Covid-19 vaccination priorities defined on machine learning
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista de Saúde Pública |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100209 |
Resumo: | ABSTRACT 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 |
id |
USP-23_f0829da91c332336112a3ab87d09fb59 |
---|---|
oai_identifier_str |
oai:scielo:S0034-89102022000100209 |
network_acronym_str |
USP-23 |
network_name_str |
Revista de Saúde Pública |
repository_id_str |
|
spelling |
Covid-19 vaccination priorities defined on machine learningCOVID-19 vaccines, supply & distributionImmunization ProgramsHealth PrioritiesMachine LearningABSTRACT 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 managersFaculdade de Saúde Pública da Universidade de São Paulo2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100209Revista de Saúde Pública v.56 2022reponame:Revista de Saúde Públicainstname:Universidade de São Paulo (USP)instacron:USP10.11606/s1518-8787.2022056004045info:eu-repo/semantics/openAccessCouto,Renato CamargosPedrosa,Tania Moreira GrilloSeara,Luciana MoreiraCouto,Carolina SearaCouto,Vitor SearaGiacomin,KarlaAbreu,Ana Claudia Couto deeng2022-03-17T00:00:00Zoai:scielo:S0034-89102022000100209Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0034-8910&lng=pt&nrm=isoONGhttps://old.scielo.br/oai/scielo-oai.phprevsp@org.usp.br||revsp1@usp.br1518-87870034-8910opendoar:2022-03-17T00:00Revista 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 |
ABSTRACT 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 |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100209 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102022000100209 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.11606/s1518-8787.2022056004045 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Faculdade de Saúde Pública da Universidade de São Paulo |
publisher.none.fl_str_mv |
Faculdade de Saúde Pública da Universidade de São Paulo |
dc.source.none.fl_str_mv |
Revista de Saúde Pública v.56 2022 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_ |
1748936506703085568 |