Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

Chen, Shi; Paul, Rajib; Janies, Daniel; Murphy, Keith; Feng, Tinghao; Thill, Jean-Claude

Chen, Shi; Paul, Rajib; Janies, Daniel; Murphy, Keith; Feng, Tinghao; Thill, Jean-Claude.

Afiliação

Chen S; Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.
Paul R; School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, United States.
Janies D; Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.
Murphy K; School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, United States.
Feng T; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States.
Thill JC; Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.

Front Public Health ; 9: 661615, 2021.

Article em En | MEDLINE | ID: mdl-34291025

Assuntos

COVID-19; Aprendizado Profundo; China; Estudos de Viabilidade; Humanos; Pandemias; SARS-CoV-2

Palavras-chave

COVID-19; deep learning; epidemic; modeling; multivariate

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Asia Idioma: En Revista: Front Public Health Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Suíça

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