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基于机器学习的Web系统的开发,用于使用多频生物电阻分析估计膜溢出.

Daisuke Nose1,2,3, Tomokazu Matsui4, Takuya Otsuka5

  • 1Department of Cardiology, Fukuoka University Faculty of Medicine, Fukuoka 814-0180, Japan.

Journal of cardiovascular development and disease
|July 28, 2023
PubMed
概括

机器学习和胸阻抗准确地估计了心力衰竭患者的胸腔溢出. 与传统技术相比,这种非侵入性方法可以改善胸内病情的评估.

关键词:
设备 设备 设备 设备估计系统估计系统心脏衰竭是因为心脏衰竭.阻抗阻抗是指阻抗的阻抗.机器学习是机器学习.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗信息学 医疗信息学
  • 心脏病学 心脏病学

背景情况:

  • 由于准确性和复杂性问题,跨胸膜阻抗 (TTZ) 对血管外肺水含量未得到充分利用.
  • 开发非侵入性方法来估计心力衰竭患者的胸内病情至关重要.

研究的目的:

  • 开发一种新系统的基础模型,以非侵入性地估计心力衰竭患者的胸内疾病.
  • 通过多频生物电阻分析来评估机器学习的有效性.

主要方法:

  • 使用多频生物电阻分析来收集电气,物理和血液学数据.
  • 开发了一个机器学习模型 (梯度提升,决策树),使用286.6中的16个特征.
  • 从63名心力衰竭患者和82名健康志愿者在入院和治疗后收集了数据.

主要成果:

  • 开发的模型在区分肺溢液 (AUC = 0.905) 中取得了高准确性.
  • 这显著超过了传统的基于频率的方法 (AUC = 0.740).
  • 该模型有效地利用了关键的电气测量和临床发现.

结论:

  • 机器学习与横胸阻抗相结合,显示出预期用于估计多流量.
  • 这种非侵入性方法提供了一种有效的方法,可以使用临床和实验室数据来评估胸内疾病.