A CPAP data-based algorithm for automatic early prediction of therapy adherence.

AbdelKebir SabilMarc Le VaillantChristy StittFrançois GoupilThierry PigeanneLaurene Leclair-VisonneauPhilippe MassonAcya Bizieux-ThaminyMarie-Pierre HumeauNicole MeslierFrédéric Gagnadoux

Published in: Sleep & breathing = Schlaf & Atmung (2020)

Automatic telemonitoring algorithms are relevant tools for early prediction of CPAP therapy adherence and may make it possible to focus therapeutic follow-up efforts on patients who are at risk of non-adherence.

Keyphrases

deep learning
machine learning
obstructive sleep apnea
end stage renal disease
ejection fraction
newly diagnosed
big data
sleep apnea
artificial intelligence
glycemic control
type diabetes
electronic health record
adipose tissue
bone marrow
insulin resistance