Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/10316/108106 https://doi.org/10.1186/s12911-017-0497-2 |
Resumo: | Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”. Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments. |
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Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windowsNeurodegenerative diseasesMild cognitive impairmentPrognostic predictionTime windowsSupervised learningNeuropsychological dataCognitive DysfunctionDementiaHumansNeuropsychological TestsPrognosisTime FactorsDisease ProgressionModels, TheoreticalSupervised Machine LearningBackground: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”. Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.Springer Nature2017-07-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/108106http://hdl.handle.net/10316/108106https://doi.org/10.1186/s12911-017-0497-2eng1472-6947Pereira, TelmaLemos, LuísCardoso, SandraSilva, DinaRodrigues, AnaSantana, IsabelMendonça, Alexandre deGuerreiro, ManuelaMadeira, Sara C.info: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:RCAAP2023-08-11T15:15:40Zoai:estudogeral.uc.pt:10316/108106Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:24:22.413758Repositório 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 |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
title |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
spellingShingle |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows Pereira, Telma Neurodegenerative diseases Mild cognitive impairment Prognostic prediction Time windows Supervised learning Neuropsychological data Cognitive Dysfunction Dementia Humans Neuropsychological Tests Prognosis Time Factors Disease Progression Models, Theoretical Supervised Machine Learning |
title_short |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
title_full |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
title_fullStr |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
title_full_unstemmed |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
title_sort |
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows |
author |
Pereira, Telma |
author_facet |
Pereira, Telma Lemos, Luís Cardoso, Sandra Silva, Dina Rodrigues, Ana Santana, Isabel Mendonça, Alexandre de Guerreiro, Manuela Madeira, Sara C. |
author_role |
author |
author2 |
Lemos, Luís Cardoso, Sandra Silva, Dina Rodrigues, Ana Santana, Isabel Mendonça, Alexandre de Guerreiro, Manuela Madeira, Sara C. |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Pereira, Telma Lemos, Luís Cardoso, Sandra Silva, Dina Rodrigues, Ana Santana, Isabel Mendonça, Alexandre de Guerreiro, Manuela Madeira, Sara C. |
dc.subject.por.fl_str_mv |
Neurodegenerative diseases Mild cognitive impairment Prognostic prediction Time windows Supervised learning Neuropsychological data Cognitive Dysfunction Dementia Humans Neuropsychological Tests Prognosis Time Factors Disease Progression Models, Theoretical Supervised Machine Learning |
topic |
Neurodegenerative diseases Mild cognitive impairment Prognostic prediction Time windows Supervised learning Neuropsychological data Cognitive Dysfunction Dementia Humans Neuropsychological Tests Prognosis Time Factors Disease Progression Models, Theoretical Supervised Machine Learning |
description |
Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”. Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-19 |
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/10316/108106 http://hdl.handle.net/10316/108106 https://doi.org/10.1186/s12911-017-0497-2 |
url |
http://hdl.handle.net/10316/108106 https://doi.org/10.1186/s12911-017-0497-2 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1472-6947 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer Nature |
publisher.none.fl_str_mv |
Springer Nature |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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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) |
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