Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records
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Computer Science > Machine Learning
arXiv:2607.16662 (cs)
[Submitted on 18 Jul 2026]
Title:Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records
View a PDF of the paper titled Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records, by Hazqeel Afyq Athaillah Kamarul Aryffin and 2 other authors
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Abstract:Accurate emergency triage decision is critical to avoid clinical deterioration, morbidity, and mortality. Machine learning-based triage system involves acquiring the main presenting complaint in text form and assessing vital signs in numerical data, enabling an automated and efficient analysis of patient information for timely and accurate prioritization of medical attention. However, modelling the intricacies of both data types requires a comprehensive understanding of the temporal structure and dependencies within the data. Thus, the aim of this study is to propose a multimodal deep learning architecture that can effectively handle both tabular and textual data. Furthermore, the proposed model exploits self-attention to to capture both local and global relationships between the features. A dataset consisting of 11,102 triage data collected from emergency department of Hospital Universiti Sains Malaysia is used for model development and validation. The proposed model demonstrated an increase of 1.95% in accuracy, 2.49% in F1-score, and 1.41% in ROC AUC compared to the baseline model. The experimental results demonstrated the potential of the proposed model in predicting triage decisions.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.16662 [cs.LG] |
| (or arXiv:2607.16662v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16662
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Mohd Halim Mohd Noor [view email][v1] Sat, 18 Jul 2026 06:35:23 UTC (559 KB)
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View a PDF of the paper titled Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records, by Hazqeel Afyq Athaillah Kamarul Aryffin and 2 other authors
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