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

701
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...
701
Dialysis01:27

Dialysis

1.1K
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...
1.1K
Dialysis01:15

Dialysis

1.6K
Dialysis is a diffusion-based purification process that separates analyte molecules from a complex matrix. This is accomplished by allowing molecules in the solution to pass through a semipermeable membrane into a liquid on the other side. The membrane is usually made of cellulose acetate or cellulose nitrate, and the second liquid must be miscible with the solution. Ions (e.g., chloride or sodium) or organic molecules (e.g., glucose) can pass through the membrane pores, which generally have...
1.6K
Hemodialysis I: Introduction01:25

Hemodialysis I: Introduction

1.2K
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...
1.2K
Hemodialysis II: Procedure and Complications01:24

Hemodialysis II: Procedure and Complications

583
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,...
583
Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

326
Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
326

You might also read

Related Articles

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

Sort by
Same author

The Chimeric Thoracodorsal Artery Perforator Flap for Reconstruction After Buccal Cancer Resection: A Retrospective Case Series.

Head & neck·2026
Same author

Three-year monitoring study of heavy metal fluxes and accumulation characteristics in mildly contaminated farmland soils of northern Guangdong, China.

Scientific reports·2026
Same author

Efficacy and safety of chaishituire granules in influenza treatment: a multi-center, randomized, double-blind, parallel-controlled clinical trial.

American journal of translational research·2026
Same author

<i>Pueraria mirifica</i>: a critical review of the ethnobotany, phytochemistry, pharmacology and challenges in standardization and safety.

Frontiers in pharmacology·2026
Same author

Right kidney contusion following endoscopic mucosal resection of hepatic flexure lymphangioma: a rare case report.

Frontiers in surgery·2026
Same author

Enabling gradient biomineralization and prestress with a macroscopic matrix: the fish swim bladder for bioprocessing-inspired fabrication.

Materials horizons·2026
Same journal

Peritoneal Glucose Uptake Reduction by Sodium-Glucose Co-Transporter 2 Inhibitors in Clinical Peritoneal Dialysis.

Blood purification·2026
Same journal

With the Generalization of AI and Algorithms, Will We Still Need a Nephrologist in the Dialysis Room?

Blood purification·2026
Same journal

Could serum CA125 aid the clinical assessment of volume status of end-stage kidney disease patients treated by peritoneal dialysis patients?

Blood purification·2026
Same journal

From Bench to Bedside: Implications and Interventions for Endotoxin Exposure in Lipopolysaccharide-Induced Apoptosis and Eryptosis.

Blood purification·2026
Same journal

Feasibility Study of a Smartphone App and Web Portal for Remote Monitoring of Chronic Kidney Disease and Peritoneal Dialysis populations: A mixed-method study.

Blood purification·2026
Same journal

Treatment of Focal Segmental Glomerulosclerosis in Low- and Middle-Income Countries: Emerging Extracorporeal Therapies.

Blood purification·2026
See all related articles

Related Experiment Video

Updated: Jan 10, 2026

A Murine Model of Hemodialysis Access-Related Hand Dysfunction
08:39

A Murine Model of Hemodialysis Access-Related Hand Dysfunction

Published on: May 31, 2022

2.0K

An Online Machine Learning Algorithm-Based Prognostic Predictive Model for Maintenance Hemodialysis Patients.

Guohai Huang1, Yue Huang2, Shaoying Xu1

  • 1Blood Purification Center, Shantou Central Hospital, Shantou, China.

Blood Purification
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine learning model to predict mortality risk in maintenance hemodialysis patients. This user-friendly tool identifies high-risk individuals, improving clinical practice and patient outcomes.

Keywords:
Machine learningMaintenance hemodialysisOnline applicationPredictive model

More Related Videos

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
07:13

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

4.9K
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

8.6K

Related Experiment Videos

Last Updated: Jan 10, 2026

A Murine Model of Hemodialysis Access-Related Hand Dysfunction
08:39

A Murine Model of Hemodialysis Access-Related Hand Dysfunction

Published on: May 31, 2022

2.0K
Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
07:13

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

4.9K
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

8.6K

Area of Science:

  • Nephrology
  • Medical Informatics
  • Machine Learning

Background:

  • Maintenance hemodialysis (MHD) patients face high mortality rates.
  • Current predictive models lack accuracy and clinical usability.
  • Precise tools are needed to identify at-risk MHD patients.

Purpose of the Study:

  • To construct a precise and user-friendly machine learning (ML)-based mortality risk predictive model for MHD patients.
  • To enhance clinical decision-making for MHD patient care.

Main Methods:

  • 601 MHD patients' clinical and laboratory data were analyzed.
  • Six ML algorithms were used to build predictive models.
  • Models were validated using AUROC and AUPRC metrics; an online application was developed.

Main Results:

  • Key predictors identified: age, BMI, hemoglobin, cholesterol, AST, and albumin.
  • The Extreme Gradient Boosting (XGBoost) model showed strong performance (AUROC 0.831, AUPRC 0.310).
  • The validated XGBoost model was integrated into a web application.

Conclusions:

  • A user-friendly ML predictive model for MHD patient mortality risk was successfully developed.
  • This tool can aid clinicians in identifying high-risk patients.
  • The web application facilitates practical clinical application and improved patient management.