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使用基于机器学习的算法来识别和量化临床实践中的运动限制:我们还在那里吗?

Fabian Schwendinger1, Ann-Kathrin Biehler, Monika Nagy-Huber2

  • 1Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, SWITZERLAND.

Medicine and science in sports and exercise
|September 13, 2023
PubMed
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机器学习算法可以从心肺运动测试 (CPET) 中准确地分类运动限制,匹配专家的表现. 这项技术可能有助于临床决策和标准化CPET解释.

科学领域:

  • 心肺血管生理学 心肺血管生理学
  • 医学中的人工智能
  • 临床决策支持 临床决策支持

背景情况:

  • 解释心肺运动测试 (CPET) 需要专门的工作人员.
  • 机器学习 (ML) 提供了自动化CPET解释的潜力.

研究的目的:

  • 评估ML算法在分类运动限制及其严重性的准确性.
  • 在临床环境中将ML算法性能与专家共识进行比较.

主要方法:

  • 对来自40岁以上患者的200个CPET历史数据集的分析.
  • 独立专家使用视觉模拟尺度对限制 (肺血管,机械呼吸,心脏循环,肌肉) 的评级.
  • 决策树和随机森林用于数据分析的应用.

主要成果:

  • 随机森林确定了特定限制的关键参数 (例如,肺血管的通风效率).
  • 决策树的准确性与专家评级相当.
  • 开发了一个结合的决策树来量化多个系统的限制.

结论:

  • ML算法在促进CPET解释和识别运动限制方面表现有前途.

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  • 这些发现支持临床决策和标准化CPET评级工具的开发.