ArXiv Preprint
Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has
been interest in using artificial intelligence methods to predict COVID-19
infection status based on vocal audio signals, for example cough recordings.
However, existing studies have limitations in terms of data collection and of
the assessment of the performances of the proposed predictive models. This
paper rigorously assesses state-of-the-art machine learning techniques used to
predict COVID-19 infection status based on vocal audio signals, using a dataset
collected by the UK Health Security Agency. This dataset includes acoustic
recordings and extensive study participant meta-data. We provide guidelines on
testing the performance of methods to classify COVID-19 infection status based
on acoustic features and we discuss how these can be extended more generally to
the development and assessment of predictive methods based on public health
datasets.
Davide Pigoli, Kieran Baker, Jobie Budd, Lorraine Butler, Harry Coppock, Sabrina Egglestone, Steven G. Gilmour, Chris Holmes, David Hurley, Radka Jersakova, Ivan Kiskin, Vasiliki Koutra, Jonathon Mellor, George Nicholson, Joe Packham, Selina Patel, Richard Payne, Stephen J. Roberts, Björn W. Schuller, Ana Tendero-Cañadas, Tracey Thornley, Alexander Titcomb
2022-12-15
