智能警报:基于机器学习的重症监护病房患者-呼吸机异步检测系统
Jaroslav Pažout1, Milan Němý2, Jakub Mikeš1
1Department of Anesthesiology and Intensive Care, 3rd Faculty of Medicine, Charles University and Kralovske Vinohrady University Hospital in Prague, Šrobárova 50, Prague 100 34, Czech Republic.
Computer methods and programs in biomedicine
|June 29, 2025
概括
一个新的AI系统,SmartAlert,从呼吸机屏幕数据中准确地检测和分类实时的患者-呼吸机异步 (PVA). 这项技术旨在通过减少警报疲劳和优化呼吸机设置来改善患者护理.
科学领域:
- 临界护理医学 临界护理医学
- 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:
- 患者呼吸器异步 (PVA) 与增加的死亡率和呼吸器诱导的肺损伤有关.
- 目前的PVA检测方法受到静态值,复杂的预处理或专有数据访问的限制.
- 需要实时的,自动化的PVA检测系统.
研究的目的:
- 开发和验证SmartAlert,这是一个在线实时系统,用于检测和分类PVA.
- 为了分类PVA严重程度,及时提醒临床医生.
- 为了利用随时可用的通风器屏幕数据进行PVA分析.
主要方法:
- 开发了使用ICU患者呼吸机屏幕记录的SmartAlert系统.
- 从视频中提取压力和流量波形,将其转换为时间序列数据.
- 采用深度神经网络进行PVA分类和报警级别分配,根据专家共识进行验证.
主要成果:
- 在预测警报级别 (83.8%) 和PVA分类 (89.3%) 中,SmartAlert实现了高准确度.
- 该系统表现出强的性能,加权AUC-ROC值为0.943的警报和0.951的PVA.
- 对于紧急报警 (99.9%) 和特定的PVA类型 (例如,无效触发98.5%) 观察到高特异性.
结论:
- 智能警报 (SmartAlert) 是一个自动化,验证的实时PVA检测,分类和临床医生警报系统.
- 该系统显示了减少报警疲劳和优化通风机管理的潜力.
- 需要进一步的临床试验来确认其对患者结局的影响.
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