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J Clin Sleep Med. 2019 Aug 15;15(8):1135-1142. doi: 10.5664/jcsm.7806.
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Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis.基于文献的分析估计全球阻塞性睡眠呼吸暂停的患病率和负担。
Lancet Respir Med. 2019 Aug;7(8):687-698. doi: 10.1016/S2213-2600(19)30198-5. Epub 2019 Jul 9.
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Embracing change, responding to challenge, and looking toward the future.拥抱变化,应对挑战,展望未来。
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Burden of sleep apnea: rationale, design, and major findings of the Wisconsin Sleep Cohort study.睡眠呼吸暂停的负担:威斯康星睡眠队列研究的基本原理、设计与主要发现
WMJ. 2009 Aug;108(5):246-9.
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Invited commentary from organizations on the draft proposal released by the Centers for Medicare & Medicaid Services for modification of national coverage determination 240.4.各组织就医疗保险和医疗补助服务中心发布的关于修改国家覆盖范围判定240.4的提案草案发表的特邀评论。
J Clin Sleep Med. 2008 Feb 15;4(1):77.
7
The predictive value of clinical and epidemiological parameters in the identification of patients with obstructive sleep apnoea (OSA): a clinical prediction algorithm in the evaluation of OSA.临床和流行病学参数在阻塞性睡眠呼吸暂停(OSA)患者识别中的预测价值:一种评估OSA的临床预测算法
Eur Arch Otorhinolaryngol. 2007 Jun;264(6):637-43. doi: 10.1007/s00405-006-0241-5. Epub 2007 Jan 26.
8
Polysomnography indexes are discordant with quality of life, symptoms, and reaction times in sleep apnea patients.多导睡眠图指标与睡眠呼吸暂停患者的生活质量、症状及反应时间不一致。
Otolaryngol Head Neck Surg. 2005 Feb;132(2):255-62. doi: 10.1016/j.otohns.2004.11.001.

机器学习时代的睡眠呼吸暂停面孔。

The faces of sleep apnea in the age of machine learning.

机构信息

ENT and Allergy Associates, New York, New York.

School of Medicine, University of Wollongong, Wollongong, Australia.

出版信息

J Clin Sleep Med. 2020 Apr 15;16(4):469-470. doi: 10.5664/jcsm.8402.

DOI:10.5664/jcsm.8402
PMID:32105211
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7161451/
Abstract

Jacobowitz O, MacKay S. The faces of sleep apnea in the age of machine learning. . 2020;16(4):469–470.

摘要

雅各布维茨 O,麦凯 S。机器学习时代的睡眠呼吸暂停之面。柳叶刀呼吸医学。2020;16(4):469-470.