Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health Records via Multimodal Fused Transformer
Journal:
arXiv
Published Date:
Feb 11, 2025
Abstract
Early prediction of pediatric cardiac arrest (CA) is critical for timely
intervention in high-risk intensive care settings. We introduce PedCA-FT, a
novel transformer-based framework that fuses tabular view of EHR with the
derived textual view of EHR to fully unleash the interactions of
high-dimensional risk factors and their dynamics. By employing dedicated
transformer modules for each modality view, PedCA-FT captures complex temporal
and contextual patterns to produce robust CA risk estimates. Evaluated on a
curated pediatric cohort from the CHOA-CICU database, our approach outperforms
ten other artificial intelligence models across five key performance metrics
and identifies clinically meaningful risk factors. These findings underscore
the potential of multimodal fusion techniques to enhance early CA detection and
improve patient care.
