Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eig...

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Bibliographic Details
Main Authors: Adrian Ion-Mărgineanu, Gabriel Kocevar, Claudio Stamile, Diana M. Sima, Françoise Durand-Dubief, Sabine Van Huffel, Dominique Sappey-Marinier
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-07-01
Series:Frontiers in Neuroscience
Subjects:
multiple sclerosis
longitudinal analysis
magnetic resonance spectroscopic imaging
EDSS
lesion load
machine learning
Online Access:http://journal.frontiersin.org/article/10.3389/fnins.2017.00398/full

Internet

http://journal.frontiersin.org/article/10.3389/fnins.2017.00398/full

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