  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.
