News & Impact

You are here

Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.

Journal Article
Tozlu C, Edwards D, Boes A, Labar D, Tsagaris KZ, Silverstein J, Pepper Lane H, Sabuncu MR, Liu C, Kuceyeski A.
Year Published: 
Neurorehabil Neural Repair. 2020 May;34(5):428-439. doi: 10.1177/1545968320909796. Epub 2020 Mar 20.
PMID: 32193984 | DOI: 10.1177/1545968320909796
Full-Text on Pubmed



Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. 


The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. 


EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median R2EN=0.91,R2RF=0.88,R2ANN=0.83,R2SVM=0.79,R2CART=0.70;P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere.


Machine learning methods may enable clinicians to accurately predict a chronic stroke patient's postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients' response to therapy and, therefore, could be included in prospective studies.

Conditions & Recovery

Stroke icon
Stroke is the leading cause of disability in the U.S.
Motor Recovery Icon
Write and walk again.