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Pedro Ledic Assaf

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Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset - Scientific Reports

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By Magda Carvalho Pires, Rafael Silva, Daniela Ponce, Fernanda Rodrigues, Fernando Anschau, Fernando Antonio Botoni, Isabela Gomes, José Miguel Chatkin, Luciane Kopittke, Luiza Marques, Patrícia Klarmann Ziegelmann, Silvia Araujo Verified, Yuri Ramires, Marcelo Carneiro, Alexandre Schwarzbold, Daniel Silveira, Guilherme Nascimento, Heloisa Reniers Vianna, Henrique Cerqueira Guimarães, Leila Beltrami Moreira, Milton Henriques Guimarães-Júnior, Talita Oliveira, Marcos André Gonçalves, Polianna Delfino Pereira, Elayne Crestani Pereira, Frederico Bartolazzi, Helena Carolina Noal, Helena Duani, Julia Teixeira Tupinambás, Juliana Machado Rugolo, Karen Brasil Ruschel, Liege Barella Zandoná, Lílian Santos Pinheiro, Luisa Argolo Assis, Magda Cesar Raposo, Maiara Anschau Floriani, Pedro Gibson Paraiso, Roberta Senger, Rochele Mosmann Menezes, Saionara Cristina Francisco, Tatiana Kurtz, Tatiani Oliveira Fereguetti, Barbara Lopes Farace, Fernanda Barbosa Lucas, Fernando Graça Aranha, Giovanna Grunewald Vietta, Pedro Ledic Assaf, Raquel Lutkmeier, Reginaldo Aparecido Valacio, Renan Goulart Finger, Rufino de Freitas
| Nature Verified
Abstract The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model).
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