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Elsevier, NeuroImage: Clinical, (7), p. 281-287, 2015

DOI: 10.1016/j.nicl.2014.11.021

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Predicting outcome in clinically isolated syndrome using machine learning

Journal article published in 2014 by V. Wottschel ORCID, D. C. Alexander, P. P. Kwok, D. T. Chard, M. L. Stromillo, N. De Stefano, A. J. Thompson, D. H. Miller, O. Ciccarelli
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient's lesion features and clinical/demographic characteristics.