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Multiple Sclerosis is a degenerative condition of the central nervous systemthat affects nearly 2.5 million of individuals in terms of their physical,cognitive, psychological and social capabilities. Researchers are currentlyinvestigating on the use of patient reported outcome measures for theassessment of impact and evolution of the disease on the life of the patients.To date, a clear understanding on the use of such measures to predict theevolution of the disease is still lacking. In this work we resort toregularized machine learning methods for binary classification and multipleoutput regression. We propose a pipeline that can be used to predict thedisease progression from patient reported measures. The obtained model istested on a data set collected from an ongoing clinical research project.

A temporal model for multiple sclerosis course evolution