Title: Multimodal Fusion of Clinical and Imaging Data for Early Prediction of Massive Transfusion in Trauma

Massive transfusion (MT) prediction in trauma remains limited by scoring systems with suboptimal discrimination and generalizability. We developed a multimodal joint fusion model integrating structured electronic health record (EHR) data with admission chest X-rays to support early MT risk stratification. From 33,824 trauma patients (2014-2024), 10,090 met inclusion criteria, including 226 MT cases (2.24%). Structured presentation variables were modeled using a multilayer perceptron, while imaging features were extracted using a pretrained DenseNet-121; intermediate feature fusion enabled end-to-end learning. Class imbalance was addressed with SMOTE for structured data and geometric augmentation for imaging. On the held-out validation set, the fusion model achieved an AUC of 0.669. At the selected threshold, sensitivity was 0.72 and specificity 0.84. Grad-CAM visualizations demonstrated attention over clinically relevant thoracoabdominal regions. Despite limited positive predictive value due to low event prevalence, these findings demonstrate feasibility of workflow-aligned multimodal MT prediction using routinely available data, warranting prospective multi-center validation.