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Luciana Moreira Seara

Biographic Data

ID8524554
NAMELuciana Moreira Seara
GIVEN NAMESLuciana Moreira
FAMILY NAMESeara
SIGNATURESEARA L M
AFFILIATIONSMinistério da Saúde
ORCID0000-0001-9822-6056
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
AUTHOR COUNT2
EDITOR COUNT0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2024
H-INDEX0
  • Development of a machine learning modelto estimate length of stay in coronaryartery bypass grafting

    Open Access•Ronaldo Costa Couto, Tânia Moreira Grillo Pedrosa et al.•ARTICLE•Revista de Saúde Pública•2024

    OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting.METHODS: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patient…

  • Covid-19 vaccination priorities defined on machine learning

    Open Access•Ronaldo Costa Couto, Tânia Moreira Grillo Pedrosa et al.•ARTICLE•Revista de Saúde Pública•2022

    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 …

No prominent works on this page.

  • Covid-19 vaccination priorities defined on machine learning

    Open Access•Ronaldo Costa Couto, Tânia Moreira Grillo Pedrosa et al.•ARTICLE•Revista de Saúde Pública•2022

    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 …

  • Development of a machine learning modelto estimate length of stay in coronaryartery bypass grafting

    Open Access•Ronaldo Costa Couto, Tânia Moreira Grillo Pedrosa et al.•ARTICLE•Revista de Saúde Pública•2024

    OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting.METHODS: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patient…

Computer Science (2 works) · Medicine (2 works) · Sepsis Diagnosis and Treatment (2 works) · 2019-20 coronavirus outbreak (1 works) · Artery (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Betacoronavirus (1 works) · Bypass grafting (1 works) · Cardiology (1 works)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae