arXiv Machine Learning July 21, 2026 By Hazqeel Afyq Athaillah Kamarul Aryffin, Kamarul Aryffin Baharuddin, Mohd Halim Mohd Noor

Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records

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arXiv:2607. 16662v1 Announce Type: new Abstract: Accurate emergency triage decision is critical to avoid clinical deterioration, morbidity, and mortality.

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multimodal

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Large Language Models as Unified Multimodal Learners for Clinical Prediction

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llms fine-tuning multimodal

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The paper introduces the Cross‑Modal Triage Network (CMTN), a multimodal deep‑learning model that fuses a Swin Transformer V2 visual encoder with a PubMedBERT text encoder to perform severity‑based triage, pathology detection, and generate visual explanations for chest radiographs. Trained on 34,639 image‑text pairs from MIMIC‑CXR‑JPG, the CMTN achieves high ordinal agreement with reference labels (QWK = 0.9341) and excellent pathology detection (macro‑AUROC = 0.9970) while operating with 34 ms latency. However, a blinded clinical audit revealed low agreement with expert radiologists (QWK = 0.1399) and only modest spatial‑semantic concordance in heatmaps, underscoring the gap between algorithmic performance and clinical judgment.

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llms nlp multimodal benchmarks

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A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs

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multimodal benchmarks safety

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