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Accuracy of Machine Learning to Predict Upper-Limb Outcome Within the First 72 Hours Poststroke.

Govert J van der Gun¹, Ruud W Selles^1,2, Carel G M Meskers^3,4

¹Department of Rehabilitation Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands. (G.J.v.d.G., R.W.S.).

|June 10, 2026

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This summary is machine-generated.

This study developed a machine learning model to predict upper-limb motor recovery after stroke using simple bedside tests. The model accurately predicts the 6-month Action Research Arm Test score within 72 hours poststroke.

Area of Science:

Neurology
Rehabilitation Medicine
Artificial Intelligence in Medicine

Background:

Accurate prediction of post-stroke motor outcome is crucial for rehabilitation planning.
Existing bedside models for upper-limb recovery prediction need improvement for early clinical use within 72 hours of stroke.
This study focuses on developing a refined prediction model for stroke patients.

Purpose of the Study:

To develop and internally validate a machine learning model for predicting the 6-month Action Research Arm Test (ARAT) score.
To utilize simple clinical tests commonly assessed within the first 3 days post-stroke for prediction.
To enhance the accuracy and feasibility of early post-stroke motor outcome prediction.

Main Methods:

Utilized data from 296 first-ever ischemic stroke patients across 4 Dutch cohort studies (2000-2019).

Keywords:

feasibility studies humans ischemic stroke machine learning shoulder

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Compared cross-validated prediction performance of multiple eXtreme Gradient Boosting models using various bedside clinical tests.

Selected a minimal predictor set model balancing feasibility and accuracy, validated on a separate test dataset (n=32) using median absolute error.

Main Results:

A model incorporating Shoulder Abduction (Motricity Index), voluntary finger extension, Fugl-Meyer Upper Extremity score, and National Institutes of Health Stroke Scale score demonstrated optimal performance.
The selected model achieved a median absolute error of 5.9 on the 0-57 ARAT score, indicating a good balance between simplicity and predictive accuracy.
The model's performance was evaluated using median absolute error and interquartile range (2.9-12.9).

Conclusions:

The developed machine learning model accurately predicts the 6-month ARAT score using a minimal set of bedside clinical tests.
Predictions are feasible within the first 3 days after stroke.
The model's median absolute error is below the minimal clinically important difference of 6 points for the ARAT, suggesting clinical relevance for rehabilitation planning.