Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps

Detalhes bibliográficos
Autor(a) principal: Araújo, Suellen Munique
Data de Publicação: 2023
Outros Autores: Nery, Sabrina Beatriz Mendes, Magalhães, Bianca G., Almeida, Kelson James, Gaspar, Pedro Dinis
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.6/14113
Resumo: Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements.
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spelling Disease Severity Index in Parkinson’s Disease Based on Self-Organizing MapsNeural networksKohonen mapsParkinson’s diseaseParkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements.Applied SciencesuBibliorumAraújo, Suellen MuniqueNery, Sabrina Beatriz MendesMagalhães, Bianca G.Almeida, Kelson JamesGaspar, Pedro Dinis2024-01-23T11:41:12Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/14113engAraújo, S.M.; Nery, S.B.M.; Magalhães, B.G.; Almeida, K.J.; Gaspar, P.D. Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps. Appl. Sci. 2023, 13, 10019. https://doi.org/ 10.3390/app1318100192076-341710.3390/app131810019info: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:RCAAP2024-11-27T12:44:09Zoai:ubibliorum.ubi.pt:10400.6/14113Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-27T12:44:09Repositório 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 Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
title Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
spellingShingle Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
Araújo, Suellen Munique
Neural networks
Kohonen maps
Parkinson’s disease
title_short Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
title_full Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
title_fullStr Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
title_full_unstemmed Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
title_sort Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
author Araújo, Suellen Munique
author_facet Araújo, Suellen Munique
Nery, Sabrina Beatriz Mendes
Magalhães, Bianca G.
Almeida, Kelson James
Gaspar, Pedro Dinis
author_role author
author2 Nery, Sabrina Beatriz Mendes
Magalhães, Bianca G.
Almeida, Kelson James
Gaspar, Pedro Dinis
author2_role author
author
author
author
dc.contributor.none.fl_str_mv uBibliorum
dc.contributor.author.fl_str_mv Araújo, Suellen Munique
Nery, Sabrina Beatriz Mendes
Magalhães, Bianca G.
Almeida, Kelson James
Gaspar, Pedro Dinis
dc.subject.por.fl_str_mv Neural networks
Kohonen maps
Parkinson’s disease
topic Neural networks
Kohonen maps
Parkinson’s disease
description Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
2024-01-23T11:41:12Z
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/10400.6/14113
url http://hdl.handle.net/10400.6/14113
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Araújo, S.M.; Nery, S.B.M.; Magalhães, B.G.; Almeida, K.J.; Gaspar, P.D. Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps. Appl. Sci. 2023, 13, 10019. https://doi.org/ 10.3390/app131810019
2076-3417
10.3390/app131810019
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Applied Sciences
publisher.none.fl_str_mv Applied Sciences
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution 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 mluisa.alvim@gmail.com
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