Análise de sinais e sintomas da infecção pelo vírus SARS-CoV-2

Autores

  • Felipe Cassemiro Ulrichsen State University of Rio de Janeiro
  • Alexandre Costa Sena State University of Rio de Janeiro
  • Luís Cristóvão Porto State University of Rio de Janeiro
  • Karla Figueiredo State University of Rio de Janeiro

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1339

Palavras-chave:

COVID-19, SARS-CoV-2, Aprendizado de Máquina

Resumo

Objetivo: Desenvolver metodologia de Mineração de Dados e Aprendizado de Máquina para diagnóstico de COVID-19. Método: Criar modelos de diagnóstico, avaliar diferenças nos sintomas entre ondas pandêmicas. Resultados: Diagnosticar infecção sintomática pelo SARS-CoV-2. Conclusão: Destacar a eficácia da metodologia na gestão pandêmica.

Biografia do Autor

Felipe Cassemiro Ulrichsen, State University of Rio de Janeiro

PhD student, Matematical and Statistics Institute, State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.

Alexandre Costa Sena, State University of Rio de Janeiro

PhD/Associate Professor, Matematical and Statistics Institute, State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.

Luís Cristóvão Porto, State University of Rio de Janeiro

PhD/Associate Professor, Piquet Carneiro University Polyclinic (PPC), State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

Karla Figueiredo, State University of Rio de Janeiro

PhD/Associate Professor, Matematical and Statistics Institute, State University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.

Referências

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Menni C, Valdes AM, Freidin MB, et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine. 2020; 26:1037–1040. DOI: https://doi.org/10.1038/s41591-020-0916-2

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Publicado

19-11-2024

Como Citar

Ulrichsen, F. C., Sena, A. C., Porto, L. C., & Figueiredo, K. (2024). Análise de sinais e sintomas da infecção pelo vírus SARS-CoV-2. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1339

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