Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal

Detalhes bibliográficos
Autor(a) principal: Patrício, André
Data de Publicação: 2021
Outros Autores: Costa, Rafael S., Henriques, Rui
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/128459
Resumo: DSAIPA/AI/ 0044/2018
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network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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spelling Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in PortugalLongitudinal Cohort studyClinical informaticsCOVID-19Data modelingIntensive care admissionsMachine learningPredictive modelsRespiratory assistanceHealth InformaticsSDG 3 - Good Health and Well-beingDSAIPA/AI/ 0044/2018Background: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. Objective: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. Methods: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. Results: For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision. Conclusions: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.LAQV@REQUIMTERUNPatrício, AndréCosta, Rafael S.Henriques, Rui2021-11-29T23:39:40Z2021-04-012021-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/128459engPURE: 34773631https://doi.org/10.2196/26075info: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:RCAAP2024-03-18T01:42:26Zoai:run.unl.pt:10362/128459Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:18.527365Repositório 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 Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
Longitudinal Cohort study
title Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
spellingShingle Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
Patrício, André
Clinical informatics
COVID-19
Data modeling
Intensive care admissions
Machine learning
Predictive models
Respiratory assistance
Health Informatics
SDG 3 - Good Health and Well-being
title_short Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
title_full Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
title_fullStr Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
title_full_unstemmed Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
title_sort Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
author Patrício, André
author_facet Patrício, André
Costa, Rafael S.
Henriques, Rui
author_role author
author2 Costa, Rafael S.
Henriques, Rui
author2_role author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
RUN
dc.contributor.author.fl_str_mv Patrício, André
Costa, Rafael S.
Henriques, Rui
dc.subject.por.fl_str_mv Clinical informatics
COVID-19
Data modeling
Intensive care admissions
Machine learning
Predictive models
Respiratory assistance
Health Informatics
SDG 3 - Good Health and Well-being
topic Clinical informatics
COVID-19
Data modeling
Intensive care admissions
Machine learning
Predictive models
Respiratory assistance
Health Informatics
SDG 3 - Good Health and Well-being
description DSAIPA/AI/ 0044/2018
publishDate 2021
dc.date.none.fl_str_mv 2021-11-29T23:39:40Z
2021-04-01
2021-04-01T00:00:00Z
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 http://hdl.handle.net/10362/128459
url http://hdl.handle.net/10362/128459
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 34773631
https://doi.org/10.2196/26075
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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