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相关概念视频

Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

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Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
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Heart Failure II: Pathophysiology01:29

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Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
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相关实验视频

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机器学习和基于物理的建模用于心脏缩.

Bogdan Milićević1,2, Miljan Milošević2,3,4, Vladimir Simić2,3

  • 1Faculty of Engineering, University of Kragujevac, Kragujevac 34000, Serbia.

Heliyon
|June 14, 2023
PubMed
概括

机器学习和有限元模型预测了6年内心脏缩的进展,提供了宝贵的临床见解. 机器学习模型更快,更适合临床实践.

关键词:
心脏过度缩小的心脏过度缩小疾病进展跟踪 追踪疾病进展有限元素分析的研究.在左心室模式下.机器学习是机器学习.

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科学领域:

  • 心脏病学 心脏病学
  • 生物医学工程 生物医学工程
  • 计算生物学 计算生物学

背景情况:

  • 预测患者的左心室重塑和扩张在临床上是重要的,但具有挑战性.
  • 心脏缩,左心室扩大的一个条件,需要准确的长期监测.

研究的目的:

  • 开发和比较基于机器学习 (ML) 和基于物理 (有限元) 的模型来预测心脏缩.
  • 评估ML和有限元模型在预测疾病演变中的准确性和速度.

主要方法:

  • 训练有素的ML模型 (随机森林,梯度增强,神经网络) 使用患者病史和心脏健康数据.
  • 开发了一种基于物理的有限元模型,模拟心脏缩的发展.
  • 收集多个患者的数据,用于模型培训和验证.

主要成果:

  • 无论是ML还是有限元素模型都准确地预测了六年来过度缩的演变,显示了类似的结果.
  • 有限元模型的准确性更高,因为它基于物理定律.
  • 机器学习模型表现出明显更快的计算时间.

结论:

  • ML模型为监测心脏缩提供了更快,潜在的临床适用方法.
  • 有限元模型提供了更高的准确性,但在计算上是密集的.
  • 将有限元模拟数据集成到ML模型中可以提高临床使用的速度和准确性.