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

Ceren Tozlu, Dylan Edwards, Aaron Boes, Stanley Fisher, K. Zoe Tsagaris, Joshua Silverstein, Heather Pepper Lane, Mert R. Sabuncu, Charles Liu, Amy Kuceyeski

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

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 (Formula presented.) 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.

Original languageEnglish (US)
Pages (from-to)428-439
Number of pages12
JournalNeurorehabilitation and Neural Repair
Volume34
Issue number5
DOIs
StatePublished - May 1 2020

Keywords

  • chronic stroke
  • Fugl-Meyer Assessment
  • machine learning
  • predictive models
  • white matter disconnectivity

ASJC Scopus subject areas

  • Rehabilitation
  • Neurology
  • Clinical Neurology

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