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Related Concept Videos

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.
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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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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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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...
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Several physiological and lifestyle factors influence blood pressure (BP). Understanding these factors is crucial as they are significant in patient education and blood pressure management.
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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.
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Recent developments in machine learning modeling methods for hypertension treatment.

Hirohiko Kohjitani1, Hiroshi Koshimizu2, Kazuki Nakamura2

  • 1Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan. kohjitani.hirohiko.7z@kyoto-u.ac.jp.

Hypertension Research : Official Journal of the Japanese Society of Hypertension
|January 12, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models are advancing hypertension treatment by analyzing time-series blood pressure data. These interpretable AI tools predict blood pressure variability and offer personalized interventions for better cardiovascular outcomes.

Keywords:
Artificial intelligenceBig time-series dataDeep neural networkExplainable AIHypertension

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Area of Science:

  • Cardiology
  • Medical Artificial Intelligence
  • Health Informatics

Background:

  • Hypertension is a primary driver of cardiovascular disease.
  • Digitalization of blood pressure monitoring generates extensive time-series data.
  • Blood pressure variability is gaining clinical significance with time-series data analysis.

Purpose of the Study:

  • To review advancements in artificial intelligence (AI) for individualized hypertension treatment.
  • To emphasize the use of time-series blood pressure data and interpretable AI models.
  • To explore AI's potential in predicting blood pressure variability and guiding interventions.

Main Methods:

  • Review of current AI models for hypertension management.
  • Focus on time-series analysis of big blood pressure data.
  • Exploration of explainable AI techniques for clinical interpretation.

Main Results:

  • Time-series blood pressure prediction models can forecast variability up to four weeks ahead.
  • Explainable AI techniques offer pathways for individualized hypertension intervention plans.
  • AI models show promise in improving hypertension outcomes through personalized strategies.

Conclusions:

  • AI, particularly using time-series data, offers significant potential for personalized hypertension management.
  • Further research is needed for prospective validation and integration of AI systems.
  • Interdisciplinary collaboration is vital for optimizing and implementing AI-driven hypertension solutions.