Search research articles

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

Integrated mass-balance-based modeling of cadmium in agricultural soils incorporating erosion processes: Future trends, accumulation rates, and dynamic critical loads.

Environmental pollution (Barking, Essex : 1987)·2026

Same author

Advancements in Dual-Load Antibody-Drug Conjugates and Challenges with Quality Analysis.

Pharmaceuticals (Basel, Switzerland)·2026

Same author

Urinary caffeine and caffeine metabolites, biological aging, and cardiovascular-kidney-metabolic syndrome: deciphering association, mediation, and mechanism via epidemiology and bioinformatics.

NPJ science of food·2026

Same author

A Novel Predictive Model Based on Glutathione Metabolism Genes RRM2 and G6PD in Hepatocellular Carcinoma.

Journal of hepatocellular carcinoma·2026

Same author

Synthesis of a Series of Ln(III) (Ln = La, Ce, Lu) Aryl Complexes and Analysis of Their Ln-L Bonding Using Multinuclear NMR Spectroscopy and DFT Calculations.

Inorganic chemistry·2026

Same author

Application of a novel LC-MS-based proteomic workflow for host cell protein monitoring and risk control in biotherapeutics.

Antibody therapeutics·2026

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models

Published on: March 1, 2015

Predicting Early Dysphagia in Acute Ischemic Stroke Using an Explainable Machine Learning Model.

Ye Li^1,2, Sihao Yu³, Xiaojuan Yu⁴

¹Graduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China.

International Journal of General Medicine

|December 11, 2025

Summary

This summary is machine-generated.

Machine learning accurately predicts early dysphagia in acute ischemic stroke (AIS) patients. The Random Forest model identified key risk factors like ADL grade and NIHSS score, aiding early intervention.

Keywords:

dysphagia ischemic stroke machine learning predictive model risk factors

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

Related Experiment Videos

Last Updated: Jan 9, 2026

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models

Published on: March 1, 2015

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

Area of Science:

Neurology
Medical Informatics
Machine Learning

Background:

Dysphagia is a common complication following acute ischemic stroke (AIS).
Early detection of dysphagia is crucial for patient outcomes and preventing complications.
Predictive models can aid clinicians in identifying at-risk patients.

Purpose of the Study:

To identify key risk factors for early dysphagia in AIS patients.
To develop an explainable machine learning (ML) model for dysphagia prediction.
To evaluate the performance of various ML models in predicting dysphagia.

Main Methods:

A cross-sectional study included 1041 AIS patients.
Feature selection used Boruta algorithm and logistic regression.
Six ML models were trained and evaluated using 10-fold cross-validation, with performance metrics including AUC-ROC, sensitivity, and specificity.

Main Results:

The incidence of early dysphagia in AIS patients was 29.3%.
The Random Forest (RF) model achieved the highest performance (AUC-ROC: 0.952).
Significant predictors included ADL grade, NIHSS score, multifocal lesions, hypoalbuminemia, coronary heart disease, and lesion hemisphere.

Conclusions:

ML models, particularly the RF model, show promise as reliable tools for predicting dysphagia in AIS.
The developed model can assist clinicians in early risk assessment and personalized treatment planning.
Explainable AI (SHAP analysis) provides insights into the identified risk factors.