AI RESEARCH
Improving Pediatric Emergency Department Triage with Modality Dropout in Late Fusion Multimodal EHR Models
arXiv CS.LG
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ArXi:2604.09905v1 Announce Type: new Emergency department triage relies heavily on both quantitative vital signs and qualitative clinical notes, yet multimodal machine learning models predicting triage acuity often suffer from modality collapse by over-relying on structured tabular data. This limitation severely hinders graphic generalizability, particularly for pediatric patients where developmental variations in vital signs make unstructured clinical narratives uniquely crucial.