A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10174/25496 https://doi.org/10.1007/978-3-030-16447-8_5 |
Resumo: | Thyroid Dysfunction is a clinical condition that affects thyroid behaviour and is reported to be the most common in all endocrine disorders. It is a multiple factorial pathology condition due to the high incidence of hypothyroidism and hyperthyroidism, which is becoming a serious health problem requiring a detailed study for early diagnosis and monitoring. Understanding the prevalence and risk factors of thyroid disease can be very useful to identify patients for screening and/or follow-up and to minimize their collateral effects. Thus, this paper describes the development of a decision support system that aims to help physicians in the decision-making process regarding thyroid dysfunction assessment. The proposed problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that have been articulated as an Artificial Neural Network approach to data processing, complemented by an unusual approach to Knowledge Representation and Argumentation that takes into account the data elements entropic states. The model performs well in the thyroid dysfunction assessment with an accuracy ranging between 93.2% and 96.9%. |
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A Many-Valued Empirical Machine for Thyroid Dysfunction AssessmentThyroid DysfunctionKnowledge Representation and ReasoningArtificial Neural NetworksEntropyLogic ProgrammingMany-Valued Empirical MachineThyroid Dysfunction is a clinical condition that affects thyroid behaviour and is reported to be the most common in all endocrine disorders. It is a multiple factorial pathology condition due to the high incidence of hypothyroidism and hyperthyroidism, which is becoming a serious health problem requiring a detailed study for early diagnosis and monitoring. Understanding the prevalence and risk factors of thyroid disease can be very useful to identify patients for screening and/or follow-up and to minimize their collateral effects. Thus, this paper describes the development of a decision support system that aims to help physicians in the decision-making process regarding thyroid dysfunction assessment. The proposed problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that have been articulated as an Artificial Neural Network approach to data processing, complemented by an unusual approach to Knowledge Representation and Argumentation that takes into account the data elements entropic states. The model performs well in the thyroid dysfunction assessment with an accuracy ranging between 93.2% and 96.9%.Springer2019-04-24T14:58:07Z2019-04-242019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/25496http://hdl.handle.net/10174/25496https://doi.org/10.1007/978-3-030-16447-8_5engSantos, S., Martins, M.R., Vicente, H., Barroca, M.G., Calisto, F., Gama, C., Ribeiro, J., Machado, J., Ávidos, L., Araújo, N., Dias, A. and Neves, J. A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 273, 47–57, 2019.1867-8211 (paper)1867-822X (electronic)https://link.springer.com/chapter/10.1007/978-3-030-16447-8_5CQE; HERCULESsofialousadasantos@gmail.commrm@uevora.pthvicente@uevora.ptmaria-gabriel.barroca@synlab.ptfernando.calisto@synlab.ptcesar.gama@synlab.ptjribeiro@estg.ipvc.ptjoana.mmachado@gmail.comliliana.avidos@ipsn.cespu.ptnuno.araujo@ipsn.cespu.pta.almeida.dias@gmail.comjneves@di.uminho.ptSantos, SofiaMartins, M. RosárioVicente, HenriqueBarroca, M. GabrielCalisto, FernandoGama, CésarRibeiro, JorgeMachado, JoanaÁvidos, LilianaAraújo, NunoDias, AlmeidaNeves, 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:RCAAP2024-01-03T19:19:18Zoai:dspace.uevora.pt:10174/25496Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:15:54.441356Repositório 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 |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
title |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
spellingShingle |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment Santos, Sofia Thyroid Dysfunction Knowledge Representation and Reasoning Artificial Neural Networks Entropy Logic Programming Many-Valued Empirical Machine |
title_short |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
title_full |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
title_fullStr |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
title_full_unstemmed |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
title_sort |
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment |
author |
Santos, Sofia |
author_facet |
Santos, Sofia Martins, M. Rosário Vicente, Henrique Barroca, M. Gabriel Calisto, Fernando Gama, César Ribeiro, Jorge Machado, Joana Ávidos, Liliana Araújo, Nuno Dias, Almeida Neves, José |
author_role |
author |
author2 |
Martins, M. Rosário Vicente, Henrique Barroca, M. Gabriel Calisto, Fernando Gama, César Ribeiro, Jorge Machado, Joana Ávidos, Liliana Araújo, Nuno Dias, Almeida Neves, José |
author2_role |
author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Santos, Sofia Martins, M. Rosário Vicente, Henrique Barroca, M. Gabriel Calisto, Fernando Gama, César Ribeiro, Jorge Machado, Joana Ávidos, Liliana Araújo, Nuno Dias, Almeida Neves, José |
dc.subject.por.fl_str_mv |
Thyroid Dysfunction Knowledge Representation and Reasoning Artificial Neural Networks Entropy Logic Programming Many-Valued Empirical Machine |
topic |
Thyroid Dysfunction Knowledge Representation and Reasoning Artificial Neural Networks Entropy Logic Programming Many-Valued Empirical Machine |
description |
Thyroid Dysfunction is a clinical condition that affects thyroid behaviour and is reported to be the most common in all endocrine disorders. It is a multiple factorial pathology condition due to the high incidence of hypothyroidism and hyperthyroidism, which is becoming a serious health problem requiring a detailed study for early diagnosis and monitoring. Understanding the prevalence and risk factors of thyroid disease can be very useful to identify patients for screening and/or follow-up and to minimize their collateral effects. Thus, this paper describes the development of a decision support system that aims to help physicians in the decision-making process regarding thyroid dysfunction assessment. The proposed problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that have been articulated as an Artificial Neural Network approach to data processing, complemented by an unusual approach to Knowledge Representation and Argumentation that takes into account the data elements entropic states. The model performs well in the thyroid dysfunction assessment with an accuracy ranging between 93.2% and 96.9%. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-04-24T14:58:07Z 2019-04-24 2019-01-01T00:00:00Z |
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/10174/25496 http://hdl.handle.net/10174/25496 https://doi.org/10.1007/978-3-030-16447-8_5 |
url |
http://hdl.handle.net/10174/25496 https://doi.org/10.1007/978-3-030-16447-8_5 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Santos, S., Martins, M.R., Vicente, H., Barroca, M.G., Calisto, F., Gama, C., Ribeiro, J., Machado, J., Ávidos, L., Araújo, N., Dias, A. and Neves, J. A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 273, 47–57, 2019. 1867-8211 (paper) 1867-822X (electronic) https://link.springer.com/chapter/10.1007/978-3-030-16447-8_5 CQE; HERCULES sofialousadasantos@gmail.com mrm@uevora.pt hvicente@uevora.pt maria-gabriel.barroca@synlab.pt fernando.calisto@synlab.pt cesar.gama@synlab.pt jribeiro@estg.ipvc.pt joana.mmachado@gmail.com liliana.avidos@ipsn.cespu.pt nuno.araujo@ipsn.cespu.pt a.almeida.dias@gmail.com jneves@di.uminho.pt |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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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RCAAP |
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RCAAP |
reponame_str |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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