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Related Concept Videos

Hypertension III: Clinical Manifestations and Diagnostic Studies01:30

Hypertension III: Clinical Manifestations and Diagnostic Studies

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Hypertension is asymptomatic and also referred to as the "silent killer" until it progresses to a severe stage or causes target organ disease. Patients may experience symptoms stemming from the strain on blood vessels and tissues in various organs or the heart's increased workload.Physical exams might show no abnormalities other than high blood pressure. Signs of vascular damage, when present, correspond to the organs supplied by the affected vessels, leading to target organ damage. For...
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Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

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Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
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Hypertension and Regulation of Blood Pressure01:18

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Hypertension, the most common cardiovascular disease, is diagnosed through repeated measurements of elevated blood pressure. Its risks, including damage to the kidney, heart, and brain, are directly proportional to blood pressure levels. Starting from 115/75 mm Hg, the risk of cardiovascular disease doubles with each increment of 20/10 mm Hg. The diagnosis relies on blood pressure measurements, not on patient symptoms, as hypertension is often asymptomatic until end-organ damage is imminent or...
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Neural Regulation of Blood Pressure01:18

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The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Hypertension II: Pathophysiology01:29

Hypertension II: Pathophysiology

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Hypertension is a chronic condition in which the blood's force against artery walls is excessively high, posing risks such as heart disease. The condition's underlying mechanisms involve complex interactions among the cardiovascular, kidney, and autonomic nervous systems.Renin-Angiotensin-Aldosterone System (RAAS): This system significantly influences blood pressure regulation. When blood pressure decreases, the kidneys secrete renin. This enzyme transforms angiotensinogen, a plasma protein,...
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Related Experiment Video

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Machine Learning for Hypertension Prediction: a Systematic Review.

Gabriel F S Silva1, Thales P Fagundes2, Bruno C Teixeira2

  • 1Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, SP, Brazil.

Current Hypertension Reports
|June 22, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms show promise for predicting hypertension, achieving high accuracy (AUROC 0.766-1.00). Further evaluation of technical factors is needed for clinical application.

Keywords:
Evaluation metricsHypertensionMachine learningModel constructionSystematic review

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Area of Science:

  • Cardiovascular Disease Research
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Hypertension remains a major global health concern.
  • Predictive models are crucial for early intervention and public health strategies.

Purpose of the Study:

  • To systematically review literature on machine learning (ML) for hypertension prediction.
  • To identify key methodologies and performance metrics in recent studies.

Main Methods:

  • Systematic literature review of articles published January 2018 - May 2021.
  • Screening and selection using the ASReview machine learning algorithm.
  • Comparative analysis of 21 selected studies based on ML model parameters and outcomes.

Main Results:

  • Machine learning models demonstrated strong predictive performance, with Area Under the ROC Curve (AUROC) ranging from 0.766 to 1.00.
  • Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest were frequently identified as high-performing algorithms.
  • Studies highlight the potential of ML for preventive clinical decisions and public health policies.

Conclusions:

  • Machine learning offers a promising avenue for hypertension prediction and management.
  • Consistent evaluation of technical aspects like outcome definition, code availability, performance, and data leakage is essential.
  • Further research should focus on improving explainability and mitigating data leakage in ML models for hypertension.