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已发表论文
成人阻塞性睡眠呼吸暂停筛查的机器学习预测模型研究进展
Authors Li S , Huang J , Xiao Z, Fan C
Received 17 March 2025
Accepted for publication 1 September 2025
Published 7 October 2025 Volume 2025:17 Pages 2575—2595
DOI https://doi.org/10.2147/NSS.S526631
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Valentina Alfonsi
Shiyuan Li,1,2,* Jiewei Huang,1,* Ziheng Xiao,1,2,* Chunmei Fan1
1The Clinical Laboratory Center of The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, People’s Republic of China; 2The Graduate School of Fujian Medical University, Fuzhou, Fujian Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Chunmei Fan, The Clinical Laboratory Center of The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, People’s Republic of China, Tel +8615906069575, Email fanchunmei9575@163.com
Abstract: Obstructive sleep apnea (OSA) is a global health problem. Patients with OSA may experience the upper airway collapsing during sleep, resulting in decreased oxygen saturation and sleep disruption, which is characterized by hypoxemia and sleep fragmentation, thereby reducing sleep quality and harming quality of life. In addition, OSA is associated with the occurrence of a variety of systemic diseases, which brings a huge burden to public health. Therefore, timely diagnosis of OSA is crucial. Polysomnography (PSG) is the most accurate method for diagnosing OSA at present, which can be used to determine the severity of sleep apnea and to monitor therapeutic efficacy. However, the PSG is difficult to be popularized because of its cumbersome operation, patients’ non-compliance, and expensive medical expenses. Therefore, it is imperative to find a convenient and fast OSA diagnosis method. In recent years, the development of machine learning prediction models and their application in the medical field have provided a new method for OSA severity diagnosis, making it possible to identify OSA severities efficiently and accurately. The purpose of this paper is to review relevant research on machine learning prediction models for OSA severity diagnosis and to provide sleep specialists with recommendations for more effective early identification and diagnosis of OSA. In addition, the challenges faced by machine learning at the level of diagnostic applications are summarized and future trends are envisioned.
Keywords: obstructive sleep apnea, machine learning, prediction model, obesity
1The Clinical Laboratory Center of The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, People’s Republic of China; 2The Graduate School of Fujian Medical University, Fuzhou, Fujian Province, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Chunmei Fan, The Clinical Laboratory Center of The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, People’s Republic of China, Tel +8615906069575, Email fanchunmei9575@163.com
Abstract: Obstructive sleep apnea (OSA) is a global health problem. Patients with OSA may experience the upper airway collapsing during sleep, resulting in decreased oxygen saturation and sleep disruption, which is characterized by hypoxemia and sleep fragmentation, thereby reducing sleep quality and harming quality of life. In addition, OSA is associated with the occurrence of a variety of systemic diseases, which brings a huge burden to public health. Therefore, timely diagnosis of OSA is crucial. Polysomnography (PSG) is the most accurate method for diagnosing OSA at present, which can be used to determine the severity of sleep apnea and to monitor therapeutic efficacy. However, the PSG is difficult to be popularized because of its cumbersome operation, patients’ non-compliance, and expensive medical expenses. Therefore, it is imperative to find a convenient and fast OSA diagnosis method. In recent years, the development of machine learning prediction models and their application in the medical field have provided a new method for OSA severity diagnosis, making it possible to identify OSA severities efficiently and accurately. The purpose of this paper is to review relevant research on machine learning prediction models for OSA severity diagnosis and to provide sleep specialists with recommendations for more effective early identification and diagnosis of OSA. In addition, the challenges faced by machine learning at the level of diagnostic applications are summarized and future trends are envisioned.
Keywords: obstructive sleep apnea, machine learning, prediction model, obesity