人工智能驱动的呼吸机制预测:使用长期短期记忆和连续传感器数据预测组织过敏性.
Ghada Ben Othman1, Amani R Ynineb1, Erhan Yumuk1,2
1Department of Electromechanics, System and Metal Engineering, Ghent University, Tech Lane Science Park 125, 9052 Ghent, Belgium.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
人工智能 (AI) 模型现在可以通过肺功能测试和可穿戴传感器的综合数据来估计和预测组织歇斯底里性,这是呼吸系统疾病的关键标志物. 这种方法大大减少了所需的测量,最大限度地减少了患者的努力和成本.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 组织hysteresivity是呼吸道疾病进展的关键指标,通常通过强制振荡技术测量.
- 目前的测量协议可能会很长时间,对患者来说很苛刻.
- 利用多变量数据和人工智能进行呼吸监测是一个新兴的领域,具有尚未开发的潜力.
研究的目的:
- 为了减少组织歇斯底里性测量的数量和持续时间.
- 探索人工智能的使用,特别是长短期记忆 (LSTM) 网络,用于估计和预测组织歇斯底里性.
- 整合强制振荡技术 (FOT) 设备和等效 (EQV) 生命监测器的数据,以进行增强的呼吸分析.
主要方法:
- 在两个小时的协议中收集了EQV LifeMonitor,FOT原型和RESMON设备的同步数据.
- 使用LSTM模型估计hysteresivity (η) 使用连续心率 (HR) 数据.
- 利用LSTM预测η,首先从心电图 (ECG) 数据预测HR.
主要成果:
- LSTM准确估计了组织hysteresivity (η),其中R2=0.851和MSE=0.296.
- LSTM成功预测了 η,其中 R2 = 0.883 和 MSE = 0.528.
- 所需测量次数减少了三倍,从10次减少到3次.
结论:
- 通过人工智能驱动的FOT和可穿戴传感器数据的整合提供了一个有前途的方法,用于准确地估计和预测组织hysteresivity.
- 这种新的方法显著降低了患者的负担,测量时间和相关成本.
- 这项研究强调了人工智能在推进门诊呼吸监测方面的潜力.
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