Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

19
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
19
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.6K
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...
1.6K
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

16
Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
16
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

13
Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
13
Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

94
The first step in nursing management of a patient with heart failure involves thoroughly assessing the patient's medical history.Subjective Data: Obtain the patient's medical history of coronary artery disease, hypertension, myocardial infarction, and symptoms like dyspnea, orthopnea, and paroxysmal nocturnal dyspnea.Objective Data: Conduct a physical examination to identify findings such as jugular vein distention, pulmonary crackles, tachycardia, murmurs, peripheral edema, and vital signs,...
94
Cardiomyopathy VI: Nursing Management01:29

Cardiomyopathy VI: Nursing Management

13
Assessment: Nursing management of patients with cardiomyopathy begins with a thorough assessment of the patient's history, including a family history of cardiomyopathy or sudden cardiac death, personal history of heart disease, hypertension, diabetes, and any alcohol consumption or drug use.During the physical examination, assess vital signs, look for signs of heart failure (such as edema, jugular venous distention, and cyanosis), auscultate for abnormal heart sounds (like murmurs and gallops),...
13

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

OpthaNet: Attention-Integrated Architecture for High-Precision Multi-Class Ophthalmic Image Classification.

Healthcare technology letters·2026
Same author

Multi-Scale Attention Fusion With Depthwise Separable Convolutions for Efficient Skin Cancer Detection.

Journal of cutaneous pathology·2025
Same author

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Scientific reports·2025
Same author

InsightNet: A Deep Learning Framework for Enhanced Plant Disease Detection and Explainable Insights.

Plant direct·2025
Same author

Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Diagnostics (Basel, Switzerland)·2025
Same author

LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning.

Plant direct·2025

相关实验视频

Updated: Jul 19, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

基于临床数据的心力衰竭预测,使用轻量级的机器学习元模型.

Istiak Mahmud1, Md Mohsin Kabir2, M F Mridha3

  • 1Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh.

Diagnostics (Basel, Switzerland)
|August 12, 2023
PubMed
概括

本研究引入了一种机器学习元模型,使用临床数据预测心力衰竭. 这种新的方法实现了87%的准确性,为早期发现和预防心脏病提供了一个有前途的工具.

关键词:
高斯的天真贝耶斯.心脏衰竭是因为心脏衰竭.决策树是一个决策树.预测 预测 预测 预测k-最近的邻居机器学习是机器学习.这是一个超级模型.随机森林分类器随机森林分类器

更多相关视频

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

6.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

相关实验视频

Last Updated: Jul 19, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
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

6.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 心力衰竭是一种具有多种贡献风险因素的危急状况.
  • 机器学习 (ML) 提供了预测心脏病的潜力,但面临着实施挑战.
  • 准确的预测对于预防危及生命的心脏事件至关重要.

研究的目的:

  • 开发和评估用于预测心力衰竭的机器学习元模型.
  • 用组合的临床数据集来评估拟议的元模型的预测性能.
  • 将元模型的准确性与其他ML模型进行心力衰竭预测的比较.

主要方法:

  • 使用随机森林分类器,高斯天真贝叶斯,决策树和k-最近邻近算法构建了一个机器学习元模型.
  • 该元模型在来自五个不同的心脏病数据集的综合数据集上进行了训练和验证.
  • 在所有数据集中使用了11个标准的临床特征来开发模型.

主要成果:

  • 拟议的机器学习元模型显示,心力衰竭的预测准确度高达87%.
  • 超级模型在预测心力衰竭方面表现优于其他个别机器学习模型.
  • 这项研究强调了组合方法在心血管风险预测中的有效性.

结论:

  • 开发的机器学习元模型为心力衰竭预测提供了准确的方法.
  • 这种方法在早期诊断和患者管理中具有显著的临床实用性潜力.
  • 进一步的研究可以探索整合更多多样化的数据源来增强预测能力.