Big data in sleep medicine: prospects and pitfalls in phenotyping
Westover, M Brandon
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CitationBianchi, Matt T, Kathryn Russo, Harriett Gabbidon, Tiaundra Smith, Balaji Goparaju, and M Brandon Westover. 2017. “Big data in sleep medicine: prospects and pitfalls in phenotyping.” Nature and Science of Sleep 9 (1): 11-29. doi:10.2147/NSS.S130141. http://dx.doi.org/10.2147/NSS.S130141.
AbstractClinical polysomnography (PSG) databases are a rich resource in the era of “big data” analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea–hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:31731751
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