Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanical Ventilation III: Noninvasive Ventilation

Mechanical Ventilation III: Noninvasive Ventilation

Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
Noninvasive Positive-Pressure Ventilation...

Mechanical Ventilation II: Invasive Ventilation

Mechanical Ventilation II: Invasive Ventilation

Ventilators are essential medical equipment used to aid patients with respiratory difficulties. Their primary function is to assist or replace spontaneous breathing by providing mechanical ventilation. There are two general classes of mechanical ventilators: negative-pressure and positive-pressure ventilators.
Negative-Pressure Ventilators
Negative-pressure ventilators create a vacuum around the chest or body to draw air into the lungs, simulating breathing. This method does not require an...

You might also read

Related Articles

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

Sort by

Same author

Clinical Impact of Guideline-Directed Medical Therapy in Patients with Left Ventricular Assist Device: An International Multicenter Study.

ESC heart failure·2026

Same author

Emotional Impact of Patient Death and Associated Psychological Distress in Cardiac Physicians.

JACC. Advances·2026

Same author

Determinants and outcomes of late electrical storm in patients supported by left ventricular assist device.

Heart rhythm·2026

Same author

Development and Validation of a Multi-Modal Algorithm for Chronic Kidney Disease Detection in a Hospital Clinical Data Warehouse.

Studies in health technology and informatics·2026

Same author

OPTIMA-DAW: Improving Cerebral Vasospasm Detection After Aneurysmal Subarachnoid Haemorrhage Using Machine Learning.

Studies in health technology and informatics·2026

Same author

Qualifying Missingness in Real-World Clinical Data for Secondary Use.

Studies in health technology and informatics·2026

Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026

Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026

Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026

Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026

Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026

Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting

Published on: January 17, 2025

Predicting Successful Weaning from Veno-Arterial ECMO Using Machine Learning.

Mathieu Beaudeau¹, Nicolas Nesseler², Jean-Philippe Verhoye³

¹CHU Rennes, INSERM, LTSI-UMR 1099, Univ Rennes, 35000 Rennes, France.

Studies in Health Technology and Informatics

|October 3, 2025

Summary

This summary is machine-generated.

Machine learning models can predict successful weaning from veno-arterial extracorporeal membrane oxygenation (ECMO). XGBoost achieved the highest accuracy, aiding clinical decisions for acute heart failure patients.

Keywords:

Clinical data warehouse Clinical decision support system Intensive care Machine learning Veno-arterial ECMO

More Related Videos

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse

Published on: October 24, 2018

Related Experiment Videos

Last Updated: Jan 16, 2026

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting

Published on: January 17, 2025

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse

Published on: October 24, 2018

Area of Science:

Cardiology
Medical Informatics
Critical Care Medicine

Background:

Extracorporeal membrane oxygenation (ECMO) provides vital cardiopulmonary support for acute heart failure.
Weaning patients from veno-arterial (V-A) ECMO presents significant clinical challenges and risks.

Purpose of the Study:

To develop and evaluate machine learning models for predicting successful V-A ECMO weaning.
To identify key clinical predictors associated with successful ECMO weaning.

Main Methods:

Retrospective analysis of 122 patients undergoing V-A ECMO at Rennes University Hospital (Jan 2020-Jan 2023).
Training and evaluation of multiple machine learning algorithms (Random Forest, XGBoost, KNN, SVM, logistic regression) using eHOP data.
Performance assessment using Area Under the Curve (AUC) metrics.

Main Results:

Machine learning models demonstrated strong predictive performance, with AUCs ranging from 0.84 to 0.86.
XGBoost achieved the highest AUC of 0.86 (95% CI: 0.72-0.96).
Significant predictors of successful weaning included ECMO flow rate, fraction of inspired oxygen (FmO2), and ECMO duration.

Conclusions:

Machine learning models show promise in assisting clinicians with V-A ECMO weaning decisions.
Further external validation is necessary to integrate these predictive tools into clinical practice.
Identifying key predictors can optimize patient management during ECMO support.