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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
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Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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一个基于近接政策优化算法的算法用于心血管疾病检测.

Yuejiao Niu1, Xianchuang Fan1, Rong Xue2

  • 1College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China.

Journal of medical engineering & technology
|March 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的人工神经网络 (ANN) 方法,用于检测运动员的心血管疾病 (CVD). 这种先进的模型有效地处理不平衡的数据,提高了运动群体心脏病诊断准确度.

关键词:
心血管疾病的风险.人工蜜蜂殖民地人工神经网络的人工神经网络不平衡的分类不平衡的分类接近政策优化近接政策优化

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科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 运动医学 运动医学

背景情况:

  • 心血管疾病 (CVD) 对运动员构成重大风险.
  • 在运动员中精确检测心血管疾病对于预防不良事件至关重要.
  • 现有的诊断方法可能会面临不平衡数据集的挑战.

研究的目的:

  • 开发和评估一种新的人工神经网络 (ANN) 模型,用于评估运动员心血管疾病风险.
  • 为了应对医疗数据集固有的不平衡分类的挑战.
  • 提高运动人口中心血管疾病检测的可靠性和准确性.

主要方法:

  • 使用基于相互学习的人工蜜蜂群 (ML-ABC) 算法进行初始体重设置.
  • 采用近距离政策优化 (PPO) 来确保稳定和高效的ANN更新.
  • 制定分类作为一个决策过程,奖励少数群体类别的识别来处理不平衡.

主要成果:

  • 拟议的ANN模型在多个数据集中展示了卓越的性能.
  • 实现了高准确度:0.88 (多诊所数据集),0.86 (NCAA数据集) 和0.82 (NHANES数据集).
  • 在运动员心血管疾病检测方面表现优于现有模型.

结论:

  • 新的ANN方法有效地检测运动员的心血管疾病.
  • ML-ABC和PPO的整合提高了模型的可靠性,并处理了类不平衡.
  • 这种方法促进了心血管疾病的检测和体育医学中的临床应用.