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.

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.