Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health Records via Multimodal Fused Transformer
This work addresses early detection of cardiac arrest in pediatric patients, potentially improving care in intensive care units, but it appears incremental as it applies multimodal fusion to a specific domain.
The paper tackled early prediction of pediatric cardiac arrest in intensive care settings by introducing PedCA-FT, a transformer-based framework that fuses tabular and textual EHR views, resulting in outperformance of ten other AI models across five key metrics.
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.