Multi-task machine learning for transfusion decision support in acute upper gastrointestinal bleeding: a novel ensemble approach with clinical validation

In the classification task, the model achieved an area under the curve (AUC) of 0.965, representing a 20.5% improvement over the traditional Glasgow-Blatchford Score (GBS). In the regression task, the two-stage stacked regressor (TSR) outperformed other machine learning models in predicting transfusion type and volume , significantly reducing the prediction error of transfusion volume. Compared to random forest (RF), XGBoost, multilayer perceptron (MLP), and backpropagation neural network (BP), the model reduced the overall loss by 9.9%, 21.0%, 38.3%, and 10.0%, respectively, validating the advantages of hierarchical feature selection and dynamic task-weighting. In the external validation set, the model exhibited favorable generalization performance with an AUC of 0.860, and showed good performance in infusion volume prediction error and weighted loss CONCLUSIONS: This study presents a robust and interpretable multi-task learning model for transfusion decision-making in AUGIB. By jointly optimizing classification and regression tasks through hierarchical feature selection and dynamic loss allocation, the model supports precision transfusion strategies and holds strong potential for broader application in emergency and critical care settings.