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

Micro(nano)plastics in the Development of Myocardial Fibrosis: From Clinical Detection to Molecular Mechanism.

Circulation research·2026
Same author

Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes.

Molecular neurobiology·2026
Same author

Polystyrene nanoplastics induce hepatic steatosis by disrupting autophagic degradation of NCoR1 and suppressing PPARα-mediated fatty acid oxidation.

Journal of nanobiotechnology·2026
Same author

Emerging nanomedicine strategies for hepatocellular carcinoma therapy.

iMetaOmics·2026
Same author

Mucosal Colonized Engineered Nissle 1917 for Targeted Gut Delivery and Restored Microbiota Homeostasis in Inflammatory Bowel Disease.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Targeting ion channel dysregulation in tumors: emerging therapeutic opportunities.

Trends in pharmacological sciences·2025

Related Experiment Video

Updated: Jul 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study.

Wencan Ji1, Canjun Wang2, Hanqing Chen3

  • 1Nanjing Medical University, Nanjing, China; Jiangsu Research Center for Primary Health Development and General Practice Education, Jiangsu, China; Department of General Practice, Zhongda Hospital, Southeast University, Nanjing, China.

Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association
|September 16, 2023
PubMed
Summary
This summary is machine-generated.

A new Gaussian Naïve Bayes model accurately predicts post-stroke cognitive impairment (PSCI) using key factors like age and C-reactive protein. This tool aids in early detection and prevention strategies for high-risk patients after acute ischemic stroke.

Keywords:
Cognitive impairmentGaussian Naïve BayesIschemic strokeMachine learningPrediction modelSHAP

More Related Videos

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

3.0K
Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
09:10

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

8.6K

Related Experiment Videos

Last Updated: Jul 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

3.0K
Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
09:10

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

8.6K

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Biostatistics

Background:

  • Post-stroke cognitive impairment (PSCI) is a significant complication requiring early detection and management.
  • Developing a diagnostic prediction model for PSCI is clinically important for stroke survivors.

Purpose of the Study:

  • To utilize machine learning algorithms to identify key predictors of PSCI.
  • To develop and validate a predictive model for PSCI occurrence within 3-6 months post-acute ischemic stroke (AIS).

Main Methods:

  • A prospective study involved 331 patients from Affiliated Zhongda Hospital and 66 from an external validation cohort.
  • Nine machine learning classification models were integrated to determine the optimal predictive model.
  • Shapley Additive exPlanations (SHAP) were used for personalized risk assessment and model interpretation.

Main Results:

  • Key predictors identified for PSCI include age, education, NIHSS, CWMD, Hcy, and CRP.
  • The Gaussian Naïve Bayes (GNB) model demonstrated superior performance with an AUC of 0.925 in the validation set.
  • The GNB model achieved high accuracy (0.864), sensitivity (0.818), and specificity (0.932) in test sets.

Conclusions:

  • A GNB model, interpreted using SHAP, effectively predicts PSCI.
  • These findings support the development of preventive strategies for individuals at high risk of PSCI.
  • The model can guide clinical decisions for early intervention in stroke patients.