论文标题
COVID网络助理：Covid-19症状预测和建议的深度学习驱动的虚拟助手
COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation
论文作者
论文摘要
随着COVID-19的大流行继续对全球医疗保健系统造成巨大负担，人们对寻找廉价症状的筛查和建议方法越来越兴趣，以帮助有效地使用可用的医疗资源（例如PCR测试）。在这项研究中，我们介绍了Covid-Net Assistant的设计，这是一个有效的虚拟助手，旨在通过深层卷积神经网络分析用户的咳嗽记录，旨在为Covid-19提供症状预测和建议。我们探索了通过机器驱动的设计探索（我们称为Covid-Net助手神经网络）生成的各种高度定制的，轻巧的卷积神经网络体系结构，在COVID19咳嗽基准数据集中。 COVID19咳嗽数据集包含来自COVID-19阳性队列的682次咳嗽记录和Covid-19阴性队列的642个。在标记为阳性的682个咳嗽记录中，通过PCR测试验证了382个记录。我们的实验结果表明有希望的是，Covid-NET助手神经网络表现出强大的预测性能，其AUC得分超过0.93，得分最高，超过0.95，同时推理快速有效。 Covid-NET助理模型是通过Covid-Net Open计划以开源方式提供的，尽管不是准备生产的解决方案，但我们希望它们的可用性是临床科学家，机器学习研究人员以及公民科学家开发创新解决方案的良好资源。
As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19 by analyzing users' cough recordings through deep convolutional neural networks. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration (which we refer to as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark dataset. The Covid19-Cough dataset comprises 682 cough recordings from a COVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682 cough recordings labeled positive, 382 recordings were verified by PCR test. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance, achieving AUC scores of over 0.93, with the best score over 0.95 while being fast and efficient in inference. The COVID-Net Assistant models are made available in an open source manner through the COVID-Net open initiative and, while not a production-ready solution, we hope their availability acts as a good resource for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative solutions.
