医学
睡眠呼吸暂停
阻塞性睡眠呼吸暂停
持续气道正压
呼吸暂停
睡眠(系统调用)
呼吸
梅德林
人工智能
机器学习
重症监护医学
内科学
计算机科学
麻醉
法学
政治学
操作系统
作者
George Bazoukis,Sandeep Chandra Bollepalli,Cheuk To Chung,Xinmu Li,Gary Tse,Bethany L. Bartley,Salma Batool-Anwar,Stuart F. Quan,Antonis A. Armoundas
出处
期刊:Journal of Clinical Sleep Medicine
[American Academy of Sleep Medicine]
日期:2023-07-01
卷期号:19 (7): 1337-1363
被引量:3
摘要
Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders.A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed.Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models.The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently.Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
科研通智能强力驱动
Strongly Powered by AbleSci AI