학술논문

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.
Document Type
Article
Source
NPJ Digital Medicine; 5/20/2024, Vol. 7 Issue 1, p1-10, 10p
Subject
MORTALITY risk factors
SELF-evaluation
RISK assessment
RESEARCH funding
ACCELEROMETERS
WEARABLE technology
EVALUATION of medical care
SLEEP duration
LONGITUDINAL method
RESEARCH
RAPID eye movement sleep
DEEP learning
LEARNING strategies
SLEEP quality
POLYSOMNOGRAPHY
CONFIDENCE intervals
SLEEP disorders
Language
ISSN
23986352
Abstract
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