shapley Additive Explanations) for individualized risk attribution and transparent decision support. Contribution/Results: EST achieves AUCs of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality prediction, respectively, with a concordance index (C-index) of 0.776—significantly outperforming conventional models. By deeply integrating EST with SHAP, our approach uniquely balances high predictive accuracy and clinical interpretability, enabling bedside identification of high-risk patients and personalized intervention planning. This work advances trustworthy AI deployment in chronic disease management.

Technology Category

Machine Learning: Transparent, Interpretable, Explainable MLHumans and AI: Explainable AI (XAI) for Human UnderstandingPhilosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Objective. Type 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies. Research Design and Methods. This study analyzed a cohort of 554 patients (aged 40-87 years) with diagnosed T2DM over a maximum follow-up period of 16.8 years, during which 202 patients (36%) died. Key survival-associated features were identified, and multiple machine learning (ML) models were trained and validated to predict all-cause mortality risk. To improve model interpretability, Shapley additive explanations (SHAP) was applied to the best-performing model. Results. The extra survival trees (EST) model, incorporating ten key features, demonstrated the best predictive performance. The model achieved a C-statistic of 0.776, with the area under the receiver operating characteristic curve (AUC) values of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality predictions, respectively. The SHAP approach was employed to interpret the model's individual decision-making processes. Conclusions. The developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization.

Problem

• Predict all-cause mortality risk in T2DM patients
• Improve interpretability of mortality prediction models
• Identify high-risk patients for optimized treatment

Innovation

• Extra Survival Trees model for mortality prediction
• SHAP for explainable AI decision interpretation
• Ten key features enhance predictive accuracy