Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
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/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. |
id |
RCAP_1b07b89c435cf36ea1d59db24cffc020 |
---|---|
oai_identifier_str |
oai:ubibliorum.ubi.pt:10400.6/14113 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
_version_ |
1817549679496265728 |