Statistics in medicine · 2024
Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pigoli Davide, Baker Kieran, Budd Jobie, Butler Lorraine, Coppock Harry, Egglestone Sabrina, Gilmour Steven G, Holmes Chris, Hurley David, Jersakova Radka, Kiskin Ivan, Koutra Vasiliki, Mellor Jonathon, Nicholson George, Packham Joe, Patel Selina, Payne Richard, Roberts Stephen J, Schuller Björn W, Tendero-Cañadas Ana, Thornley Tracey, Titcomb Alexander
Аннотация
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 limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing-RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS-CoV-2 infection status and extensive study participant meta-data. This allowed us to rigorously assess state-of-the-art machine learning techniques to predict SARS-CoV-2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks.