Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Hemodialysis III: Nursing Management01:25

Hemodialysis III: Nursing Management

416
The nursing management of a patient undergoing hemodialysis includes several critical steps, starting with a thorough assessment before the procedure.Before the Hemodialysis ProcedureFirst, record the patient's vital signs—blood pressure, heart rate, respiratory rate, and temperature—to establish a baseline. This baseline is essential for detecting conditions such as hypotension that could impact the patient's response to dialysis. Document the patient's pre-dialysis weight, as this...
416
Hemodialysis II: Procedure and Complications01:24

Hemodialysis II: Procedure and Complications

293
DialyzersA hemodialysis (HD) dialyzer is a plastic cartridge containing thousands of parallel hollow fibers, which serve as semipermeable membranes. These fibers are typically made from cellulose-based or other synthetic materials. During HD, blood is pumped into the top of the cartridge and distributed among these fibers. Simultaneously, dialysis fluid, known as dialysate, is introduced into the bottom of the cartridge, bathing the outside of the fibers. Across the semipermeable membrane,...
293
Hemodialysis I: Introduction01:25

Hemodialysis I: Introduction

674
Hemodialysis (HD) is a medical treatment that artificially removes waste products, excess fluids, and toxins from the blood when the kidneys are no longer able to perform these functions effectively. In this process, blood is filtered through a semipermeable membrane, allowing for the selective removal of waste while preserving necessary components like blood cells and proteins. Hemodialysis is typically performed in patients with end-stage renal disease (ESRD) or severe kidney...
674
Dialysis01:27

Dialysis

859
Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
859
Heart Failure Drugs: Diuretics01:22

Heart Failure Drugs: Diuretics

608
Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
608
Alterations in Blood Pressure01:30

Alterations in Blood Pressure

1.6K
Alterations in blood pressure, such as hypertension (high blood pressure) and hypotension (low blood pressure), significantly affect human health. Understanding these conditions' classifications, causes, and symptoms is essential for effective management and treatment.
Hypertension (High blood pressure)
Hypertension occurs when blood pressure readings consistently exceed the normal range. It is diagnosed when systolic blood pressure (the top number, indicating pressure while the heart...
1.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI-Assisted Diagnosis of <i>Trichomonas vaginalis</i> from Routine Gram-Stained Vaginal Smears.

Diagnostics (Basel, Switzerland)·2026
Same author

Influence of Weather and Air Quality on Clinical Outcomes in End-Stage Kidney Disease: A Retrospective Hospital-Based Study.

Environmental health insights·2026
Same author

Immersive Virtual Reality Exercise: Effects on Cortisol, Quality of Life, Cognitive Function, and Psychological Symptoms in Fibromyalgia.

Medicina (Kaunas, Lithuania)·2026
Same author

Evaluating Bio-Inspired Metaheuristics for Dynamic Surgical Scheduling: A Resilient Three-Stage Flow Shop Model Under Stochastic Emergency Arrivals.

Biomimetics (Basel, Switzerland)·2026
Same author

Predictive Models for Early Infection Detection in Nursing Home Residents: Evaluation of Imputation Techniques and Complementary Data Sources.

Healthcare (Basel, Switzerland)·2026
Same author

ILK Deletion Protects Against Chronic Kidney Disease-Associated Vascular Damage.

International journal of molecular sciences·2026

Related Experiment Video

Updated: Nov 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K

Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers.

Juan A Gómez-Pulido1, José M Gómez-Pulido2, Diego Rodríguez-Puyol3

  • 1Department of Technologies of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain.

International Journal of Environmental Research and Public Health
|March 6, 2021
PubMed
Summary

This study predicts hypotension during dialysis using clinical data. Machine learning models achieved over 80% accuracy, aiding in preventing this serious complication for chronic renal disease patients.

Keywords:
clinical monitoringdecision treeshemodialysishypotensionsupervised learningsupport vector machines

More Related Videos

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats
06:38

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

12.3K
Measurement of Tissue Oxygenation Using Near-Infrared Spectroscopy in Patients Undergoing Hemodialysis
04:36

Measurement of Tissue Oxygenation Using Near-Infrared Spectroscopy in Patients Undergoing Hemodialysis

Published on: October 2, 2020

2.4K

Related Experiment Videos

Last Updated: Nov 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats
06:38

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

12.3K
Measurement of Tissue Oxygenation Using Near-Infrared Spectroscopy in Patients Undergoing Hemodialysis
04:36

Measurement of Tissue Oxygenation Using Near-Infrared Spectroscopy in Patients Undergoing Hemodialysis

Published on: October 2, 2020

2.4K

Area of Science:

  • Nephrology and Artificial Intelligence
  • Clinical Data Analysis
  • Predictive Modeling in Healthcare

Background:

  • Hypotension during dialysis is a significant risk factor for mortality in patients with advanced chronic renal disease.
  • Clinical parameters monitored during dialysis are crucial for predicting hypotension, especially when analytical data is unavailable or delayed.

Purpose of the Study:

  • To develop and validate a predictive model for hypotension during dialysis sessions.
  • To leverage machine learning on a large-scale dialysis database for improved patient safety.

Main Methods:

  • Utilized a database of 98,015 dialysis sessions from 758 patients.
  • Employed machine learning classifiers trained on 22 clinical parameters, patient age, and gender, measured multiple times during sessions.

Main Results:

  • The predictive model demonstrated a success rate higher than 80% in forecasting hypotension.
  • Identified key clinical parameters for effective hypotension prediction.

Conclusions:

  • Machine learning models can accurately predict dialysis-induced hypotension using readily available clinical data.
  • This approach offers a valuable tool for proactive hypotension management and improved patient outcomes in chronic renal disease care.