Artikler med mandater om offentlig tilgang - Tiago Carvalho, Ph.D.Les mer
Ikke tilgjengelig noe sted: 1
An RS-GIS-based comprehensiveimpact assessment of floods—A case study in Madeira River, Western Brazilian Amazon
LBL Santos, T Carvalho, LO Anderson, CM Rudorff, V Marchezini, ...
IEEE Geoscience and Remote Sensing Letters 14 (9), 1614-1617, 2017
Mandater: German Research Foundation
Tilgjengelige et eller annet sted: 13
Proceedings of the 3rd IPLeiria’s International Health Congress: Leiria, Portugal. 6-7 May 2016
CC Tomás, E Oliveira, D Sousa, M Uba-Chupel, G Furtado, C Rocha, ...
BMC Health Services Research 16, 111-242, 2016
Mandater: Fundação para a Ciência e a Tecnologia, Portugal
About interfaces between machine learning, complex networks, survivability analysis, and disaster risk reduction
LBL Santos, LR Londe, TJ de Carvalho, D S Menasché, ...
Towards mathematics, computers and environment: A disasters perspective, 185-215, 2019
Mandater: German Research Foundation
Towards neonatal mortality risk classification: A data-driven approach using neonatal, maternal, and social factors
CE Beluzo, E Silva, LC Alves, RC Bresan, NM Arruda, R Sovat, ...
Informatics in medicine unlocked 20, 100398, 2020
Mandater: Bill & Melinda Gates Foundation
Maternal characteristics and the risk of neonatal mortality in Brazil between 2006 and 2016
PH Costa, LC Alves, CE Beluzo, NM Arruda, RC Bresan, T Carvalho
International Journal of Population Studies 5 (2), 24-33, 2019
Mandater: Bill & Melinda Gates Foundation
Assessing the performance of machine learning models to predict neonatal mortality risk in Brazil, 2000-2016
LC Alves, CE Beluzo, NM Arruda, RC Bresan, T Carvalho
medRxiv, 2020.05. 22.20109165, 2020
Mandater: Bill & Melinda Gates Foundation
SPNeoDeath: A demographic and epidemiological dataset having infant, mother, prenatal care and childbirth data related to births and neonatal deaths in São Paulo city Brazil …
CE Beluzo, E Silva, LC Alves, RC Bresan, NM Arruda, R Sovat, ...
Data in brief 32, 106093, 2020
Mandater: Bill & Melinda Gates Foundation
Machine learning to predict neonatal mortality using public health data from sao paulo-Brazil
CE Beluzo, LC Alves, E Silva, R Bresan, N Arruda, T Carvalho
medRxiv, 2020.06. 19.20112953, 2020
Mandater: Bill & Melinda Gates Foundation
NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting
CE Beluzo, LC Alves, NM Arruda, C Sepetauskas, E Silva, T Carvalho
medRxiv, 2021.04. 22.21255916, 2021
Mandater: Bill & Melinda Gates Foundation
Machine Learning for Neonatal Mortality Risk Assessment: A Case Study Using Public Health Data from São Paulo
CE Beluzo, LC Alves, R Bresan, N Arruda, R Sovat, T Carvalho
medRxiv, 2020.05. 25.20112896, 2020
Mandater: Bill & Melinda Gates Foundation
Insights on neonatal mortality: differences between public and private health access at São Paulo city between 2012 and 2017
CE Beluzo, MZ Zaniboni, T Carvalho, LC Alves
medRxiv, 2022.09. 02.22278781, 2022
Mandater: Bill & Melinda Gates Foundation
The maternal profile associated with the underlying root cause of neonatal mortality using machine learning and administrative health data
CE Beluzo, T Carvalho, LC Alves
medRxiv, 2022.08. 15.22278783, 2022
Mandater: Bill & Melinda Gates Foundation
Big Data Visualization Methods Applied in the Context of Neonatal Mortality
CE Beluzo, LR Pimentel, TJ de Carvalho
Anais do Computer on the Beach 11, 592-595, 2020
Mandater: Bill & Melinda Gates Foundation
Comparação entre uma neuroprevisão (empírica) e um modelo físico simplificado para estimação hidrológica
LBL Santos, OA Candido, GRT Lima, AR Carvalho, T Carvalho
Proceeding Series of the Brazilian Society of Computational and Applied …, 2018
Mandater: German Research Foundation
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