Journal article
Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke
Neurorehabilitation and neural repair, Vol.34(5), pp.428-439
05/2020
DOI: 10.1177/1545968320909796
PMCID: PMC7217740
PMID: 32193984
Abstract
. 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.
. 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
.
. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median
< .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.
Details
- Title: Subtitle
- Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke
- Creators
- Ceren Tozlu - Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USADylan Edwards - Burke Neurological Institute, White Plains, NY, USAAaron Boes - Departments of Pediatrics, Neurology & Psychiatry, Iowa Neuroimaging and Noninvasive Brain Stimulation Laboratory, University of Iowa Hospitals and Clinics, Iowa City, IA, USADouglas Labar - Department of Neurology, Weill Cornell Medical College, New York, NY, USAK Zoe Tsagaris - Burke Neurological Institute, White Plains, NY, USAJoshua Silverstein - Burke Neurological Institute, White Plains, NY, USAHeather Pepper Lane - Burke Neurological Institute, White Plains, NY, USAMert R Sabuncu - School of Electrical and Computer Engineering and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USACharles Liu - Rancho Los Amigos National Rehabilitation Center, Downey, CA, USAAmy Kuceyeski - Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Resource Type
- Journal article
- Publication Details
- Neurorehabilitation and neural repair, Vol.34(5), pp.428-439
- DOI
- 10.1177/1545968320909796
- PMID
- 32193984
- PMCID
- PMC7217740
- NLM abbreviation
- Neurorehabil Neural Repair
- ISSN
- 1545-9683
- eISSN
- 1552-6844
- Publisher
- United States
- Grant note
- R01 LM012719 / NLM NIH HHS R01 NS102646 / NINDS NIH HHS R01 AG053949 / NIA NIH HHS R21 NS104634 / NINDS NIH HHS
- Language
- English
- Date published
- 05/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Neurology; Psychiatry; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Neurology (Pediatrics)
- Record Identifier
- 9984070409002771
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