Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
Department of Medicine, Harvard Medical School, Boston, MA, USA.
Neurotherapeutics. 2021 Jan;18(1):228-243. doi: 10.1007/s13311-021-01014-9. Epub 2021 Apr 7.
Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.
流行病学睡眠研究旨在确定睡眠影响人类健康的相互作用和因果机制，并设计改善整个生命周期睡眠的干预策略。通过进一步关注睡眠障碍的环境和遗传病因学，以及开发风险分层算法来识别有睡眠障碍风险或受其影响的人群，可以推进这些目标。这些研究依赖于全面的睡眠相关数据，这些数据通常包含跨越多个时间点的复杂多维生理和分子测量。因此，睡眠研究非常适合应用能够处理高维数据的计算方法。在这里，我们调查了机器学习和深度学习的最新进展，以及具有睡眠数据的大型人类队列研究的可用性，这些研究可以共同推动睡眠研究领域的下一个突破。我们描述了与睡眠相关的数据类型和数据集，并介绍了该领域中一些可以作为算法方法目标的任务，以及在追求这些任务时面临的挑战和机遇。
