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 Experiment Video

Updated: Jun 28, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review.

Ika Agustin Atika Putri1, Annisa Ristya Rahmanti1, Guardian Yoki Sanjaya1

  • 1Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

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

A Digital Twin Framework for Structural Health Monitoring of Existing Large-Span Bridges.

Sensors (Basel, Switzerland)·2026
Same author

Prediction of Early Functional Outcome After Acute Ischemic Stroke Using Real-World Clinical Data in Vietnam and Indonesia: Retrospective Cohort Study.

Studies in health technology and informatics·2026
Same author

Association Between Long-term Use of H<sub>2</sub> Receptor Antagonists and Prostate Cancer Risk: A Case-Control Study in Taiwan.

Journal of Cancer·2026
Same author

Lessons of practices of community-based maternal and child health surveillance system during and post COVID-19 in Indonesia.

Global health action·2025
Same author

Developing community-based surveillance systems for vaccine-preventable diseases: lessons learned from Indonesia.

Global health action·2025
Same author

A Preliminary Result: Is Implementing WHO SMART Guidelines for AMR and AMU/C Surveillance in Indonesia Feasible?

Studies in health technology and informatics·2025

Machine learning (ML) in stroke research primarily targets tertiary prevention, predicting outcomes using routine clinical data. While promising, challenges in data and model validation persist for broader application.

Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • The Earlier Medicine framework emphasizes proactive and personalized stroke care across primary, secondary, and tertiary prevention levels.
  • Machine learning (ML) offers potential for enhancing stroke research and clinical decision-making.
  • A comprehensive understanding of ML applications in stroke is needed.

Purpose of the Study:

  • To conduct a scoping review of machine learning applications in stroke research.
  • To map the use of ML across primary, secondary, and tertiary stroke prevention.
  • To identify common predictors, ML models, and challenges in the field.

Main Methods:

  • Systematic literature search conducted in PubMed and Scopus databases.
Keywords:
Earlier MedicineMachine LearningStroke

More Related Videos

Application of Bedside Lower Extremity Rehabilitation Robots in Stroke Rehabilitation: A Randomized Controlled Trial
04:38

Application of Bedside Lower Extremity Rehabilitation Robots in Stroke Rehabilitation: A Randomized Controlled Trial

Published on: November 28, 2025

Related Experiment Videos

Last Updated: Jun 28, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Application of Bedside Lower Extremity Rehabilitation Robots in Stroke Rehabilitation: A Randomized Controlled Trial
04:38

Application of Bedside Lower Extremity Rehabilitation Robots in Stroke Rehabilitation: A Randomized Controlled Trial

Published on: November 28, 2025

  • Adherence to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
  • Inclusion of 105 eligible studies focusing on ML in stroke research.

    • Main Results:

    • The majority of studies concentrated on tertiary prevention (e.g., predicting mortality, complications, functional outcomes).
    • Fewer studies addressed primary (risk prediction) or secondary (acute phase detection) prevention.
    • Commonly used predictors included age, NIHSS score, glucose, stroke volume, BMI, hypertension, and diabetes.
    • Traditional ML models (logistic regression, random forest, SVM) are dominant, with a rise in Ensemble/Hybrid and Deep Learning models since 2021.

    Conclusions:

    • Machine learning shows significant promise for improving stroke prediction and patient outcomes.
    • Challenges include data heterogeneity, lack of model transparency, and the need for robust external validation.
    • Further research is required to overcome these limitations and facilitate clinical translation.