Shun-Tian Cai

Explainable random forest–SHAP framework for Montreal phenotype–stratified prediction of one-year complications in Crohn’s disease

Background Early identification of phenotype-stratified, short-term complication risk remains a critical unmet need in Crohn’s disease (CD). Existing models focus on single outcomes and lack interpretability, limiting clinical use. We developed a clinically interpretable machine-learning framework to enable Montreal ph...