机械通风患者的患者-通风器异步分类:基于模型或机器学习方法?
Christopher Yew Shuen Ang1, Yeong Shiong Chiew2, Xin Wang1
1School of Engineering, Monash University Malaysia, Selangor, Malaysia.
Computer methods and programs in biomedicine
|July 19, 2024
概括
通过使用基于规则的方法 (如歇斯底里循环分析 (HLA) 和机器学习模型) 的自动化患者-呼吸机异步 (PVA) 检测显示了持续监测的前景. 在检测PVA和非PVA事件方面,HLA表现优越.
科学领域:
- 生物医学工程 生物医学工程
- 呼吸系统医学 呼吸系统医学
- 医疗保健中的人工智能
背景情况:
- 患者呼吸器异步 (PVA) 与不良临床结果有关,目前监测不足.
- 需要自动化PVA检测系统来克服标准观测方法的局限性.
- 现有的基于模型和机器学习的PVA检测方法表现出可变的性能,可能会错过特定的PVA事件.
研究的目的:
- 为了比较基于规则的算法 (歇斯底里循环分析 - HLA) 与机器学习模型 (三输入卷积神经网络 - TCNN) 的性能,以检测患者-呼吸器异步 (PVA).
- 用一个独立的患者队列来追溯验证两种PVA检测方法.
主要方法:
- 歇斯底里环分析 (HLA),基于规则的方法 (RBM) 和三输入卷积神经网络 (TCNN) 用于分类七种类型的PVA.
- 使用类激活映射 (CAM) 热图可视化影响TCNN决策的波形段,提高可解释性.
- 应用HLA和TCNN来分类PVA发病率在11名机械通风患者的回顾性队列中.
主要成果:
- 自验证显示,与TCNN模型 (89.5%,98.3%,83.9%) 相比,HLA的整体性能优越 (精度,灵敏度,特异性:97.5%,96.6%,98.1%).
- 由于其基于规则的性质,TCNN模型在检测PVA方面表现出更高的灵敏度,而HLA则表现出更好地识别非PVA呼吸周期.
- 虽然整体AI检测率相似,但特定PVA类型的患者内分布在HLA和TCNN之间有所不同.
结论:
- HLA和TCNN都在PVA检测方面表现出有效性,这表明实时连续监测的潜力.
- 像TCNN这样的机器学习模型显示出良好的PVA识别,但需要优化的架构和多样化的训练数据才能广泛采用临床.
- 基于规则的方法,如HLA,为PVA检测提供了可靠的方法,为潜在的模式提供了明确的见解,并与透明度和可靠性的临床需求保持一致.
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