Search

Your search keyword '"de Brito, Ana Maria"' showing total 8 results
Start Over Author "de Brito, Ana Maria" Publication Year Range Last 3 years
8 results on '"de Brito, Ana Maria"'

1. Hepatitis B prevalence among men who have sex with men in Brazil

Author

Motta-Castro, Ana Rita C, Kerr, Ligia, Kendall, Carl, Mota, Rosa S, Guimaraes, Mark Drew C, Leal, Andrea Fachel, Merchan-Hamann, Edgar, Dourado, Ines, Veras, Maria Amelia, de Brito, Ana Maria, Pontes, Alexandre Kerr, Macena, Raimunda Hermelinda M, Knauth, Daniela, Lima, Luana NGC, Cavalcante, Socorro, Diaz-Bermudez, Ximena P, Oliveira, Lisangela C, Magno, Laio, Camillo, Ana Claudia, Lemos, Marcílio F, Silva, Vanessa Cristina M, Compri, Adriana P, and Moreira, Regina Celia

Published
2023

2. Hepatitis C virus prevalence among men who have sex with men: a cross-sectional study in 12 Brazilian cities

Author

Silva, Vanessa C. M., Kerr, Lígia R. F. S., Kendall, Carl, Mota, Rosa S., Guimarães, Mark Drew C., Leal, Andréa F., Merchan-Hamann, Edgar, Dourado, Inês, Veras, Maria Amélia, de Brito, Ana Maria, Pontes, Alexandre K., Macena, Raimunda H. M., Knauth, Daniela, Lima, Luana N. G. C., Cavalcante, Socorro, Camillo, Ana Cláudia, Díaz-Bermudez, Ximena P., Oliveira, Lisangela C., Magno, Laio, Lemos, Marcílio F., Compri, Adriana P., Motta-Castro, Ana Rita C., and Moreira, Regina C.

Published
2023
Full Text
View/download PDF

6. Perceived Risk of HIV Infection and Acceptability of PrEP among Men Who Have Sex with Men in Brazil

Author

Soares, Fabiane, primary, Magno, Laio, additional, da Silva, Luís Augusto V., additional, Guimarães, Mark Drew Crosland, additional, Leal, Andrea Fachel, additional, Knauth, Daniela, additional, Veras, Maria Amélia, additional, de Brito, Ana Maria, additional, Kendall, Carl, additional, Kerr, Ligia Regina Franco Sansigolo, additional, and Dourado, Inês, additional

Published
2022
Full Text
View/download PDF

8. Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts.

Author

Wichmann, Roberta Moreira, Fernandes, Fernando Timoteo, Chiavegatto Filho, Alexandre Dias Porto, IACOV-BR Network, Ciconelle, Ana Claudia Martins, de Brito, Ana Maria Espírito Santo, Nunes, Bruno Pereira, Silva, Dárcia Lima e, Anschau, Fernando, de Castro Rodrigues, Henrique, Rocha, Hermano Alexandre Lima, dos Reis, João Conrado Bueno, de Oliveira Cavalcante, Liane, de Oliveira, Liszt Palmeira, dos Santos Andrade, Lorena Sofia, Nasi, Luiz Antonio, de Maria Felix, Marcelo, Mimica, Marcelo Jenne, de Almeida Araujo, Maria Elizete, and Arnoni, Mariana Volpe

Subjects
MACHINE learning, MACHINE performance, BOOSTING algorithms, RECEIVER operating characteristic curves, COVID-19 testing, REVERSE transcriptase polymerase chain reaction
Abstract

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results