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

Stereotype Content Model02:16

Stereotype Content Model

13.1K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.1K
Introduction to Sensory Receptors01:31

Introduction to Sensory Receptors

9.0K
Sensory receptors are vital in our ability to perceive and interpret the world. Sensory receptors are specialized cells in the peripheral nervous system that respond to various stimuli and enable one to experience different sensations. Based on specific criteria, sensory receptors are classified into distinct types.
The first classification criterion is based on cell type, position, and function. Some receptor cells are neurons with free nerve endings, where their dendrites are embedded in the...
9.0K
Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Feedback control systems01:26

Feedback control systems

800
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
800
PD Controller: Design01:26

PD Controller: Design

761
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
761
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

376
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
376

You might also read

Related Articles

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

Sort by
Same author

Real-time algorithm-driven ventilation feedback to improve lung-protective ventilation in patients with ARDS (REALVENT-study): study protocol for a multicentre randomised controlled trial.

Respiratory researchĀ·2026
Same author

Effect of early dexamethasone on major complications and all-cause mortality in severe burns (DEXA-BURN): study protocol for a multicenter, randomized, placebo-controlled, double-blind trial.

TrialsĀ·2026
Same author

Impact of high versus low fraction of inspired oxygen for anaesthetic washout and extubation on postoperative pulmonary atelectasis formation and oxygenation: a randomised controlled trial.

British journal of anaesthesiaĀ·2026
Same author

Large language models for optimizing clinical trial recruitment in ICUs: application to ventilator-induced diaphragm dysfunction.

Critical care (London, England)Ā·2026
Same author

Reverse Triggering With and Without Breath Stacking During Pressure-Controlled Ventilation: Exposure Burden and Associations With Ventilation Outcomes.

Respiratory careĀ·2026
Same author

Primary language other than English and the risk of delirium after surgery in an English-speaking hospital: a retrospective cohort study<sup>āœ°</sup>.

British journal of anaesthesiaĀ·2026
Same journal

Correction: VExUS score: optimizing its use in perioperative and critical care management.

Critical care (London, England)Ā·2026
Same journal

Optimizing Ī²-lactam antibiotics with the highest concentration-for continuous infusion reduce carbon footprint in intensive care.

Critical care (London, England)Ā·2026
Same journal

Physiological and clinical effects of selected airway clearance techniques in mechanically ventilated adult ICU patients: a systematic review and synthesis without meta-analysis.

Critical care (London, England)Ā·2026
Same journal

How we use the neurological pupil index (NPi).

Critical care (London, England)Ā·2026
Same journal

45th International Symposium on Intensive Care & Emergency Medicine.

Critical care (London, England)Ā·2026
Same journal

High-resolution monitoring reveals fragmented 24-hour light exposure in intensive care units.

Critical care (London, England)Ā·2026
See all related articles

Related Experiment Video

Updated: Apr 30, 2026

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

8.2K

Subphenotyping prone position responders with machine learning.

Maxime Fosset1,2,3,4, Dario von Wedel1,2,5, Simone Redaelli1,2,6,7

  • 1Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

Critical Care (London, England)
|March 15, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning identified three acute respiratory distress syndrome (ARDS) subphenotypes in patients undergoing prone positioning, with one group showing significantly higher mortality. However, predicting response to prone positioning was not possible with current data.

Keywords:
ARDSClusteringMachine LearningPhenotypesPrecision MedicineProne Position

More Related Videos

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.2K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K

Related Experiment Videos

Last Updated: Apr 30, 2026

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

8.2K
An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.2K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K

Area of Science:

  • Critical Care Medicine
  • Respiratory Medicine
  • Data Science in Healthcare

Background:

  • Acute respiratory distress syndrome (ARDS) is a complex condition with variable patient responses to treatments like prone positioning.
  • Identifying distinct patient groups within ARDS is crucial for personalized treatment strategies.

Purpose of the Study:

  • To utilize machine learning to identify subphenotypes of ARDS patients undergoing prone positioning.
  • To assess the association between these subphenotypes and mortality and response to prone positioning.

Main Methods:

  • A retrospective analysis of 353 mechanically ventilated ARDS patients who received prone positioning.
  • Unsupervised machine learning applied to respiratory mechanics, oxygenation, and demographic data from the supine position.
  • Evaluation of 28-day mortality and response to prone positioning based on key respiratory parameters.

Main Results:

  • Three distinct ARDS subphenotypes were identified.
  • Subphenotype 3 exhibited significantly higher 28-day mortality (56%) compared to others.
  • No significant differences in response to prone positioning were observed across the identified subphenotypes.

Conclusions:

  • Distinct ARDS subphenotypes with differential mortality exist in patients undergoing prone positioning.
  • Current data and methods do not allow prediction of which patients benefit from prone positioning.
  • Further research using multimodal data is needed to better characterize ARDS heterogeneity.