Big data and machine learning to tackle diabetes management

Detalhes bibliográficos
Autor(a) principal: Pina, Ana F
Data de Publicação: 2022
Outros Autores: Meneses, Maria João, Sousa-Lima, Inês, Henriques, Roberto, Raposo, João F, Macedo, Maria Paula
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103994
https://doi.org/10.1111/eci.13890
Resumo: Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.
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spelling Big data and machine learning to tackle diabetes managementbig data; cluster analysis; diabetes; machine learningType 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.2022-10-172023-10-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103994http://hdl.handle.net/10316/103994https://doi.org/10.1111/eci.13890por0014-29721365-2362https://onlinelibrary.wiley.com/doi/10.1111/eci.13890Pina, Ana FMeneses, Maria JoãoSousa-Lima, InêsHenriques, RobertoRaposo, João FMacedo, Maria Paulainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-27T11:10:43Zoai:estudogeral.uc.pt:10316/103994Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:43.903581Repositório 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 Big data and machine learning to tackle diabetes management
title Big data and machine learning to tackle diabetes management
spellingShingle Big data and machine learning to tackle diabetes management
Pina, Ana F
big data; cluster analysis; diabetes; machine learning
title_short Big data and machine learning to tackle diabetes management
title_full Big data and machine learning to tackle diabetes management
title_fullStr Big data and machine learning to tackle diabetes management
title_full_unstemmed Big data and machine learning to tackle diabetes management
title_sort Big data and machine learning to tackle diabetes management
author Pina, Ana F
author_facet Pina, Ana F
Meneses, Maria João
Sousa-Lima, Inês
Henriques, Roberto
Raposo, João F
Macedo, Maria Paula
author_role author
author2 Meneses, Maria João
Sousa-Lima, Inês
Henriques, Roberto
Raposo, João F
Macedo, Maria Paula
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Pina, Ana F
Meneses, Maria João
Sousa-Lima, Inês
Henriques, Roberto
Raposo, João F
Macedo, Maria Paula
dc.subject.por.fl_str_mv big data; cluster analysis; diabetes; machine learning
topic big data; cluster analysis; diabetes; machine learning
description Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-17
2023-10-17T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/103994
http://hdl.handle.net/10316/103994
https://doi.org/10.1111/eci.13890
url http://hdl.handle.net/10316/103994
https://doi.org/10.1111/eci.13890
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv 0014-2972
1365-2362
https://onlinelibrary.wiley.com/doi/10.1111/eci.13890
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