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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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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...
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Pathophysiology of Heart Failure01:17

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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...
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Dementia01:30

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

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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,...
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Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

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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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Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

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Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
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Dementia and Heart Failure Classification Using Optimized Weighted Objective Distance and Blood Biomarker-Based

Veerasak Noonpan1, Supansa Chaising2, Georgi Hristov3

  • 1Computer and Communication Engineering for Capacity Building Research Center, School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand.

Bioengineering (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

A new measurement, the optimized weighted objective distance (OWOD), effectively distinguishes dementia from heart failure. This novel approach improves diagnostic accuracy and reduces misclassifications, aiding clinical decision-making.

Keywords:
blood biomarkersdementiaheart failureobjective distancerisk factorsweighting features

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Dementia and heart failure are significant global health challenges, particularly with aging populations.
  • Accurate diagnosis is hindered by limited access to advanced diagnostic equipment and tests.
  • Distinguishing between dementia and heart failure is crucial to prevent misdiagnosis and inappropriate patient referrals.

Purpose of the Study:

  • To introduce a novel measurement, the optimized weighted objective distance (OWOD), for improved classification of dementia and heart failure.
  • To enhance model generalization and classification performance using a data-driven approach.
  • To integrate novel blood biomarker features with existing risk factors for robust diagnostic capabilities.

Main Methods:

  • Developed the optimized weighted objective distance (OWOD), a modified weighted objective distance metric.
  • Applied multi-feature distance normalization and identified key classification features.
  • Integrated 20 features, including risk factors and proposed blood biomarkers, from 10,000 electronic health records.
  • Utilized machine learning models for classification and performance evaluation.

Main Results:

  • The OWOD-based classification method achieved high performance metrics: 95.45% accuracy, 96.14% precision, 94.70% recall, and 95.42% F1-score.
  • The area under the ROC curve reached 97.10%, indicating strong discriminative power.
  • The OWOD method outperformed other machine learning models, including gradient boosting, decision tree, neural network, and support vector machine.

Conclusions:

  • The optimized weighted objective distance (OWOD) offers a powerful and accurate tool for differentiating between dementia and heart failure.
  • The integration of blood biomarkers significantly enhances classification performance.
  • This novel approach holds promise for improving diagnostic accuracy in clinical settings, especially where resources are limited.