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Related Concept Videos

Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

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 patient.
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

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...
Factors affecting Blood pressure01:28

Factors affecting Blood pressure

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.
Physiological Factors:
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

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
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
Measurement of Blood Pressure01:17

Measurement of Blood Pressure

Assessing blood pressure is a standard procedure executed in virtually all medical environments. The method utilized today was established over a hundred years ago by an innovative Russian doctor, Dr. Nikolai Korotkoff. The soft ticking noise, known as Korotkoff sounds, heard while taking blood pressure readings results from turbulent blood flow within the vessels. The apparatus required for this procedure includes a sphygmomanometer, a blood pressure cuff attached to a gauge, and a stethoscope.

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  3. A Machine Learning-based Framework For Predicting Hypertension Using Serum Hematological Factors.
  1. Home
  3. A Machine Learning-based Framework For Predicting Hypertension Using Serum Hematological Factors.

Related Experiment Videos

A machine learning-based framework for predicting hypertension using serum hematological factors.

Mina Moradi1, Vahid Mahdavizadeh2, Aida Yavari Kondori3

  • 1Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran.

Scientific Reports
|June 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models can predict hypertension (HTN) risk using common blood tests and clinical data. Age, BMI, and red blood cell counts were key indicators for developing this hypertension prediction framework.

Keywords:
Glomerular filtration rate (GFR)Hematologic factorsHypertensionMachine learning

Related Experiment Videos

Area of Science:

  • Cardiovascular Medicine
  • Biomedical Informatics
  • Machine Learning in Healthcare

Background:

  • Hypertension (HTN) is a primary global risk factor for cardiovascular disease (CVD) and mortality.
  • Early detection and intervention are crucial for managing HTN and its complications.
  • Routinely collected clinical and hematologic data offer potential for developing predictive models.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML)-based predictive framework for hypertension risk.
  • To identify key hematologic and clinical parameters that predict incident hypertension.
  • To establish an interpretable ML model for hypertension risk assessment.

Main Methods:

  • Analysis of data from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study (n=4,923).
  • Inclusion of hematologic biomarkers (e.g., WBC, RBC, MCV, PLT) and clinical/demographic variables (e.g., age, BMI, GFR).
  • Application and comparison of multiple ML algorithms, including XGBoost, LightGBM, and logistic regression, with median imputation for missing values.

    • Main Results:

    • XGBoost demonstrated the best performance with a ROC-AUC of 0.66 (95% CI: 0.63-0.69).
    • Key predictors identified were age, body mass index (BMI), red blood cell (RBC) count, and mean corpuscular volume (MCV).
    • Models showed stable performance across training and test sets, indicating minimal overfitting.

    Conclusions:

    • An ML-driven framework effectively utilizes routine clinical and hematologic data for exploring hypertension risk.
    • The study identified significant predictors, including modifiable factors like BMI and physical activity.
    • This work provides a benchmark for future research on hypertension prediction using comprehensive datasets.