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

Pulse rhythm01:30

Pulse rhythm

799
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
799
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

726
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
726

You might also read

Related Articles

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

Sort by
Same author

EMBC Special Issue: Modeling Early Oxygenation Trajectory in PARDS from High-Frequency Mechanical Ventilation Signals Using Deep Sequence Architectures.

IEEE transactions on bio-medical engineering·2026
Same author

Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients.

NPJ digital medicine·2026
Same author

A Novel, Interpretable Machine Learning Model Predicts Furosemide Dosing After Congenital Cardiac Surgery.

Pediatric cardiology·2026
Same author

The impact of data source on real-world medication adherence and exposure measures: From prescription to sold.

Journal of managed care & specialty pharmacy·2026
Same author

Targeting of Bacteria Using Amylase-Degradable, Copper-Loaded Starch Nanoparticles.

Antibiotics (Basel, Switzerland)·2026
Same author

LoRA-based methods on Unet for transfer learning in aneurysmal subarachnoid hematoma segmentation.

BMC medical imaging·2025
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

170

Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals.

Olivia P Alge1, Jonathan Gryak2, J Scott VanEpps3,4,5,6,7

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

Diagnostics (Basel, Switzerland)
|February 10, 2024
PubMed
Summary
This summary is machine-generated.

This study explored using privileged information with machine learning for sepsis prognosis. While not significantly improving predictions, electrocardiogram data and privileged information showed promise in specific models for predicting patient deterioration.

Keywords:
machine learningprivileged informationsignal processing

More Related Videos

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients
05:01

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

Published on: October 17, 2017

7.0K
A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats
05:56

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

2.1K

Related Experiment Videos

Last Updated: Jul 4, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

170
A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients
05:01

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

Published on: October 17, 2017

7.0K
A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats
05:56

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

2.1K

Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Critical Care Medicine

Background:

  • Sepsis prognosis is challenging, requiring accurate and timely prediction of patient deterioration.
  • Machine learning models are increasingly used to analyze complex health data for clinical decision support.
  • The learning using privileged information (LUPI) paradigm offers a potential method to enhance predictive accuracy by incorporating auxiliary data.

Purpose of the Study:

  • To apply the LUPI paradigm to sepsis prognosis using electrocardiogram (ECG) and electronic health record (EHR) data.
  • To evaluate the effectiveness of LUPI in predicting increases in the quick Sequential Organ Failure Assessment (qSOFA) score.
  • To assess the utility of ECG signal processing and EHR data in sepsis prognosis models.

Main Methods:

  • Retrospective analysis of patient data from intensive care units (ICUs).
  • Development of support vector machine (SVM) models with and without privileged information.
  • Utilized signal processing techniques on ECG data and integrated EHR variables.
  • Compared model performance on a small, critically ill cohort and a broader ICU cohort.

Main Results:

  • ECG data was found to be informative for predicting sepsis progression in both cohorts.
  • Privileged information demonstrated utility in a signal-informed model within the smaller, critically ill cohort.
  • LUPI did not yield statistically significant improvements in predictive performance across both cohorts in this study.

Conclusions:

  • While LUPI did not significantly enhance sepsis prognosis models in this study, the approach warrants further investigation.
  • ECG signal processing combined with EHR data holds potential for improving sepsis outcome prediction.
  • Future research should explore advanced LUPI strategies and feature engineering for sepsis prognosis.