The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

Machine learning use for prognostic purposes in multiple sclerosis / Seccia, Ruggiero; Romano, Silvia; Salvetti, Marco; Crisanti, Andrea; Palagi, Laura; Grassi, Francesca. - In: LIFE. - ISSN 2075-1729. - 11:2(2021). [10.3390/life11020122]

Machine learning use for prognostic purposes in multiple sclerosis

Ruggiero Seccia;Silvia Romano;Marco Salvetti;Andrea Crisanti;Laura Palagi;Francesca Grassi
2021

Abstract

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.
2021
multiple sclerosis; machine learning; disease progression; prognostication
01 Pubblicazione su rivista::01a Articolo in rivista
Machine learning use for prognostic purposes in multiple sclerosis / Seccia, Ruggiero; Romano, Silvia; Salvetti, Marco; Crisanti, Andrea; Palagi, Laura; Grassi, Francesca. - In: LIFE. - ISSN 2075-1729. - 11:2(2021). [10.3390/life11020122]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1492440
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