Prediction of disease progression and outcomes in multiple sclerosis with machine learning
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/101248 https://doi.org/10.1038/s41598-020-78212-6 |
Resumo: | 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. |
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Prediction of disease progression and outcomes in multiple sclerosis with machine learningAdultDiagnosis, Computer-AssistedDisease ProgressionFemaleHumansMaleMultiple Sclerosis, Relapsing-RemittingMachine LearningMultiple 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.2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/101248http://hdl.handle.net/10316/101248https://doi.org/10.1038/s41598-020-78212-6eng2045-2322Pinto, Mauro F.Oliveira, HugoBatista, SóniaCruz, LuísPinto, MafaldaCorreia, InêsMartins, PedroTeixeira, Césarinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-08-18T20:43:41Zoai:estudogeral.uc.pt:10316/101248Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:29.090674Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
title |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
spellingShingle |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning Pinto, Mauro F. Adult Diagnosis, Computer-Assisted Disease Progression Female Humans Male Multiple Sclerosis, Relapsing-Remitting Machine Learning |
title_short |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
title_full |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
title_fullStr |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
title_full_unstemmed |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
title_sort |
Prediction of disease progression and outcomes in multiple sclerosis with machine learning |
author |
Pinto, Mauro F. |
author_facet |
Pinto, Mauro F. Oliveira, Hugo Batista, Sónia Cruz, Luís Pinto, Mafalda Correia, Inês Martins, Pedro Teixeira, César |
author_role |
author |
author2 |
Oliveira, Hugo Batista, Sónia Cruz, Luís Pinto, Mafalda Correia, Inês Martins, Pedro Teixeira, César |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Pinto, Mauro F. Oliveira, Hugo Batista, Sónia Cruz, Luís Pinto, Mafalda Correia, Inês Martins, Pedro Teixeira, César |
dc.subject.por.fl_str_mv |
Adult Diagnosis, Computer-Assisted Disease Progression Female Humans Male Multiple Sclerosis, Relapsing-Remitting Machine Learning |
topic |
Adult Diagnosis, Computer-Assisted Disease Progression Female Humans Male Multiple Sclerosis, Relapsing-Remitting Machine Learning |
description |
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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/101248 http://hdl.handle.net/10316/101248 https://doi.org/10.1038/s41598-020-78212-6 |
url |
http://hdl.handle.net/10316/101248 https://doi.org/10.1038/s41598-020-78212-6 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2045-2322 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134079670550528 |