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Testing for Obstructive Sleep Apnea by Area Socioeconomic Deprivation in Six Million Adults
This study assesses the rate of testing for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.
This study assesses the rate of testing for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.
This study assesses the rate of treatment for Obstructive Sleep Apnea analyzed through a lens of socioeconomic demographic data.
This study demonstrates potential to improve identification of RBD and RBD subtype-specific EEG biomarkers associated with synucleinopathy and PTSD/TASD.
This study features novel analytic methods for explainability, SBCP (spectral band cluster prevalence), with potential applications to Narcolepsy disorder-specific EEG biomarkers and AI understandability.
In this study, we evaluate whether patients are likely to comply with receiving a follow-up PSG following an indeterminate HSAT to rule out any presence of OSA and assess the demographic characteristics of individuals who are more likely to follow the AASM guidelines.
This research examined the feasibility for machine learning algorithms to improve upon screening for obstructive and central sleep apnea (SA) at the population health level using existing health insurance claims data.
This study examines the relationship between OSA Therapy and other key healthcare economics, including the prevalence of undiagnosed OSA, rate of diagnosed patients not starting continuous positive airway pressure (CPAP) therapy, spectrum of CPAP treatment adherence, and effects of concurrent co-morbidity.
This research abstract addresses various components and methods deployed in AI and covers examples of how AI is used to screen, endotype, diagnose, and treat sleep disorders.
What to expect at this year’s virtual SLEEP 2021 conference: forward thinking presentations, revolutionary data, and groundbreaking research.
In this study, we contribute to this growing body of clinical AI validation evidence and experimental design methodologies with an interoperable AI scoring engine for sleep studies in Adult and Pediatric populations.