Zobrazeno 1 - 10 of 12 pro vyhledávání: '"Fernando Timoteo Fernandes"'
1
Akademický článek
Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts
Autor: Roberta Moreira Wichmann, Fernando Timoteo Fernandes, Alexandre Dias Porto Chiavegatto Filho, IACOV-BR Network
Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
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 ident
Externí odkaz: https://doaj.org/article/5efa086e908b432199861ac96016e18d
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2
Akademický článek
A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil
Autor: Fernando Timoteo Fernandes, Tiago Almeida de Oliveira, Cristiane Esteves Teixeira, Andre Filipe de Moraes Batista, Gabriel Dalla Costa, Alexandre Dias Porto Chiavegatto Filho
Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
Abstract The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing
Externí odkaz: https://doaj.org/article/66f01b49c2e04bd0b657561542dabf2c
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3
Akademický článek
Recovering records on cancer of the larynx from anonymous health information databases
Autor: Fernando Timoteo Fernandes, Diego Rodrigues Mendonça e Silva, Felipe Campos, Vilma Sousa Santana, Lucas Cuani, Maria Paula Curado, Leonardo Salvi, Eduardo Algranti
Publikováno v: Revista Brasileira de Epidemiologia, Vol 24 (2021)
ABSTRACT: Objective: To develop a linkage algorithm to match anonymous death records of cancer of the larynx (ICD-10 C32X), retrieved from the Mortality Information System (SIM) and the Hospital Information System of the Brazilian Unified National He
Externí odkaz: https://doaj.org/article/57f53d5abdd4474497610e7347535ac6
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4
Akademický článek
Perspectivas do uso de mineração de dados e aprendizado de máquina em saúde e segurança no trabalho
Autor: Fernando Timoteo Fernandes, Alexandre Dias Porto Chiavegatto Filho
Publikováno v: Revista Brasileira de Saúde Ocupacional, Vol 44 (2019)
Resumo Introdução: a variedade, volume e velocidade de geração de dados (big data) possibilitam novas e mais complexas análises. Objetivo: discutir e apresentar técnicas de mineração de dados (data mining) e de aprendizado de máquina (machin
Externí odkaz: https://doaj.org/article/e0e785757bce4347b259033b1e13e522
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5
Akademický článek
Prediction of absenteeism in public schools teachers with machine learning
Autor: Fernando Timoteo Fernandes, Alexandre Dias Porto Chiavegatto Filho
Publikováno v: Revista de Saúde Pública
ABSTRACT OBJECTIVE To predict the risk of absence from work due to morbidities of teachers working in early childhood education in the municipal public schools, using machine learning algorithms. METHODS This is a cross-sectional study using secondar
Externí odkaz: https://doaj.org/article/c305069f5318425fb26bab157bb0c74c
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6
Sex-Specific Mortality from Asbestos-Related Diseases, Lung and Ovarian Cancer in Municipalities with High Asbestos Consumption, Brazil, 2000–2017
Autor: Cézar Akiyoshi Saito, Marco Antonio Bussacos, Leonardo Salvi, Carolina Mensi, Dario Consonni, Fernando Timoteo Fernandes, Felipe Campos, Franciana Cavalcante, Eduardo Algranti
Publikováno v: International Journal of Environmental Research and Public Health; Volume 19; Issue 6; Pages: 3656
The aim of this study is to compare the mortality rates for typical asbestos-related diseases (ARD-T: mesothelioma, asbestosis, and pleural plaques) and for lung and ovarian cancer in Brazilian municipalities where asbestos mines and asbestos-cement
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8334aa98f4a8a31ff7e4ad10d0fbe875
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7
Machine learning em saúde e segurança do trabalhador: perspectivas, desafios e aplicações
Autor: Fernando Timoteo Fernandes
Publikováno v: Biblioteca Digital de Teses e Dissertações da USP
Universidade de São Paulo (USP)
instacron:USP
Algoritmos de machine learning têm impactado a área da saúde nos últimos anos. Muita dessa popularidade deve-se aos ganhos de performance preditiva em comparação aos modelos estatísticos tradicionais, já que estes algoritmos conseguem captura
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1cb1eb0a3d520795203d8c046ce0a28
https://doi.org/10.11606/t.6.2021.tde-27012022-140548
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8
A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil
Autor: Tiago Almeida de Oliveira, André Filipe de Moraes Batista, Gabriel Dalla Costa, Fernando Timoteo Fernandes, Alexandre Dias Porto Chiavegatto Filho, Cristiane Esteves Teixeira
Publikováno v: Scientific Reports
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Scientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
IntroductionThe new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in develop
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afc9a93395c7f237e383351f943625b0
http://europepmc.org/articles/PMC7870665
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10
Recuperação de registros de câncer de laringe de bancos de dados anônimos de saúde
Autor: Fernando Timoteo Fernandes, Diego Rodrigues Mendonça e Silva, Felipe Campos, Vilma Sousa Santana, Lucas Cuani, Maria Paula Curado, Leonardo Salvi, Eduardo Algranti
Publikováno v: Revista Brasileira de Epidemiologia v.24 2021
Revista brasileira de epidemiologia
Associação Brasileira de Saúde Coletiva (ABRASCO)
instacron:ABRASCO
Revista Brasileira de Epidemiologia, Volume: 24, Article number: e210011, Published: 02 APR 2021
Revista Brasileira de Epidemiologia, Vol 24 (2021)
Objective: To develop a linkage algorithm to match anonymous death records of cancer of the larynx (ICD-10 C32X), retrieved from the Mortality Information System (SIM) and the Hospital Information System of the Brazilian Unified National Health Syste
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27076fa1ef0bec6cb5bca797c3ea4a8c
https://pubmed.ncbi.nlm.nih.gov/33825773
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