基于多模态注意力的深度学习用于电子健康记录的急诊分诊
Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records
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- Hospital Universiti Sains Malaysia（马来西亚理科大学医院）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
研究旨在提出多模态深度学习架构用于急诊分诊，利用自注意力捕捉特征关系，处理表格和文本数据。用马来西亚理科大学医院急诊科数据集验证，该模型比基线模型在准确率、F-1分数和ROC AUC上均有提升，展现出预测分诊决策的潜力。
中文摘要 AI 辅助
准确的急诊分诊决策对于避免临床恶化、发病和死亡至关重要。基于机器学习的分诊系统涉及获取文本形式的主要主诉并评估数值数据中的生命体征，以便对患者信息进行自动化和高效分析，及时准确地确定医疗护理优先级。然而，对这两种数据类型的复杂性进行建模需要全面了解数据中的时间结构和依赖性。因此，本研究旨在提出一种能够有效处理表格和文本数据的多模态深度学习架构。此外，所提出的模型利用自注意力来捕捉特征之间的局部和全局关系。使用从马来西亚理科大学医院急诊科收集的11102份分诊数据组成的数据集进行模型开发和验证。与基线模型相比，所提出的模型在准确率上提高了1.95%，F1分数提高了2.49%，ROC AUC提高了1.41%。实验结果证明了所提出模型在预测分诊决策方面的潜力。
英文摘要
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.
