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相关概念视频

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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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.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用基于大规模人口数据集的机器学习模型预测无氧值的心率.

Atsuko Nakayama1,2, Tomoharu Iwata3,4, Hiroki Sakuma3,4

  • 1Department of Cardiovascular Medicine, Sakakibara Heart Institute, Tokyo 183-0003, Japan.

Journal of clinical medicine
|January 11, 2025
PubMed
概括

一个新的机器学习模型使用非运动临床数据准确预测无氧值 (AT-HR) 的目标心率. 这种方法为心血管疾病患者的运动处方提供了比传统配方更精确的方法.

关键词:
心脏康复心脏康复功能选择 功能选择梯度增强可以提高梯度.机器学习是机器学习.目标运动心率心率.

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科学领域:

  • 心脏病学 心脏病学
  • 运动生理学 运动生理学
  • 机器学习在医学中的应用

背景情况:

  • 在厌氧值 (AT-HR) 确定目标心率对于心血管疾病 (CVD) 患者有效的运动处方至关重要.
  • 心肺运动测试 (CPET) 是AT-HR确定标准方法,但可能是资源密集的.
  • 需要开发替代方法来根据现有的临床特征预测AT-HR.

研究的目的:

  • 开发和验证一种机器学习 (ML) 模型,仅使用非运动临床特征来预测AT-HR.
  • 将ML模型的准确性与AT-HR预测的既定指南推方程进行比较.
  • 确定对AT-HR预测有显著贡献的关键临床特征.

主要方法:

  • 利用来自21482例CPET病例的8228名参与者 (健康人和心血管疾病患者) 的数据集.
  • 采用渐变增强ML模型,对78个临床特征 (例如人口统计学,生命体征,血液检测,心声学) 进行培训.
  • 使用平均绝对误差 (MAE) 评估预测准确性,并将ML模型结果与Karvonen和更简单的公式进行比较.

主要成果:

  • 与指导方程相比,ML模型实现了显著较低的MAE (7.7 ± 0.2 bpm) (例如,Karvonen: 34.5 ± 0.3 bpm,11.9 ± 0.2 bpm;更简单的公式:15.9 ± 0.3 bpm,9.7 ± 0.2 bpm).
  • 对于AT-HR的关键预测因素包括静止心率,年龄,N-终端亲大脑尿性 (NT-proBNP),静止静脉血压,hsCRP,心血管疾病诊断和β-阻断剂的使用.
  • 使用前10-20个特征保持了高预测准确度,这表明了特征效率.

结论:

  • 从非运动临床特征中成功开发了一种基于ML的AT-HR准确的预测模型.
  • 这个模型有可能简化和增强心脏康复的运动处方.
  • 该研究确定了AT-HR的新型决定因素,如NT-proBNP和hsCRP,为心血管疾病病理生理学提供了新的见解.