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

Authors

Michael R Kolesnikov, Phillip D Jenkins, Vishnu Mohan, Laszlo Kiraly, Karen Eden, Steven Bedrick

Published in

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. Volume 2026. Pages 239-248. Epub Jun 01, 2026.

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.

PMID:
42317825
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

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