Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework.

ABSTRACT: Clarifying whether physiological sleep measures predict mortality could inform risk screening; however, such investigations should account for complex and potentially non-linear relationships among health risk factors. We aimed to establish the predictive utility of polysomnography (PSG)-assessed sleep measures for mortality using a novel permutation random forest (PRF) machine learning framework. Data collected from the years 1995 to present are from the Sleep Heart Health Study (SHHS; n = 5,734) and the Wisconsin Sleep Cohort Study (WSCS; n = 1,015), and include initial assessments of sleep and health, and up to 15 years of follow-up for all-cause mortality. We applied PRF models to quantify the predictive abilities of 24 measures grouped into five domains: PSG-assessed sleep (four measu

SUBMITTER: Wallace ML 

PROVIDER: S-EPMC8591145 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature