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相关概念视频

Heart Failure Drugs: β-Blockers01:22

Heart Failure Drugs: β-Blockers

259
β-adrenergic antagonists, commonly known as β-blockers, block the effects of sympathetic neurotransmitters such as noradrenaline (NA) and adrenaline (ADR). They have several beneficial effects in heart failure treatment. They reduce heart rate, the force of contraction, and cardiac muscle relaxation. They also slow the atrial-ventricular conduction rate and raise the threshold for arrhythmias. The concentration of β-blockers determines their effects on bronchodilation,...
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Heart Failure Drugs: Inhibitors of Renin-Angiotensin System01:26

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The activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system (RAAS) contributes to cardiac remodeling, and inhibiting the RAAS is a pharmacological target in heart failure management. As a result, neurohumoral modulation is a crucial treatment principle for managing heart failure. This approach involves using medications like ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, mineralocorticoid receptor antagonists (MRAs), and neutral...
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Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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使用自适应性博托克斯优化基于深度长期反复卷积网络的增强心脏病风险预测.

R Vijay Sai1, B G Geetha1

  • 1Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tiruchengode, TN, Inida.

Technology and health care : official journal of the European Society for Engineering and Medicine
|April 30, 2025
PubMed
概括

本研究介绍了一种使用深度学习的基于物联网的先进心脏病预测方法. 这种新的方法显著提高了分类准确性,有助于远程监测高血压等疾病的医疗保健.

关键词:
心脏病预测 心脏病预测基于物联网的疾病预测预测.数据预处理数据预处理.深度学习是一种深度学习.功能选择 功能选择

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科学领域:

  • 心血管健康 心血管健康
  • 人工智能在医学中的应用
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

  • 心脏病仍然是全球主要的死亡原因.
  • 准确的预测是具有挑战性的,需要先进的技术.
  • 物联网传感器数据为改善疾病预测提供了潜力.

研究的目的:

  • 利用物联网传感器数据开发用于心脏病分类的综合方法.
  • 通过先进的数据预处理,功能选择和深度学习来提高预测准确性.
  • 为远程医疗监测提供可靠的工具,特别是对于高血压等疾病.

主要方法:

  • 使用基于集群的数据计算和规范化 (CDIN) 和基于距离的异常值检测 (RMDBOD) 的强大的Mahalanobis数据预处理.
  • 通过改进的二进制基于量子的航空导航优化 (IBQANO) 算法进行功能选择.
  • 使用深度长期反复卷积网络 (DLRCN) 进行分类,并通过自适应性玻尿酸优化算法 (ABOA) 进行优化.

主要成果:

  • 拟议的方法论在匈牙利,UCI和克利夫兰心脏病数据集上表现出卓越的性能.
  • 实现了高准确率:克利夫兰数据集上的99.72%,UCI数据集上的99.41%.
  • 与现有的心脏病预测方法相比,观察到显著的改善.

结论:

  • 开发的方法代表了远程医疗保健和心血管疾病预测的重大进步.
  • 为管理高血压等疾病提供可靠和准确的解决方案,特别是在老年人中.
  • 强调了整合物联网,人工智能和深度学习在主动健康管理中的潜力.