Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.
Neurorehabil Neural Repair
; 34(5): 428-439, 2020 05.
Article
em En
| MEDLINE
| ID: mdl-32193984
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 REN2=0.91,RRF2=0.88,RANN2=0.83,RSVM2=0.79,RCART2=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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Avaliação de Resultados em Cuidados de Saúde
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Redes Neurais de Computação
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Potencial Evocado Motor
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Acidente Vascular Cerebral
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Extremidade Superior
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Estimulação Magnética Transcraniana
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Terapia por Exercício
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Aprendizado de Máquina
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Reabilitação do Acidente Vascular Cerebral
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Córtex Motor
Tipo de estudo:
Clinical_trials
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article