Model for Prediction of Progression in Multiple Sclerosis
2019
Multiple sclerosis is an idiopathic inflammatory disease of the central nervous system and the second most
common cause of disability in young adults. Choosing an effective treatment is crucial to preventing disability.
However, response to treatment varies greatly between patients. Because of this, accurate and timely detection
of individual response to treatment is an essential requisite of efficient personalised multiple sclerosis therapy.
Nowadays, there is a lack of comprehensive predictive models of response to individual treatment.This paper
arises from the clinical need to improve this situation. To achieve it, all patient's information was used to
evaluate the effectiveness of demographic, clinical and paraclinical variables of individual response to fourteen
disease-modifying therapies in MSBase, an international cohort. A personalized prediction model to three stages
of disease, as a support tool in clinical decision making for each MS patient, was developed applying machine
learning and Big Data techniques. These techniques were also used to reduce the data set and define a minimum
set of characteristics for each patient. Best predictors for the response to treatment were identified to refine the
predictive model. Fourteen relevant variables were selected. A web application was implemented to be used to
support the specialist neurologist in real time. This tool provides a prediction of progression in EDSS from the
last relapse of an individual patient, and a report for the medical expert.
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