Interpretable machine learning model predicts the early gastrointestinal dysfunction risk in severely burned patients: a multicenter-based study.

Background: Gastrointestinal (GI) dysfunction is a life-threatening complication following severe burn injury, significantly increasing risks of multi-organ failure and mortality. This study aimed to develop and validate the first machine learning (ML)-based clinical prediction model for GI dysfunction after severe burns by leveraging explainable artificial intelligence (AI) techniques to support early clinical intervention. Methods: In this retrospective multicenter study, 570 patients with severe burns were enrolled: 469 from Hospital A [randomly split into training ( Results: Among 570 patients, the incidence of GI dysfunction was 35.61% (203/570). The XGBoost algorithm showed superior discrimination, with an AUC of 0.910 (95% CI: 0.878-0.941) in the training set, 0.851 (0.790-0.913) in the internal validation set, and 0.908 (0.837-0.979) in the external validation set. SHAP analysis identified five key predictors by importance: SOFA score, TBSA, inhalation injury, blood culture result, and hematuria. Conclusion: We developed and validated the first interpretable ML-based model for predicting GI dysfunction after severe burn injury, with XGBoost achieving high performance. This model could help identify high-risk patients for personalized pre-emptive management.