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We reviewed 254 scientific articles that use machine learning in obstructive sleep apnea research, covering quantitative analysis of demographics of study samples, the range of tasks addressed by. Objective this narrative review evaluates the application of machine learning in osa research, focusing on model performance, dataset characteristics, demographic representation, and validation strategies. We evaluated the landscape of machine learning in osa research by assessing the techniques used, application areas, model evaluation strategies, and dataset characteristics across studies.

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The review analyzed 254 studies published between 2018 and 2023, finding that most applied machine learning to classify and diagnose obstructive sleep apnea (osa), with deep learning being the most. Our analysis revealed that the majority of machine learning applications focused on osa classification and diagnosis, utilizing various data sources such as polysomnography,. Our analysis revealed that most machine learning applications focused on osa classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and.

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This narrative review examines data extracted from 254 scientific publications published in the pubmed database between january 2018 and march 2023

Studies were categorized by machine learning. Despite these reviews, no review to our knowledge has made a broader investigation into different applications of machine learning beyond osa diagnosis, and a detailed analysis of the different data.