Machine Learning Use for Prognostic Purposes in Multiple Sclerosis
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...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/11/2/122 |
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| author | Ruggiero Seccia Silvia Romano Marco Salvetti Andrea Crisanti Laura Palagi Francesca Grassi |
| author_facet | Ruggiero Seccia Silvia Romano Marco Salvetti Andrea Crisanti Laura Palagi Francesca Grassi |
| author_sort | Ruggiero Seccia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:32:53Z |
| format | Article |
| id | doaj.art-10612e4e859a42a998c5ba223f3b7dfb |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T05:32:53Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-10612e4e859a42a998c5ba223f3b7dfb2023-12-03T12:30:24ZengMDPI AGLife2075-17292021-02-0111212210.3390/life11020122Machine Learning Use for Prognostic Purposes in Multiple SclerosisRuggiero Seccia0Silvia Romano1Marco Salvetti2Andrea Crisanti3Laura Palagi4Francesca Grassi5Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, ItalyDepartment of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, ItalyDepartment of Physics, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, ItalyThe 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.https://www.mdpi.com/2075-1729/11/2/122multiple sclerosismachine learningdisease progressionprognostication |
| spellingShingle | Ruggiero Seccia Silvia Romano Marco Salvetti Andrea Crisanti Laura Palagi Francesca Grassi Machine Learning Use for Prognostic Purposes in Multiple Sclerosis Life multiple sclerosis machine learning disease progression prognostication |
| title | Machine Learning Use for Prognostic Purposes in Multiple Sclerosis |
| title_full | Machine Learning Use for Prognostic Purposes in Multiple Sclerosis |
| title_fullStr | Machine Learning Use for Prognostic Purposes in Multiple Sclerosis |
| title_full_unstemmed | Machine Learning Use for Prognostic Purposes in Multiple Sclerosis |
| title_short | Machine Learning Use for Prognostic Purposes in Multiple Sclerosis |
| title_sort | machine learning use for prognostic purposes in multiple sclerosis |
| topic | multiple sclerosis machine learning disease progression prognostication |
| url | https://www.mdpi.com/2075-1729/11/2/122 |
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