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

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Title: Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.
Authors: Tozlu C; Department of Radiology, Weill Cornell Medicine, New York, NY, USA.; Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA., Edwards D; Moss Rehabilitation Research Institute, Elkins Park, PA, USA.; Edith Cowan University, Joondalup, Australia.; Burke Neurological Institute, White Plains, NY, USA., Boes A; Departments of Pediatrics, Neurology & Psychiatry, Iowa Neuroimaging and Noninvasive Brain Stimulation Laboratory, University of Iowa Hospitals and Clinics, Iowa City, IA, USA., Labar D; Department of Neurology, Weill Cornell Medical College, New York, NY, USA., Tsagaris KZ; Burke Neurological Institute, White Plains, NY, USA., Silverstein J; Burke Neurological Institute, White Plains, NY, USA., Pepper Lane H; Burke Neurological Institute, White Plains, NY, USA., Sabuncu MR; School of Electrical and Computer Engineering and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA., Liu C; USC Neurorestoration Center, Los Angeles, CA.; Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA., Kuceyeski A; Department of Radiology, Weill Cornell Medicine, New York, NY, USA.; Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
Source: Neurorehabilitation and neural repair [Neurorehabil Neural Repair] 2020 May; Vol. 34 (5), pp. 428-439. Date of Electronic Publication: 2020 Mar 20.
Publication Type: Journal Article; Multicenter Study; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
Journal Info: Publisher: Sage Publications Country of Publication: United States NLM ID: 100892086 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-6844 (Electronic) Linking ISSN: 15459683 NLM ISO Abbreviation: Neurorehabil Neural Repair Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:1552-6844
DOI:10.1177/1545968320909796