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

• URL: http://arxiv.org/abs/2211.11944v1
• Date: Tue, 22 Nov 2022 01:41:48 GMT
• Title: COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation
• Authors: Pengyuan Shi, Yuetong Wang, Saad Abbasi, Alexander Wong
• Abstract summary: We introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance.
• Score: 75.74756992992147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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