Guo Muzhe, Nguyen Long, Du Hongfei, Jin Fang
Department of Statistics, The George Washington University, Washington, DC, United States.
Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States.
Front Big Data. 2022 Apr 28;5:801998. doi: 10.3389/fdata.2022.801998. eCollection 2022.
Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short time. This paper proposes a model which combines Long-short Term Memory (LSTM) and Deep Neural Network (DNN) to early and accurately classify disease stages of the patients to address the problem at a low cost. In this model, the LSTM component will exploit temporal features while the DNN component extracts attributed features to enhance the model's classification performance. Our experimental results demonstrate that the proposed model achieves substantially better prediction accuracy than existing state-of-art methods. Moreover, we explore the importance of different vital indicators to help patients and doctors identify the critical factors at different COVID-19 stages. Finally, we create case studies demonstrating the differences between severe and mild patients and show the signs of recovery from COVID-19 disease by extracting shape patterns based on temporal features of patients. In summary, by identifying the disease stages, this research will help patients understand their current disease situation. Furthermore, it will also help doctors to provide patients with an immediate treatment plan remotely that addresses their specific disease stages, thus optimizing their usage of limited medical resources.
2019冠状病毒病（COVID-19）是一种传染病，在全球范围内导致医院资源不堪重负。因此，决定将COVID-19患者收治入院还是居家隔离，成为在短时间内管理大量患者的关键解决方案。本文提出了一种结合长短期记忆网络（LSTM）和深度神经网络（DNN）的模型，用于早期准确地对患者的疾病阶段进行分类，以低成本解决该问题。在这个模型中，LSTM组件将利用时间特征，而DNN组件提取属性特征以提高模型的分类性能。我们的实验结果表明，所提出的模型比现有的先进方法具有显著更好的预测准确性。此外，我们探究了不同生命体征指标的重要性，以帮助患者和医生识别COVID-19不同阶段的关键因素。最后，我们创建了案例研究，展示了重症和轻症患者之间的差异，并通过基于患者时间特征提取形状模式来显示COVID-19疾病的康复迹象。总之，通过识别疾病阶段，本研究将帮助患者了解其当前的病情。此外，它还将帮助医生远程为患者提供针对其特定疾病阶段的即时治疗方案，从而优化有限医疗资源的使用。
