Artificial Neural Networks in Diabetes Control
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/10174/15760 https://doi.org/10.1109/SAI.2015.7237169 |
Resumo: | Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening. |
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Artificial Neural Networks in Diabetes ControlDiabetes MellitusLogic ProgrammingArtificial Neural NetworksQuality-of-InformationDegree-of-ConfidenceDiabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening.IEEE2015-09-11T12:22:44Z2015-09-112015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/15760http://hdl.handle.net/10174/15760https://doi.org/10.1109/SAI.2015.7237169engFernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P. & Neves J., Artificial Neural Networks in Diabetes Control. In Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370, IEEE Edition, 2015.9ISBN: 978-1-4799-8547-0http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7237169DQUIfilipe_fernandes719@msn.comhvicente@uevora.ptabelha@di.uminho.ptjmac@di.uminho.ptpjon@di.uminho.ptjneves@di.uminho.ptProceedings of the 2015 Science and Information Conference (SAI 2015)Fernandes, FilipeVicente, HenriqueAbelha, AntónioMachado, JoséNovais, PauloNeves, Joséinfo: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:RCAAP2024-01-03T19:01:55Zoai:dspace.uevora.pt:10174/15760Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:08:14.335299Repositório 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 |
Artificial Neural Networks in Diabetes Control |
title |
Artificial Neural Networks in Diabetes Control |
spellingShingle |
Artificial Neural Networks in Diabetes Control Fernandes, Filipe Diabetes Mellitus Logic Programming Artificial Neural Networks Quality-of-Information Degree-of-Confidence |
title_short |
Artificial Neural Networks in Diabetes Control |
title_full |
Artificial Neural Networks in Diabetes Control |
title_fullStr |
Artificial Neural Networks in Diabetes Control |
title_full_unstemmed |
Artificial Neural Networks in Diabetes Control |
title_sort |
Artificial Neural Networks in Diabetes Control |
author |
Fernandes, Filipe |
author_facet |
Fernandes, Filipe Vicente, Henrique Abelha, António Machado, José Novais, Paulo Neves, José |
author_role |
author |
author2 |
Vicente, Henrique Abelha, António Machado, José Novais, Paulo Neves, José |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Fernandes, Filipe Vicente, Henrique Abelha, António Machado, José Novais, Paulo Neves, José |
dc.subject.por.fl_str_mv |
Diabetes Mellitus Logic Programming Artificial Neural Networks Quality-of-Information Degree-of-Confidence |
topic |
Diabetes Mellitus Logic Programming Artificial Neural Networks Quality-of-Information Degree-of-Confidence |
description |
Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-09-11T12:22:44Z 2015-09-11 2015-01-01T00:00:00Z |
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/10174/15760 http://hdl.handle.net/10174/15760 https://doi.org/10.1109/SAI.2015.7237169 |
url |
http://hdl.handle.net/10174/15760 https://doi.org/10.1109/SAI.2015.7237169 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P. & Neves J., Artificial Neural Networks in Diabetes Control. In Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370, IEEE Edition, 2015. 9 ISBN: 978-1-4799-8547-0 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7237169 DQUI filipe_fernandes719@msn.com hvicente@uevora.pt abelha@di.uminho.pt jmac@di.uminho.pt pjon@di.uminho.pt jneves@di.uminho.pt Proceedings of the 2015 Science and Information Conference (SAI 2015) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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 |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
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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