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Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Mechanical Ventilation II: Invasive Ventilation01:23

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...
Mechanical Ventilation III: Noninvasive Ventilation01:23

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 (NIPPV)
Mechanical Ventilation I: Indication and Settings01:29

Mechanical Ventilation I: Indication and Settings

Mechanical ventilation is a life-saving technique for managing acute respiratory failure and other respiratory complications. The process involves using a machine known as a ventilator to supply oxygen to the lungs and assist in removing carbon dioxide. It serves as a bridge to long-term mechanical ventilation or a temporary measure until ventilatory support is discontinued. The ventilator can maintain this function for a prolonged period, providing critical support for patients until they can...

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Related Experiment Video

Updated: Jul 16, 2026

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse
06:41

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse

Published on: October 24, 2018

A High Granularity Machine Learning Model to Predict Successful Weaning During V-V ECMO.

Sukethram Sivakumar1,2, Leon Fan3, Mingfeng Cao3

  • 1Division of Neuroscience Critical Care, Departments of Neurosurgery, Anesthesiology, Critical Care Medicine, The Johns Hopkins Hospital, Baltimore, Maryland, USA.

Artificial Organs
|July 14, 2026
PubMed
Summary

High-granularity machine learning models accurately predict venovenous extracorporeal membrane oxygenation (V-V ECMO) weaning outcomes. These models, utilizing electronic medical record data, outperformed the standard Respiratory ECMO Survival Prediction score.

Related Experiment Videos

Last Updated: Jul 16, 2026

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse
06:41

Veno-Venous Extracorporeal Membrane Oxygenation in a Mouse

Published on: October 24, 2018

Area of Science:

  • Critical Care Medicine
  • Machine Learning in Healthcare
  • Extracorporeal Membrane Oxygenation

Background:

  • Weaning from venovenous extracorporeal membrane oxygenation (V-V ECMO) lacks standardized protocols.
  • Delayed weaning increases risks of vascular injury and multi-organ failure.
  • Existing prediction scores have limitations in guiding V-V ECMO weaning decisions.

Purpose of the Study:

  • To evaluate if high-granularity electronic medical record (EMR) data can predict V-V ECMO weaning outcomes.
  • To compare the predictive performance of machine learning (ML) models using EMR data against the Respiratory ECMO Survival Prediction (RESP) score.

Main Methods:

  • Retrospective review of adult V-V ECMO patients (2016-2024).
  • Development of ML models using RESP score alone and longitudinal EMR time-series data (pre-ECMO, first 24h, last 24h, full run).
  • Utilized Random Forest, CatBoost, AdaBoost, and XGBoost algorithms with SHAP for feature importance analysis.

Main Results:

  • 119 patients included; 52.1% experienced in-hospital mortality.
  • ML model using full ECMO run data (Model E) achieved the highest AUROC of 0.904, outperforming the RESP score model (Model A, AUROC 0.815).
  • Key predictors identified: Body Mass Index (BMI), pulse, SpO2, and immunocompromised status.

Conclusions:

  • High-granularity ML models accurately predict V-V ECMO weaning success.
  • BMI, age, and immunocompromised status are significant predictors of weaning outcomes.
  • These ML models demonstrate superior performance to RESP score-based models, suggesting clinical utility.