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As the COVID-19 pandemic continues to put a significant burden on healthcaresystems worldwide, there has been growing interest in finding inexpensivesymptom pre-screening and recommendation methods to assist in efficiently usingavailable medical resources such as PCR tests. In this study, we introduce thedesign of COVID-Net Assistant, an efficient virtual assistant designed toprovide symptom prediction and recommendations for COVID-19 by analyzing users'cough recordings through deep convolutional neural networks. We explore avariety of highly customized, lightweight convolutional neural networkarchitectures generated via machine-driven design exploration (which we referto as COVID-Net Assistant neural networks) on the Covid19-Cough benchmarkdataset. The Covid19-Cough dataset comprises 682 cough recordings from aCOVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682cough recordings labeled positive, 382 recordings were verified by PCR test.Our experimental results show promising, with the COVID-Net Assistant neuralnetworks demonstrating robust predictive performance, achieving AUC scores ofover 0.93, with the best score over 0.95 while being fast and efficient ininference. The COVID-Net Assistant models are made available in an open sourcemanner through the COVID-Net open initiative and, while not a production-readysolution, we hope their availability acts as a good resource for clinicalscientists, machine learning researchers, as well as citizen scientists todevelop innovative solutions.

COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation