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Sleep apnoea is now regarded as a highly prevalent systemic, multimorbid, chronic disease requiring a combination of long-term home-based treatments. Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio-economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to 'Big Data' that, if used appropriately, has great potential for the benefit of health, well-being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep-disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real-life evidence-based medicine for sleep apnoea.
This article was published in the following journal.
Name: Respirology (Carlton, Vic.)
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Periods of sleep manifested by changes in EEG activity and certain behavioral correlates; includes Stage 1: sleep onset, drowsy sleep; Stage 2: light sleep; Stages 3 and 4: delta sleep, light sleep, deep sleep, telencephalic sleep.
Dyssomnias (i.e., insomnias or hypersomnias) associated with dysfunction of internal sleep mechanisms or secondary to a sleep-related medical disorder (e.g., sleep apnea, post-traumatic sleep disorders, etc.). (From Thorpy, Sleep Disorders Medicine, 1994, p187)
Movements or behaviors associated with sleep, sleep stages, or partial arousals from sleep that may impair sleep maintenance. Parasomnias are generally divided into four groups: arousal disorders, sleep-wake transition disorders, parasomnias of REM sleep, and nonspecific parasomnias. (From Thorpy, Sleep Disorders Medicine, 1994, p191)
A sleep disorder of central nervous system origin characterized by prolonged nocturnal sleep and periods of daytime drowsiness. Affected individuals experience difficulty with awakening in the morning and may have associated sleep drunkenness, automatic behaviors, and memory disturbances. This condition differs from narcolepsy in that daytime sleep periods are longer, there is no association with CATAPLEXY, and the multiple sleep latency onset test does not record sleep-onset rapid eye movement sleep. (From Chokroverty, Sleep Disorders Medicine, 1994, pp319-20; Psychiatry Clin Neurosci 1998 Apr:52(2):125-129)
Conditions characterized by disturbances of usual sleep patterns or behaviors. Sleep disorders may be divided into three major categories: DYSSOMNIAS (i.e. disorders characterized by insomnia or hypersomnia), PARASOMNIAS (abnormal sleep behaviors), and sleep disorders secondary to medical or psychiatric disorders. (From Thorpy, Sleep Disorders Medicine, 1994, p187)
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