Rapidly scaling screening, testing and quarantine has shown to be an effective strategy to combat the COVID-19 pandemic. We consider the application of deep learning techniques to distinguish individuals with COVID from non-COVID by using data acquirable from a phone. Using cough and context (symptoms and meta-data) represent such a promising approach. Several independent works in this direction have shown promising results. However, none of them report performance across clinically relevant data splits. Specifically, the performance where the development and test sets are split in time (retrospective validation) and across sites (broad validation). Although there is meaningful generalization across these splits the performance significantly varies (up to 0.1 AUC score). In addition, we study the performance of symptomatic and asymptomatic individuals across these three splits. Finally, we show that our model focuses on meaningful features of the input, cough bouts for cough and relevant symptoms for context. The code and checkpoints are available at https://github.com/WadhwaniAI/cough-against-covid
翻译:快速扩大筛查、检测和检疫已证明是防治COVID-19大流行的有效战略。我们考虑运用深层次学习技术,通过使用从电话获得的数据,将COVID与非COVID区分开来。使用咳嗽和上下文(症状和元数据)代表了这样一种有希望的方法。在这方面的一些独立工作已经显示出有希望的结果。但是,没有一项独立工作能够报告临床相关数据的不同性能。具体来说,开发和测试组在时间上(反向验证)和地点间(广域验证)的性能是分开的。虽然这些功能之间有实际的概括性能差异很大(高达0.1 ACU得分)。此外,我们研究这三部分的症状和无症状个人的性能。最后,我们表明,我们的模型侧重于投入的有意义的特征、咳嗽和相关的症状。代码和检查站见https://github.com/WadhwaniAI/cough-agan-covid。