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Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms

Medicine and Health

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms

M. Gadaleta, J. M. Radin, et al.

This exciting study led by Matteo Gadaleta, Jennifer M. Radin, and their team reveals the promising potential of a machine learning model to accurately detect COVID-19 infections using data from wearable devices. The research demonstrates how scalable and passive monitoring can be achieved even without self-reported symptoms, marking a significant advancement in public health monitoring.

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Playback language: English
Abstract
This study investigated the accuracy of a machine learning model in detecting COVID-19 infection using data from wearable devices and self-reported symptoms. A total of 38,911 individuals were enrolled, with 1118 testing positive and 7032 negative for COVID-19. An explainable gradient boosting prediction model achieved an AUC of 0.83 (symptomatic cohort) and 0.70 (all individuals, excluding self-reported symptoms). The model adapted to different sensor data and engagement levels, outperforming state-of-the-art algorithms in similar conditions. This approach enables scalable, passive monitoring of COVID-19 infection even in settings without self-reported symptoms.
Publisher
npj Digital Medicine
Published On
Dec 08, 2021
Authors
Matteo Gadaleta, Jennifer M. Radin, Katie Baca-Motes, Edward Ramos, Vik Khetarpal, Eric J. Topol, Steven R. Steinbulh, Giorgio Quer
Tags
COVID-19
machine learning
wearable devices
predictive model
passive monitoring
public health
sensor data

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