使用投票回归器进行风扇压力预测,并使用患者呼吸时间的时间序列数据
Ali Raza1, Furqan Rustam2, Hafeez Ur Rehman Siddiqui1
1Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
Health informatics journal
|February 23, 2025
概括
一个新的混合通风器压力预测器 (H-VPP) 准确预测机械通风器的气道压力. 这种重要工具有助于预防肺损伤,并确保为危急患者提供足够的氧气.
科学领域:
- 生物医学工程 生物医学工程
- 关键护理医学 关键护理医学
- 呼吸系统生理学 呼吸系统生理学
背景情况:
- 机械呼吸机对于挽救生命至关重要,特别是对于COVID-19患者.
- 错误的呼吸机压力会导致肺部损伤或氧气不足,增加死亡率.
- 精确预测通风机呼吸回路中的气道压力至关重要.
研究的目的:
- 开发和评估一种新的混合式通风机压力预测器 (H-VPP).
- 在患者呼吸期间,准确预测通风器呼吸回路内的气道压力.
- 确定影响通风器压力的关键因素,例如肺部属性R和C.
主要方法:
- 提出了一种新的混合式通风机压力预测器 (H-VPP) 方法.
- 对通风器参数进行了探索性数据分析.
- 确定了高肺抵抗 (R) 和合规 (C) 值作为高压的关键预测指标.
主要成果:
- 在H-VPP实现了0.78.8的确定系数 (R2) 的0.78.
- 该模型显示平均绝对误差 (MAE) 为0.028.8.
- 平均平方误差 (MSE) 为0.003,超过了其他ML/DL模型.
结论:
- 拟议的H-VPP方法在通风机压力预测方面表现出卓越的性能.
- 实现了高精度,超越了现有的机器学习和深度学习模型.
- 对比分析证实了H-VPP的最先进的性能.
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