Machine Learning applied to home care for predicting passing away conditions

Detalhes bibliográficos
Autor(a) principal: Silva, Daniel Henrique Cordeiro
Data de Publicação: 2022
Outros Autores: Timo, Elisa Maria do Nascimento
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/36078
Resumo: In home care processes, where multidisciplinary health teams take care of their patients at home, there are several challenges for resource management and remote monitoring, where, sometimes, resources are not used in main priority situations. The advent of technology, the availability of data in management systems and the new decision-making support tools bring enormous possibilities, financial return and greater comfort for patients and families. This work aims to present the application of machine learning, using the CRISP-DM methodology, to identify patients with a greater chance of hospitalization or to pass away at home.
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spelling Machine Learning applied to home care for predicting passing away conditionsMachine Learning aplicado a la atención domiciliaria para predecir las condiciones de muerteMachine Learning aplicado à atenção domiciliar para predição de condição de óbito Atenção domiciliar à saúdeGestão em saúdeCiência de dadosAprendizado de máquinaInteligência artificial.Home careHealthcare managementMachine learningData scienceArtificial intelligence.Atención domiciliariaGestión de la saludCiencia de datosAprendizaje automáticoInteligencia artificial.In home care processes, where multidisciplinary health teams take care of their patients at home, there are several challenges for resource management and remote monitoring, where, sometimes, resources are not used in main priority situations. The advent of technology, the availability of data in management systems and the new decision-making support tools bring enormous possibilities, financial return and greater comfort for patients and families. This work aims to present the application of machine learning, using the CRISP-DM methodology, to identify patients with a greater chance of hospitalization or to pass away at home.En los procesos de atención domiciliaria, donde los pacientes son atendidos en el domicilio por equipos sanitarios multidisciplinares, existen varios retos para la gestión y seguimiento a distancia, no siendo raros los casos en los que no se utilizan recursos en situaciones realmente prioritarias. El advenimiento de la tecnología, la disponibilidad de datos en los distintos sistemas de gestión, así como las nuevas herramientas de apoyo a la decisión, traen enormes posibilidades, retorno económico y mayor cumplimiento para pacientes y familiares. Este trabajo tiene como objetivo presentar la aplicación del aprendizaje automático, utilizando la metodología CRISP-DM, para identificar pacientes con mayor probabilidad de hospitalización o muerte en el hogar.Nos processos de atenção domiciliar, onde pacientes são cuidados em casa por equipes de saúde multidisciplinares, diversos são os desafios para o gerenciamento e monitoramento à distância, não sendo raros os casos em que os recursos não são empregados nas situações realmente prioritárias. O advento da tecnologia, a disponibilidade de dados nos diversos sistemas de gestão e bem como as novas ferramentas de suporte à tomada de decisão trazem enormes possibilidades, retorno financeiro e maior conforto para pacientes e famílias. Este trabalho tem o objetivo de apresentar a aplicação de aprendizado de máquina, utilizando-se da metodologia CRISP-DM, para identificação de pacientes com maior chance de hospitalização ou óbito domiciliar.Research, Society and Development2022-10-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/3607810.33448/rsd-v11i14.36078Research, Society and Development; Vol. 11 No. 14; e230111436078Research, Society and Development; Vol. 11 Núm. 14; e230111436078Research, Society and Development; v. 11 n. 14; e2301114360782525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/36078/30283Copyright (c) 2022 Daniel Henrique Cordeiro Silva; Elisa Maria do Nascimento Timohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSilva, Daniel Henrique Cordeiro Timo, Elisa Maria do Nascimento2022-11-08T13:36:27Zoai:ojs.pkp.sfu.ca:article/36078Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:50:45.673023Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Machine Learning applied to home care for predicting passing away conditions
Machine Learning aplicado a la atención domiciliaria para predecir las condiciones de muerte
Machine Learning aplicado à atenção domiciliar para predição de condição de óbito
title Machine Learning applied to home care for predicting passing away conditions
spellingShingle Machine Learning applied to home care for predicting passing away conditions
Silva, Daniel Henrique Cordeiro
Atenção domiciliar à saúde
Gestão em saúde
Ciência de dados
Aprendizado de máquina
Inteligência artificial.
Home care
Healthcare management
Machine learning
Data science
Artificial intelligence.
Atención domiciliaria
Gestión de la salud
Ciencia de datos
Aprendizaje automático
Inteligencia artificial.
title_short Machine Learning applied to home care for predicting passing away conditions
title_full Machine Learning applied to home care for predicting passing away conditions
title_fullStr Machine Learning applied to home care for predicting passing away conditions
title_full_unstemmed Machine Learning applied to home care for predicting passing away conditions
title_sort Machine Learning applied to home care for predicting passing away conditions
author Silva, Daniel Henrique Cordeiro
author_facet Silva, Daniel Henrique Cordeiro
Timo, Elisa Maria do Nascimento
author_role author
author2 Timo, Elisa Maria do Nascimento
author2_role author
dc.contributor.author.fl_str_mv Silva, Daniel Henrique Cordeiro
Timo, Elisa Maria do Nascimento
dc.subject.por.fl_str_mv Atenção domiciliar à saúde
Gestão em saúde
Ciência de dados
Aprendizado de máquina
Inteligência artificial.
Home care
Healthcare management
Machine learning
Data science
Artificial intelligence.
Atención domiciliaria
Gestión de la salud
Ciencia de datos
Aprendizaje automático
Inteligencia artificial.
topic Atenção domiciliar à saúde
Gestão em saúde
Ciência de dados
Aprendizado de máquina
Inteligência artificial.
Home care
Healthcare management
Machine learning
Data science
Artificial intelligence.
Atención domiciliaria
Gestión de la salud
Ciencia de datos
Aprendizaje automático
Inteligencia artificial.
description In home care processes, where multidisciplinary health teams take care of their patients at home, there are several challenges for resource management and remote monitoring, where, sometimes, resources are not used in main priority situations. The advent of technology, the availability of data in management systems and the new decision-making support tools bring enormous possibilities, financial return and greater comfort for patients and families. This work aims to present the application of machine learning, using the CRISP-DM methodology, to identify patients with a greater chance of hospitalization or to pass away at home.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-25
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/36078
10.33448/rsd-v11i14.36078
url https://rsdjournal.org/index.php/rsd/article/view/36078
identifier_str_mv 10.33448/rsd-v11i14.36078
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/36078/30283
dc.rights.driver.fl_str_mv Copyright (c) 2022 Daniel Henrique Cordeiro Silva; Elisa Maria do Nascimento Timo
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Daniel Henrique Cordeiro Silva; Elisa Maria do Nascimento Timo
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 11 No. 14; e230111436078
Research, Society and Development; Vol. 11 Núm. 14; e230111436078
Research, Society and Development; v. 11 n. 14; e230111436078
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
_version_ 1797052726458187776

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