论文标题
使用深度学习方法对Covid-19的早期预测相关的住院激增
An early prediction of covid-19 associated hospitalization surge using deep learning approach
论文作者
论文摘要
Covid-19引起的全球大流行在各个方面都影响我们的生活。截至9月11日，超过2800万人对COVID-19的感染呈阳性，超过911,000人在这场病毒之战中丧生。由于住院量的限制和ICU床短缺，一些患者无法接受适当的治疗。估计的未来住院至关重要，因此可以根据需要分配医疗资源。在这项研究中，我们建议使用4个复发性神经网络来推断下一周的住院变化，而当前一周。结果表明，序列模型的序列在住院预测中的高精度为0.938，AUC的高度为0.938，AUC为0.850。我们的工作有可能预测住院需求，并在重新启动时向医疗提供者和其他利益相关者发出警告。
The global pandemic caused by COVID-19 affects our lives in all aspects. As of September 11, more than 28 million people have tested positive for COVID-19 infection, and more than 911,000 people have lost their lives in this virus battle. Some patients can not receive appropriate medical treatment due the limits of hospitalization volume and shortage of ICU beds. An estimated future hospitalization is critical so that medical resources can be allocated as needed. In this study, we propose to use 4 recurrent neural networks to infer hospitalization change for the following week compared with the current week. Results show that sequence to sequence model with attention achieves a high accuracy of 0.938 and AUC of 0.850 in the hospitalization prediction. Our work has the potential to predict the hospitalization need and send a warning to medical providers and other stakeholders when a re-surge initializes.
