使用机器学习的新型光谱图的应用可以检测患者的呼吸机异步
Ishmael Obeso1, Benjamin Yoon1, David Ledbetter1
1Ishmael Obeso, Benjamin Yoon, David Ledbetter, Melissa Aczon, Eugene Laksana, Alice Zhou, Andrew Eckberg, Keith Mertan, Robinder G. Khemani, and Randall Wetzel are with the Children's Hospital Los Angeles, California.
Biomedical signal processing and control
|August 17, 2023
概括
患者-呼吸器失调 (PVD) 可能会伤害ICU患者. 卷积神经网络 (CNN) 准确地检测双循环呼吸和PVD亚型,使及时干预能够改善患者的结果.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 患者-呼吸器失调 (PVD) 是机械呼吸患者不良结果的重要危险因素.
- 现有的PVD检测方法的有效性有限,通常需要临床医生的大量时间和专业知识.
- 双循环 (DC) 呼吸,即为一个患者的努力提供两次呼吸器呼吸,是PVD的一个特别有害的后果.
研究的目的:
- 开发和验证使用卷积神经网络 (CNN) 识别PVD及其亚型的自动化方法.
- 通过提供独立于临床医生的专业知识的自动化PVD检测系统,使早期,有针对性的临床干预成为可能.
主要方法:
- 来自一项涉及患有急性呼吸困扰综合征的儿童的临床试验数据的二次分析.
- 从通风器波形 (流量,压力,食道测量) 来生成谱图.
- 训练CNN模型来检测DC呼吸和潜在的PVD亚型:反向触发器 (RT) 和不足的支持 (IS).
主要成果:
- 对于DC呼吸检测的CNN模型实现了接收器操作特征曲线 (AUROC) 下的面积为0.980.
- 对于底层PVD亚型的多目标检测模型实现了RT的AUROC为0.980,IS的AUROC为0.976.
- 在75%的灵敏度下,DC呼吸检测的需要提醒数 (NNA) 为1.3,而RT和IS的NNA分别为1.6和4.0,具有高特异性.
结论:
- 使用通风机波形的光谱图的CNN可以有效地识别DC呼吸.
- 这种自动化方法可以准确地检测潜在的PVD亚型,从而促进有针对性的临床干预.
- 开发的方法为在重症监护机构中早期检测和管理PVD提供了一个有前途的工具.
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