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Title: Prediction of disease progression and outcomes in multiple sclerosis with machine learning
Authors: Pinto, Mauro F. 
Oliveira, Hugo 
Batista, Sónia 
Cruz, Luís
Pinto, Mafalda
Correia, Inês
Martins, Pedro 
Teixeira, César 
Issue Date: 2020
Project: FCT and European Social Fund - project CISUC - UID/CEC/00326/2020 
FCT, POCH and EU - PhD grant SFRH/BD/139757/2018 
Serial title, monograph or event: Scientific Reports
Volume: 10
Issue: 1
Abstract: Multiple Sclerosis is a chronic inflammatory disease, affecting the Central Nervous System and leading to irreversible neurological damage, such as long term functional impairment and disability. It has no cure and the symptoms vary widely, depending on the affected regions, amount of damage, and the ability to activate compensatory mechanisms, which constitutes a challenge to evaluate and predict its course. Additionally, relapsing-remitting patients can evolve its course into a secondary progressive, characterized by a slow progression of disability independent of relapses. With clinical information from Multiple Sclerosis patients, we developed a machine learning exploration framework concerning this disease evolution, more specifically to obtain three predictions: one on conversion to secondary progressive course and two on disease severity with rapid accumulation of disability, concerning the 6th and 10th years of progression. For the first case, the best results were obtained within two years: AUC=[Formula: see text], sensitivity=[Formula: see text] and specificity=[Formula: see text]; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC=[Formula: see text], sensitivity=[Formula: see text], and specificity=[Formula: see text]. The Expanded Disability Status Scale value, the majority of functional systems, affected functions during relapses, and age at onset were described as the most predictive features. These results demonstrate the possibility of predicting Multiple Sclerosis progression by using machine learning, which may help to understand this disease's dynamics and thus, advise physicians on medication intake.
ISSN: 2045-2322
DOI: 10.1038/s41598-020-78212-6
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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