用深度学习融合词嵌入与特征，提升急诊患者分流预测准确率
EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition Analysis
- 将类别特征与数值特征融合嵌入，构建多模态神经网络
- 在MIMIC-IV-ED数据集上达到96%准确率，优于现有方法
- 适合医疗智能决策系统开发人员参考使用
急诊科拥挤问题日益严重，主要源于人口老龄化和医疗成本上升。患者分流决策因此变得更加复杂，资源紧张使高危患者需优先处理。本文提出EF-Net模型，通过将类别特征嵌入神经网络层，并融合数值特征，实现对患者分流的精准预测。进一步结合XGBoost模型，采用软投票机制构建集成模型。实验结果表明，在MIMIC-IV-ED数据集上，单个EF-Net模型准确率达95.33%，集成模型准确率提升至96%。该模型在准确率、AUROC和F1分数上均优于现有方法，展现出良好的可扩展性，可作为急诊患者分流评估的实用解决方案。代码已公开于https://github.com/nafisa67/thesis。
原文摘要 · Abstract (English)
One of the most urgent problems is the overcrowding in emergency departments (EDs), caused by an aging population and rising healthcare costs. Patient dispositions have become more complex as a result of the strain on hospital infrastructure and the scarcity of medical resources. Individuals with more dangerous health issues should be prioritized in the emergency room. Thus, our research aims to develop a prediction model for patient disposition using EF-Net. This model will incorporate categorical features into the neural network layer and add numerical features with the embedded categorical features. We combine the EF-Net and XGBoost models to attain higher accuracy in our results. The result is generated using the soft voting technique. In EF-Net, we attained an accuracy of 95.33%, whereas in the Ensemble Model, we achieved an accuracy of 96%. The experiment's analysis shows that EF-Net surpasses existing works in accuracy, AUROC, and F1-Score on the MIMIC-IV-ED dataset, demonstrating its potential as a scalable solution for patient disposition assessment. Our code is available at https://github.com/nafisa67/thesis
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