EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/148223 |
Resumo: | Among other hospitalization causes and cases, the clinical emergency is a critical case and the data of the reporting patients are biased as well as poorly managed due to the chaotic situation. The world has faced chaos over the past year due to the frequent waves of COVID-19 and the resulting emergencies. The data banks, linked with the clinical emergencies require serious quantitative and qualitative analysis to drive interpretable conclusions for necessary future emergency measures and to develop explainable artificial intelligence tools. This important procedure involves the clear understanding of the data patterns and topologies, which is a great challenge for the multidimensional data sets. Mathematically, the topological mapping can resolve this problem by mapping higher-dimensional data to two-dimensional representation, based on the overall association. Proper data mining and pattern recognition can help in improving the rapid patients admission, in providing the medical resources timely and in proper patient administration. In this paper, the importance of self-organizing maps, to interpret the hospital data, particularly for the COVID-19 epidemic is discussed in detail. Important variables are identified with the aid of networks and mappings. |
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EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATACiências Tecnológicas, Ciências da engenharia e tecnologiasTechnological sciences, Engineering and technologyAmong other hospitalization causes and cases, the clinical emergency is a critical case and the data of the reporting patients are biased as well as poorly managed due to the chaotic situation. The world has faced chaos over the past year due to the frequent waves of COVID-19 and the resulting emergencies. The data banks, linked with the clinical emergencies require serious quantitative and qualitative analysis to drive interpretable conclusions for necessary future emergency measures and to develop explainable artificial intelligence tools. This important procedure involves the clear understanding of the data patterns and topologies, which is a great challenge for the multidimensional data sets. Mathematically, the topological mapping can resolve this problem by mapping higher-dimensional data to two-dimensional representation, based on the overall association. Proper data mining and pattern recognition can help in improving the rapid patients admission, in providing the medical resources timely and in proper patient administration. In this paper, the importance of self-organizing maps, to interpret the hospital data, particularly for the COVID-19 epidemic is discussed in detail. Important variables are identified with the aid of networks and mappings.2022-052022-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfimage/pnghttps://hdl.handle.net/10216/148223eng0218-348X10.1142/s0218348x22401223Zhenhua YuAyesha SohailTaher A. NofalJoão Manuel R. S. Tavaresinfo: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:RCAAP2023-11-29T15:25:46Zoai:repositorio-aberto.up.pt:10216/148223Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:23:30.291925Repositório 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 |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
title |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
spellingShingle |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA Zhenhua Yu Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
title_short |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
title_full |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
title_fullStr |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
title_full_unstemmed |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
title_sort |
EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA |
author |
Zhenhua Yu |
author_facet |
Zhenhua Yu Ayesha Sohail Taher A. Nofal João Manuel R. S. Tavares |
author_role |
author |
author2 |
Ayesha Sohail Taher A. Nofal João Manuel R. S. Tavares |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Zhenhua Yu Ayesha Sohail Taher A. Nofal João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
topic |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
description |
Among other hospitalization causes and cases, the clinical emergency is a critical case and the data of the reporting patients are biased as well as poorly managed due to the chaotic situation. The world has faced chaos over the past year due to the frequent waves of COVID-19 and the resulting emergencies. The data banks, linked with the clinical emergencies require serious quantitative and qualitative analysis to drive interpretable conclusions for necessary future emergency measures and to develop explainable artificial intelligence tools. This important procedure involves the clear understanding of the data patterns and topologies, which is a great challenge for the multidimensional data sets. Mathematically, the topological mapping can resolve this problem by mapping higher-dimensional data to two-dimensional representation, based on the overall association. Proper data mining and pattern recognition can help in improving the rapid patients admission, in providing the medical resources timely and in proper patient administration. In this paper, the importance of self-organizing maps, to interpret the hospital data, particularly for the COVID-19 epidemic is discussed in detail. Important variables are identified with the aid of networks and mappings. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05 2022-05-01T00:00:00Z |
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 |
https://hdl.handle.net/10216/148223 |
url |
https://hdl.handle.net/10216/148223 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0218-348X 10.1142/s0218348x22401223 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf image/png |
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) |
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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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