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相关概念视频

Factors Influencing Heart Rate01:30

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
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Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise
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使用可穿戴数据预测术前心肺呼吸能力的可解释框架.

Iqram Hussain1, Julianna Zeepvat1, M Cary Reid2

  • 1Department of Anesthesiology, Weill Cornell Medicine, New York, NY 10065, United States.

Computer methods and programs in biomedicine
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概括

可穿戴设备可以使用活动和心率数据准确预测老年人的心肺呼吸能力 (CRF). 这使得更好的手术风险评估和个性化预康复能够改善患者的治疗结果.

关键词:
心脏呼吸系统的健康状况可以解释性 解释性机器学习就是机器学习.个性化医疗是个性化的医疗.进行手术前评估.可穿戴设备可以穿戴.

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科学领域:

  • 评估心肺呼吸系统的健康状况
  • 医疗保健中的可穿戴技术
  • 机器学习在医学中的应用

背景情况:

  • 手术前的心肺呼吸能力 (CRF) 对于手术风险分层至关重要.
  • 正式的CRF测试 (CPET,6MWT) 往往是不切实际的例行手术前查.
  • 手腕穿戴可穿戴设备为可访问的CRF估计提供了一个潜在的解决方案.

研究的目的:

  • 开发一种可临床解释的机器学习 (ML) 模型,使用可穿戴数据来预测老年人CRF.
  • 评估模型能够估计6分钟步行测试 (6MWT) 距离,用于术前风险评估.

主要方法:

  • 在65名接受重大非心脏手术的老年人中,在一周内从Fitbit设备收集了心率和活动数据.
  • 采用了ML集合回归模型来预测CRF,使用6MWT结果作为索引.
  • 利用Shapley特征归属来理解可穿戴数据对CRF预测的贡献.

主要成果:

  • 较高的CRF与增加的中度至强度体力活动 (MVPA),最大活动能量消耗 (aEEmax),心率恢复 (HRR) 和非线性心率变化 (HRV) 相相关.
  • 随机森林和线性回归模型显示CRF具有很强的预测能力 (R2=0.91和R2=0.81).
  • 沙普利的分析证实了MVPA,aEEmax,HRR和HRV动态作为增强CRF的关键指标.

结论:

  • 可穿戴设备衍生活动和心率指标可以提供手术前的CRF评估.
  • 这种方法支持手术风险分层和个性化预康复策略.
  • 整合可穿戴设备可以通过更好的手术前评估来改善患者的治疗结果.