摘要：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= $$0.86\pm 0.07$$ , sensitivity= $$0.76\pm 0.14$$ and specificity= $$0.77\pm 0.05$$ ; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC= $$0.89\pm 0.03$$ , sensitivity= $$0.84\pm 0.11$$ , and specificity= $$0.81\pm 0.05$$ . 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.
其他摘要：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= $$0.86\pm 0.07$$ 0.86 ± 0.07 , sensitivity= $$0.76\pm 0.14$$ 0.76 ± 0.14 and specificity= $$0.77\pm 0.05$$ 0.77 ± 0.05 ; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC= $$0.89\pm 0.03$$ 0.89 ± 0.03 , sensitivity= $$0.84\pm 0.11$$ 0.84 ± 0.11 , and specificity= $$0.81\pm 0.05$$ 0.81 ± 0.05 . 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.
