@article{Kolesnikov:2026aa,
 abstract = {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.},
 author = {Kolesnikov, Michael R and Jenkins, Phillip D and Mohan, Vishnu and Kiraly, Laszlo and Eden, Karen and Bedrick, Steven},
 date-added = {2026-06-22 17:43:02 -0700},
 date-modified = {2026-06-25 09:55:25 -0700},
 journal = {AMIA Jt Summits Transl Sci Proc},
 journal-full = {AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science},
 month = {June},
 pages = {239-248},
 pmc = {PMC13274277},
 pmid = {42317825},
 pst = {epublish},
 title = {Title: Multimodal Fusion of Clinical and Imaging Data for Early Prediction of Massive Transfusion in Trauma},
 volume = {2026},
 year = {2026}
}