Frontiers in endocrinology · 2026
Dynamic evolution of readmission risk factors across short-, medium-, and long-term horizons in type 2 diabetes: a machine learning-based predictive modeling study with SHAP interpretability.
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Li Lei, Jiang Sheng
Аннотация
BACKGROUND: T2DM readmission risk factors may evolve across time windows, but this dynamic remains poorly understood.
METHODS: This retrospective cohort study developed nine machine learning models to predict 30-day, 60-day, and 365-day readmission in 12,041 T2DM patients (with an additional 2,007 patients used for temporal validation of the 30-day and 60-day models). Feature selection was performed using LASSO and Boruta. SHAP analysis was used for interpretability, with temporal validation performed for short- and medium-term models.
RESULTS: ANN achieved the highest AUROC for 30-day and 60-day predictions. Random forest showed competitive performance for 365-day prediction. SHAP analysis revealed a dynamic evolution: age dominated the 30-day window; length of hospital stay and inflammatory markers (SII, SIRI) emerged as key predictors in the 60-day window; and diabetes-specific chronic complications dominated the 365-day window.
CONCLUSION: Model selection should be time window-specific: ANN for short/medium-term, random forest for long-term prediction. Risk factors shift from acute vulnerability to inflammatory burden and then to chronic complications, supporting dynamic risk monitoring in T2DM patients.
METHODS: This retrospective cohort study developed nine machine learning models to predict 30-day, 60-day, and 365-day readmission in 12,041 T2DM patients (with an additional 2,007 patients used for temporal validation of the 30-day and 60-day models). Feature selection was performed using LASSO and Boruta. SHAP analysis was used for interpretability, with temporal validation performed for short- and medium-term models.
RESULTS: ANN achieved the highest AUROC for 30-day and 60-day predictions. Random forest showed competitive performance for 365-day prediction. SHAP analysis revealed a dynamic evolution: age dominated the 30-day window; length of hospital stay and inflammatory markers (SII, SIRI) emerged as key predictors in the 60-day window; and diabetes-specific chronic complications dominated the 365-day window.
CONCLUSION: Model selection should be time window-specific: ANN for short/medium-term, random forest for long-term prediction. Risk factors shift from acute vulnerability to inflammatory burden and then to chronic complications, supporting dynamic risk monitoring in T2DM patients.