Assessing the performance of machine learning methods trained on public health observational data: a case study from COVID-19
(2024)
Journal Article
Pigoli, D., Baker, K., Budd, J., Butler, L., Coppock, H., Egglestone, S., Gilmour, S., Holmes, C., Hurley, D., Jersakova, R., Kiskin, I., Koutra, V., Mellor, J., Nicholson, G., Packham, J., Patel, S., Roberts, S. J., Schuller, B. W., Tendero-Cañadas, A., Thornley, T., & Titcomb, A. (2024). Assessing the performance of machine learning methods trained on public health observational data: a case study from COVID-19. Statistics in Medicine, https://doi.org/10.1002/sim.10211
From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitati... Read More about Assessing the performance of machine learning methods trained on public health observational data: a case study from COVID-19.