Early Prediction of Upper-Limb Recovery Poststroke Using Machine Learning: A Clinically Feasible Approach

Highlight

• A minimal set of bedside clinical tests within 72 hours poststroke predicts 6-month arm motor function with high accuracy.
• Machine learning model utilizes Shoulder Abduction, voluntary finger extension, Fugl-Meyer Upper Extremity score, and NIH Stroke Scale.
• Median absolute error of 5.9 is below the minimal clinically important difference for the Action Research Arm Test.

Study Background