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

You might also read

Related Articles

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

Sort by
Same author

[Genetic structure of X-STR loci in Bai, Dai and Yi ethnic groups and their affinity with five major populations of China].

Yi chuan = Hereditas·2009
Same author

Suppression of lung cancer in murine model: treated by combination of recombinant human endostsatin adenovirus with low-dose cisplatin.

Journal of experimental & clinical cancer research : CR·2009
Same author

[Researches on cloning and expression of the gene encoding Schistosoma japonicum tetraspanins extracellular loop 2 and its immunogenicity].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2009
Same author

Asparanin A induces G(2)/M cell cycle arrest and apoptosis in human hepatocellular carcinoma HepG2 cells.

Biochemical and biophysical research communications·2009
Same author

Pharmacophore modeling study based on known spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors.

Bioorganic & medicinal chemistry letters·2009
Same author

[Application of NIR spectroscopy to multiple gas components identification].

Guang pu xue yu guang pu fen xi = Guang pu·2009

Related Experiment Video

Updated: Dec 11, 2025

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

Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke.

Li Yang1, Qinqin Liu2, Qiuli Zhao2

  • 1School of Nursing, Qingdao University, Qingdao, China.

Brain and Behavior
|August 20, 2020
PubMed
Summary

Machine learning models effectively predict prehospital delay in acute ischemic stroke patients, performing comparably to logistic regression. These tools can aid in assessing stroke risk and improving timely treatment.

Keywords:
Bayesian networkacute ischemic strokemachine learningprehospital delaysupport vector machine

More Related Videos

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.5K
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.5K

Related Experiment Videos

Last Updated: Dec 11, 2025

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
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.5K
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.5K

Area of Science:

  • Neurology
  • Medical Informatics
  • Biostatistics

Background:

  • Prehospital delay significantly impacts outcomes for acute ischemic stroke (AIS) patients.
  • Identifying factors influencing long onset-to-door times is crucial for stroke management.
  • Predictive models can help assess the likelihood of prehospital delay in high-risk populations.

Purpose of the Study:

  • To identify factors associated with prolonged onset-to-door times in AIS patients.
  • To establish predictive models for prehospital delay using machine learning and logistic regression.
  • To compare the predictive performance of machine learning algorithms against traditional logistic regression.

Main Methods:

  • Analysis of medical records and interviews from 450 AIS patients hospitalized between November 2018 and July 2019.
  • Application of Support Vector Machine and Bayesian Network algorithms.
  • Comparison of machine learning models with logistic regression using variable selection methods and Area Under the Curve (AUC) analysis.

Main Results:

  • 87.3% of AIS patients experienced prehospital delay (onset-to-door time ≥ 3 hours).
  • Both machine learning and logistic regression models demonstrated strong predictive performance for prehospital delay (mean AUC range: 0.800-0.846).
  • The performance difference between the best machine learning model and the best logistic regression model was negligible (0.014).

Conclusions:

  • Machine learning algorithms are not inferior to logistic regression for predicting prehospital delay in stroke.
  • All developed models exhibited good discrimination capabilities.
  • These models offer potential for valuable diagnostic programs to predict prehospital delay.