Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment
Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment
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Abstract
Description
Keywords
Explainable Artificial Intelligence, Machine Learning, Obstructive Sleep Apnea, Polysomnography, Random Forest, Sleep Architecture, Sleep Disturbance Ratio, XGBoost
Fields of Science
Citation
WoS Q
Scopus Q
Source
Volume
16
Issue
16
Start Page
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