Wearable sensor data and self-reported symptoms for COVID-19 detection

By G. Quer, J. Radin, M. Gadaleta, Katie Baca-Motes, Lauren Ariniello, Edward Ramos, V. Kheterpal, Eric J. Topol, Steven R. Steinhubl. Nature Medicine, 2020

397 citations recorded by the source corpus.

Subfields: Information Systems, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c15a93ad91e17412f464e4447ba0fd3e70544bbb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Pranav Rajpurkar
2. J. Acosta
3. G. Falcone
4. Eric J. Topol

Citations in the corpus

• End-to-end design of wearable sensors (2022, methodology): “2012 Integrated wearable sensor arrays for multiplexed perspiration analysis(10) Paper-based wearable sensors for continuous breath chemistry monitoring(99) First wearable glucose monitoring system (‘FreeStyle Libre’ by Abbott) Smartwatches for the early detection of symptomatic and pre-symptomatic ”
• Multimodal biomedical AI (2022, background): “…considering participant self-reported symptoms and sensor metrics improved performance relative to either modality alone, reaching an area under the receiver operating curve value of 0.80 (95% confidence interval 0.73–0.86) for classifying COVID-19-positive versus COVID-19-negative status 61 .”