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: May 26, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
10:15

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Published on: July 2, 2013

Machine learning models in post-stroke aphasia: a scoping review.

Xiaoxue Li1, Hengjie Song1,2, Ningjing Guo1

  • 1School of Nursing, Shanxi Medical University, Shanxi, China.

Frontiers in Neurology
|May 25, 2026
PubMed
Summary

Machine learning models show great potential for diagnosing and predicting outcomes in post-stroke aphasia. Further research with diverse data is needed to improve clinical use.

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

The application of large language models in bariatric surgery: A scoping review.

PloS one·2026
Same author

3D Covalent Organic Network Membranes With Regionally Ordered Nanochannels for Efficient Molecular Sieving.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Capsule neural tensor networks with multi-aspect information for Few-shot Knowledge Graph Completion.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Virtual screening and molecular dynamics simulation for identification of natural antiviral agents targeting SARS-CoV-2 NSP10.

Biochemical and biophysical research communications·2022
Same author

Identification of inhibitors targeting HIF-2α/c-Myc by molecular docking and MM-GBSA technology.

Journal of receptor and signal transduction research·2020

Area of Science:

  • Computational linguistics
  • Neurorehabilitation
  • Artificial intelligence in medicine

Background:

  • Post-stroke aphasia significantly impacts communication and quality of life.
  • Machine learning (ML) offers novel approaches to understand and manage aphasia.

Purpose of the Study:

  • To systematically review ML model applications in post-stroke aphasia.
  • To guide the development and clinical implementation of ML models for aphasia.

Main Methods:

  • Scoping review methodology.
  • Comprehensive literature search across major databases (e.g., Web of Science, PubMed, Embase).
  • Screening, summarization, and analysis of 19 relevant articles.

Main Results:

Keywords:
aphasiamachine learningnursing carescoping reviewstroke

More Related Videos

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke
08:01

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke

Published on: July 10, 2014

Related Experiment Videos

Last Updated: May 26, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
10:15

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Published on: July 2, 2013

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke
08:01

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke

Published on: July 10, 2014

  • Supervised ML algorithms (Random Forests, Neural Networks, SVMs) are predominant.
  • Models utilize diverse, multimodal data sources.
  • ML functions include diagnosis, severity assessment, outcome prediction, and symptom monitoring.

    • Conclusions:

    • ML models demonstrate high applicability and broad potential in post-stroke aphasia management.
    • Future research should focus on multi-center, multi-modal data and external validation for enhanced robustness and clinical feasibility.