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

翻译：急诊科（ED）人满为患是当前最紧迫的问题之一，其根源在于人口老龄化和医疗成本上升。医院基础设施承受压力以及医疗资源稀缺，导致患者处置变得更为复杂。在急诊室中，应优先处理健康状况更危急的患者。因此，本研究旨在利用EF-Net开发一个患者处置预测模型。该模型将分类特征整合到神经网络层中，并将数值特征与嵌入的分类特征相结合。我们结合EF-Net与XGBoost模型，以获得更高的结果准确性。最终结果采用软投票技术生成。在EF-Net中，我们获得了95.33%的准确率，而在集成模型中，我们实现了96%的准确率。实验分析表明，在MIMIC-IV-ED数据集上，EF-Net在准确率、AUROC和F1分数方面均超越了现有工作，证明了其作为可扩展的患者处置评估方案的潜力。我们的代码可在 https://github.com/nafisa67/thesis 获取。