An approach to a wrist wearable based Covid-19 prediction system to protect Health Care Professionals.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 2459-2463, 2022 07.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2018737
ABSTRACT
With healthcare professionals being the frontline warriors in battling the Covid pandemic, their risk of exposure to the virus is extremely high. We present our experience in building a system, aimed at monitoring the physiology of these professionals 24/7, with the hope of providing timely detection of infection and thereby better care. We discuss a machine learning approach and model using signals from a wrist wearable device to predict infection at a very early stage. In a double-blind test on a small group of patients, our model could successfully identify the infected versus non-infected cases with near 100% accuracy. We also discuss some of the challenges we faced, both technical and non-technical, to get this system off the ground as well as offer some suggestions that could help tackle these hurdles.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Dispositivos Electrónicos Vestibles
/
COVID-19
Tipo de estudio:
Estudios diagnósticos
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Humanos
Idioma:
Inglés
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2022
Tipo del documento:
Artículo
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