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Related Experiment Video

Updated: Jul 19, 2026

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

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Published on: February 22, 2020

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

Geske Luzum¹, Gyrd Thrane², Stina Aam³

¹Department of Neuromedicine and Movement Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.

Archives of Physical Medicine and Rehabilitation

|January 19, 2024

Summary

Machine learning accurately predicts long-term fatigue after stroke. This tool can identify individuals at risk for post-stroke fatigue, aiding early clinical intervention.

Keywords:

Stroke fatigue long-term follow-up machine learning prediction

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Last Updated: Jul 19, 2026

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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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Published on: July 14, 2023

Area of Science:

Neuroscience
Medical Informatics
Rehabilitation Medicine

Background:

Post-stroke fatigue is a common and persistent issue affecting patient recovery and quality of life.
Predicting the onset and severity of chronic fatigue is crucial for timely intervention.

Purpose of the Study:

To develop and validate a machine learning model for predicting fatigue 18 months post-stroke.
To identify key predictors of fatigue from acute and sub-acute stroke phases.

Main Methods:

A prospective multicenter cohort study included 474 stroke participants.
Data from acute and 3-month post-stroke phases were used to train a random forest model.
Fatigue Severity Scale (FSS-7) assessed fatigue at 18 months.

Main Results:

The random forest model achieved 69% accuracy in predicting fatigue at 18 months.
The model demonstrated a sensitivity of 0.69 and specificity of 0.74.
Area Under the Curve (AUC) was 0.79, indicating good predictive performance.

Conclusions:

Machine learning models show promise in predicting chronic post-stroke fatigue.
The developed model has satisfactory predictive ability for clinical application.
Early identification of fatigue risk can inform personalized rehabilitation strategies.