Covid-19 vaccination priorities defined on machine learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista de Saúde Pública |
Texto Completo: | https://www.revistas.usp.br/rsp/article/view/195781 |
Resumo: | OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS: Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS: The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers. |
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Covid-19 vaccination priorities defined on machine learningCOVID-19 vaccines, supply & distributionImmunization ProgramsHealth PrioritiesMachine LearningOBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS: Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS: The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers.Universidade de São Paulo. Faculdade de Saúde Pública2022-03-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/xmlhttps://www.revistas.usp.br/rsp/article/view/19578110.11606/s1518-8787.2022056004045Revista de Saúde Pública; Vol. 56 (2022); 11Revista de Saúde Pública; Vol. 56 (2022); 11Revista de Saúde Pública; v. 56 (2022); 111518-87870034-8910reponame:Revista de Saúde Públicainstname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/rsp/article/view/195781/180680https://www.revistas.usp.br/rsp/article/view/195781/180679Copyright (c) 2022 Renato Camargos Couto, Tania Moreira Grillo Pedrosa, Luciana Moreira Seara, Carolina Seara Couto, Vitor Seara Couto, Karla Giacomin, Ana Claudia Couto de Abreuhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCouto, Renato Camargos Pedrosa, Tania Moreira GrilloSeara, Luciana MoreiraCouto, Carolina SearaCouto, Vitor Seara Giacomin, KarlaAbreu, Ana Claudia Couto de2022-03-18T18:37:51Zoai:revistas.usp.br:article/195781Revistahttps://www.revistas.usp.br/rsp/indexONGhttps://www.revistas.usp.br/rsp/oairevsp@org.usp.br||revsp1@usp.br1518-87870034-8910opendoar:2022-03-18T18:37:51Revista de Saúde Pública - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Covid-19 vaccination priorities defined on machine learning |
title |
Covid-19 vaccination priorities defined on machine learning |
spellingShingle |
Covid-19 vaccination priorities defined on machine learning Couto, Renato Camargos COVID-19 vaccines, supply & distribution Immunization Programs Health Priorities Machine Learning |
title_short |
Covid-19 vaccination priorities defined on machine learning |
title_full |
Covid-19 vaccination priorities defined on machine learning |
title_fullStr |
Covid-19 vaccination priorities defined on machine learning |
title_full_unstemmed |
Covid-19 vaccination priorities defined on machine learning |
title_sort |
Covid-19 vaccination priorities defined on machine learning |
author |
Couto, Renato Camargos |
author_facet |
Couto, Renato Camargos Pedrosa, Tania Moreira Grillo Seara, Luciana Moreira Couto, Carolina Seara Couto, Vitor Seara Giacomin, Karla Abreu, Ana Claudia Couto de |
author_role |
author |
author2 |
Pedrosa, Tania Moreira Grillo Seara, Luciana Moreira Couto, Carolina Seara Couto, Vitor Seara Giacomin, Karla Abreu, Ana Claudia Couto de |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Couto, Renato Camargos Pedrosa, Tania Moreira Grillo Seara, Luciana Moreira Couto, Carolina Seara Couto, Vitor Seara Giacomin, Karla Abreu, Ana Claudia Couto de |
dc.subject.por.fl_str_mv |
COVID-19 vaccines, supply & distribution Immunization Programs Health Priorities Machine Learning |
topic |
COVID-19 vaccines, supply & distribution Immunization Programs Health Priorities Machine Learning |
description |
OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS: Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS: The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-11 |
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://www.revistas.usp.br/rsp/article/view/195781 10.11606/s1518-8787.2022056004045 |
url |
https://www.revistas.usp.br/rsp/article/view/195781 |
identifier_str_mv |
10.11606/s1518-8787.2022056004045 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/rsp/article/view/195781/180680 https://www.revistas.usp.br/rsp/article/view/195781/180679 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/xml |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo. Faculdade de Saúde Pública |
publisher.none.fl_str_mv |
Universidade de São Paulo. Faculdade de Saúde Pública |
dc.source.none.fl_str_mv |
Revista de Saúde Pública; Vol. 56 (2022); 11 Revista de Saúde Pública; Vol. 56 (2022); 11 Revista de Saúde Pública; v. 56 (2022); 11 1518-8787 0034-8910 reponame:Revista de Saúde Pública instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Revista de Saúde Pública |
collection |
Revista de Saúde Pública |
repository.name.fl_str_mv |
Revista de Saúde Pública - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
revsp@org.usp.br||revsp1@usp.br |
_version_ |
1800221802644897792 |