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Related Concept Videos

Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

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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...
208
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

310
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...
310
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

2.8K
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...
2.8K
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

699
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...
699
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

673
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...
673
Heart Failure Drugs: β-Blockers01:22

Heart Failure Drugs: β-Blockers

755
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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Related Experiment Video

Updated: Jan 12, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Published on: July 22, 2025

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Machine learning enhanced acute heart failure phenotype prediction using natural language processing and random

Pei-Hsuan Chang1, Feng-Ching Liao1,2, Yi-Ching Wu1

  • 1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Frontiers in Artificial Intelligence
|November 3, 2025
PubMed
Summary
This summary is machine-generated.

This study predicts acute heart failure (AHF) phenotypes using machine learning and clinical text. The integrated model accurately classifies AHF, improving personalized treatment for heart failure patients.

Keywords:
heart failureheart failure phenotypesmachine learning modelnatural language processingrandom forest

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Area of Science:

  • Clinical informatics
  • Artificial intelligence in medicine
  • Cardiology

Background:

  • Heart failure (HF) presents diverse phenotypes, posing public health challenges.
  • Early diagnosis of specific HF phenotypes is critical for effective treatment.

Purpose of the Study:

  • To predict acute heart failure (AHF) phenotypes (HFrEF, HFmrEF, HFpEF) using random forests.
  • To evaluate integrated natural language processing (NLP) and machine learning (ML) models for AHF phenotype classification.

Main Methods:

  • Utilized structured and unstructured data from the MIMIC-III database, including clinical text and laboratory results.
  • Employed LASSO for feature selection and one-hot encoding for textual data.
  • Assessed model performance using AUROC and AUPRC, blinded to LVEF information.

Main Results:

  • The combined model achieved an accuracy of 0.70 ± 0.03 and AUROC of 0.76 ± 0.02 on the training dataset, validated independently.
  • Optimal performance was observed with up to 100 combined features, with minimal performance degradation down to 10 features.

Conclusions:

  • Integrated NLP and ML models enhance AHF phenotype classification.
  • Multifaceted data analysis supports personalized heart failure treatment strategies.
  • Timely, phenotype-specific management can be informed by early AHF identification.