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Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.4K
Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
1.4K
Heart Failure Drugs: Inhibitors of Renin-Angiotensin System01:26

Heart Failure Drugs: Inhibitors of Renin-Angiotensin System

360
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...
360
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Heart Failure Drugs: β-Blockers01:22

Heart Failure Drugs: β-Blockers

303
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,...
303

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相关实验视频

Updated: May 23, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

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一个可解释的多目标混合机器学习模型,用于降低心力衰竭死亡率.

F M Javed Mehedi Shamrat1, Majdi Khalid2, Thamir M Qadah3

  • 1Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

PeerJ. Computer science
|March 10, 2025
PubMed
概括
此摘要是机器生成的。

一个新的多目标堆叠启用混合模型 (MO-SEHM) 改善了早期心力衰竭 (HF) 诊断. 这种先进的机器学习方法实现了94.87%的准确性,识别了更好的患者存活率的关键特征.

关键词:
功能选择 功能选择心脏衰竭是因为心脏衰竭.地方可解释的模型-无神论解释.多目的堆叠启用混合模型 (MO-SEHM)在NSGA-II中,NSGA-II是最重要的.

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相关实验视频

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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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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
  • 机器学习 机器学习

背景情况:

  • 心力衰竭 (HF) 是全球主要的死亡原因,需要早期诊断以改善存活率.
  • 当前的机器学习 (ML) 和特征选择方法与新数据和复杂模式作斗争.

研究的目的:

  • 引入先进的多目标堆叠启用混合模型 (MO-SEHM),以提高早期心力衰竭检测.
  • 用特征选择来提高ML模型在诊断心力衰竭中的准确性和稳定性.

主要方法:

  • 开发了一种MO-SEHM,将堆叠启用混合模型 (SEHM) 分类器与非主导排序遗传算法II (NSGA-II) 集成在一起,用于多目标特征选择.
  • 在费萨拉巴德心脏病研究所 (FIOC) 的心力衰竭数据集上评估了六个ML模型,包括带有和没有NSGA-II的SEHM.
  • 利用本地可解释的模型不可知解释 (LIME) 来确保模型的透明度.

主要成果:

  • 与其他模型相比,MO-SEHM表现出卓越的性能,达到94.87%的精度.
  • 该模型确定了九个相关特征,这些特征对于准确的心力衰竭诊断至关重要.
  • 帕雷托前线分析证实了拟议的MO-SEHM的有效性.

结论:

  • 通过优化特征选择和强大的分类,MO-SEHM在早期心力衰竭诊断方面取得了重大进展.
  • 使用LIME的模型的可解释性增强了患者和利益相关者的信任和理解.
  • 这种方法有望改善心血管疾病管理中的患者结果.