Forecasting weekly emergency department demand in a Portuguese private hospital

Detalhes bibliográficos
Autor(a) principal: Alfaro, Miguel Alexandre Rocha Martins
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/154017
Resumo: The overcrowding phenomenon is a worldwide problem that has been negatively affecting both public and private hospitals. A suitable and efficient planning of ED resources may diminish the effects of this event. Therefore, a Linear Regression, SARIMAX and Long-Short Term Memory models were developed to forecast weekly ED arrivals. Based on a Machine Learning multi-step ahead predictive tool to help in the decision-making process, the hospital may ensure a good quality of services. First, the predictive tool was used to forecast weekly ED demand for all patients in a big unit of a private Portuguese healthcare provider, CUF, and then, to predict the Urgent Patients weekly ED arrivals for the same unit.
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spelling Forecasting weekly emergency department demand in a Portuguese private hospitalHealthcareEmergency departmentMachine learningTime seriesMulti-step-ahead forecastingDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe overcrowding phenomenon is a worldwide problem that has been negatively affecting both public and private hospitals. A suitable and efficient planning of ED resources may diminish the effects of this event. Therefore, a Linear Regression, SARIMAX and Long-Short Term Memory models were developed to forecast weekly ED arrivals. Based on a Machine Learning multi-step ahead predictive tool to help in the decision-making process, the hospital may ensure a good quality of services. First, the predictive tool was used to forecast weekly ED demand for all patients in a big unit of a private Portuguese healthcare provider, CUF, and then, to predict the Urgent Patients weekly ED arrivals for the same unit.Velho, IolandaRUNAlfaro, Miguel Alexandre Rocha Martins2023-06-16T14:08:28Z2023-01-092023-01-092023-01-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/154017TID:203310586enginfo: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:RCAAP2024-03-11T05:36:31Zoai:run.unl.pt:10362/154017Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:28.705108Repositório 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 Forecasting weekly emergency department demand in a Portuguese private hospital
title Forecasting weekly emergency department demand in a Portuguese private hospital
spellingShingle Forecasting weekly emergency department demand in a Portuguese private hospital
Alfaro, Miguel Alexandre Rocha Martins
Healthcare
Emergency department
Machine learning
Time series
Multi-step-ahead forecasting
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Forecasting weekly emergency department demand in a Portuguese private hospital
title_full Forecasting weekly emergency department demand in a Portuguese private hospital
title_fullStr Forecasting weekly emergency department demand in a Portuguese private hospital
title_full_unstemmed Forecasting weekly emergency department demand in a Portuguese private hospital
title_sort Forecasting weekly emergency department demand in a Portuguese private hospital
author Alfaro, Miguel Alexandre Rocha Martins
author_facet Alfaro, Miguel Alexandre Rocha Martins
author_role author
dc.contributor.none.fl_str_mv Velho, Iolanda
RUN
dc.contributor.author.fl_str_mv Alfaro, Miguel Alexandre Rocha Martins
dc.subject.por.fl_str_mv Healthcare
Emergency department
Machine learning
Time series
Multi-step-ahead forecasting
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Healthcare
Emergency department
Machine learning
Time series
Multi-step-ahead forecasting
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description The overcrowding phenomenon is a worldwide problem that has been negatively affecting both public and private hospitals. A suitable and efficient planning of ED resources may diminish the effects of this event. Therefore, a Linear Regression, SARIMAX and Long-Short Term Memory models were developed to forecast weekly ED arrivals. Based on a Machine Learning multi-step ahead predictive tool to help in the decision-making process, the hospital may ensure a good quality of services. First, the predictive tool was used to forecast weekly ED demand for all patients in a big unit of a private Portuguese healthcare provider, CUF, and then, to predict the Urgent Patients weekly ED arrivals for the same unit.
publishDate 2023
dc.date.none.fl_str_mv 2023-06-16T14:08:28Z
2023-01-09
2023-01-09
2023-01-09T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/154017
TID:203310586
url http://hdl.handle.net/10362/154017
identifier_str_mv TID:203310586
dc.language.iso.fl_str_mv eng
language eng
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.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
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