机器学习应用在阻塞性睡眠呼吸暂停中的状态和机会:叙述性审查
Matheus Lima Diniz Araujo1, Trevor Winger2, Samer Ghosn1
1Cleveland Clinic Foundation, Cleveland, OH, USA.
medRxiv : the preprint server for health sciences
|March 10, 2025
概括
机器学习对阻塞性睡眠呼吸暂停 (OSA) 研究有希望,但需要更多的数据和强有力的验证. 未来的研究必须解决在OSA诊断和治疗中公平的AI应用的人口差距.
科学领域:
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种常见且严重的睡眠障碍,在睡眠期间有呼吸暂停.
- 机器学习 (ML) 在OSA研究中越来越多地用于诊断,治疗和理解疾病机制.
研究的目的:
- 审查ML在OSA研究中的应用.
- 分析模型性能,数据集,人口统计和验证方法.
- 确定未来研究和临床决策的趋势和差距.
主要方法:
- 254个PubMed出版物的叙事审查 (2018年1月至2023年3月).
- 根据ML应用,模型,任务,验证指标,数据源和人口统计数据对研究进行分类.
主要成果:
- 机器学习应用主要集中在使用各种数据源 (PSG,ECG,可穿戴设备) 的OSA分类和诊断上.
- 研究队伍主要是超重的男性,女性代表性低,年轻的肥胖成年人,老年人以及各种种族群体.
- 许多研究采用了小样本,缺乏强大的模型验证.
结论:
- 具有更大,更具代表性的数据集和偏差缓解的包容性研究对于OSA中可概括的ML模型至关重要.
- 解决人口和方法差距对于OSA医疗保健中的强大和公平的AI至关重要.
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