S1418 A Clustering-Based Machine Learning Approach for Mortality Prediction in Gastrointestinal Bleeding

Introduction: Gastrointestinal (GI) bleeding is a common, life‐threatening emergency with considerable morbidity and mortality. Traditional risk scores like AIMS65 and Glasgow-Blatchford Score (GBS) are limited in capturing non-linear clinical interactions. This study aimed to develop and validate a machine learning (ML) model employing a clustering-based ensemble approach to predict 30-day mortality in GI bleeding patients and to compare its performance with established risk scores. Methods: We retrospectively analyzed data from the MIMIC-IV-ED database, identifying 5,453 ED patients with GI bleeding (236 deaths; 4.32%) within 30 days. Sixteen clinical and laboratory variables were selected. The cohort was divided into training (80%) and test (20%) sets. Survivors were partitioned into 24 clusters using K-Means, and separate Random Forest models were trained on each cluster combined with all deceased cases. Overall performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across different thresholds. Results: The model achieved an AUC of 0.88 (95% CI: 0.86–0.90), significantly outperforming AIMS65 (0.71, 95% CI: 0.60–0.82) and GBS (0.74, 95% CI: 0.64–0.85) (P < 0.0001). At optimal thresholds, the model achieved a sensitivity of 86.8% and specificity of 74.6%. At maximal sensitivity thresholds (ranging between 90-100%), the model maintained a higher specificity (56.1%) than AIMS65 (35.6%) and GBS (17.1%). SHAP analysis identified age, hemodynamic instability, coagulopathy, and comorbidities as key predictors. Conclusion: Our clustering-based ensemble ML model provides a simple and superior risk stratification for 30-day mortality in GI bleeding compared to conventional scores, offering promising potential for real-time clinical integration.