Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke
Authors
Ceren Tozlu
Dylan Edwards, Edith Cowan UniversityFollow
Aaron Boes
Douglas Labar
K. Zoe Tsagaris
Joshua Silverstein
Heather Pepper Lane
Mert R. Sabuncu
Charles Liu
Amy Kuceyeski
Document Type
Journal Article
Publication Title
Neurorehabilitation and Neural Repair
Publisher
SAGE Publications Inc.
School
School of Medical and Health Sciences
RAS ID
31496
Abstract
Background. 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. Objectives. 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. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median ��2EN=0.91, ��2RF=0.88, ��2ANN=0.83, ��2SVM=0.79, ��2CART=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. Conclusion. 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. © The Author(s) 2020.
DOI
10.1177/1545968320909796
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Comments
Tozlu, C., Edwards, D., Boes, A., Labar, D., Tsagaris, K. Z., Silverstein, J., ... & Kuceyeski, A. (2020). Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke. Neurorehabilitation and Neural Repair, 34(5) 428–439. https://doi.org/10.1177/1545968320909796