Abstract #3381

Machine learning based prediction of clinical progression in multiple sclerosis

Samantha Noteboom¹, Moritz Seiler², Claudia Chien², Roshan P. Rane², Eva M. M. Strijbis³, Friedemann Paul², Menno M. Schoonheim¹, and Kerstin Ritter²

¹MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, Netherlands, ²Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, ³MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, Netherlands

Synopsis

Keywords: Multiple Sclerosis, Brain

Machine learning may aid in individualized prediction of disease progression in multiple sclerosis (MS). In this study, we used data from 354 patients with MS to evaluate the capability of different machine learning approaches at predicting future disease worsening. Multiple clinical endpoints of disease worsening were tested and different combinations of clinical and structural MRI measures were used as inputs. Machine learning models were capable of discriminating between patients with low and high disability but did not perform well in predicting future disease course.

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