机器学习在机械通风期间的患者-呼吸机异步中的应用进展:系统性审查
Guanxu Jiang1,2, Jiawei Ma2, Hao Xu2
1Wuxi Medical College of Jiangnan University, Wuxi, 214122, China.
Critical care (London, England)
|July 10, 2025
概括
机器学习 (ML) 显示出在机械通风过程中检测患者-呼吸器异步 (PVA) 的前景. 然而,广泛的临床使用需要标准化定义,外部验证,以及改善ML方法的PVA识别和预测报告.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 呼吸系统护理 呼吸系统护理
背景情况:
- 患者 - 呼吸器异步 (PVA) 是机械呼吸的常见并发症,需要手动评估.
- 机器学习 (ML) 为自动化PVA检测和预测提供了一个机会.
研究的目的:
- 系统地审查ML方法和在PVA检测和预测中的性能.
- 为了评估机械通风期间PVA的ML应用的当前状态.
主要方法:
- 在PubMed,Embase,Web of Science和Cochrane数据库中的系统文献搜索 (截至2024年11月).
- 包含原始研究,应用ML技术来评估机械呼吸的成年患者的PVA.
- 对方法和技术特征进行精细的质量评估的开发.
主要成果:
- 包含了14项研究,用于19种PVA类型的11种ML方法.
- ML模型显示了PVA识别的高性能 (F1得分为0.731-0.988).
- 报告指出,外部验证有限,对类不平衡和超参数调整的报告不一致.
结论:
- ML模型显示了PVA识别和预测的巨大潜力.
- 临床应用受到方法异质性,可变定义和不充分的验证的阻碍.
- 未来的研究需要标准化定义,外部验证和详细的报告来实现可重复性.
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