Explainable machine learning for predicting postoperative length of stay after gastrectomy: a nationwide study using XGBoost and SHAP.

This proof-of-concept study demonstrates the feasibility of predicting length of stay from admission data, showing that explainable AI can replicate intuitive patterns in surgical oncology while simultaneously identifying unexpected insights from administrative data. These findings highlight the clinical potential of explainable AI for perioperative workflow optimization.