Análisis de síntomas de la infección por el virus SARS-CoV-2

Autores/as

  • 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

Palabras clave:

Aprendizaje Automático, COVID-19, SARS-CoV-2

Resumen

Objetivo: Desarrollar una metodología de Minería de Datos y Aprendizaje Automático para el diagnóstico de COVID-19. Método: Crear modelos de diagnóstico, evaluar diferencias en síntomas entre olas pandémica. Resultados: Diagnosticar infección sintomática por SARS-CoV-2. Conclusión: Destacar la eficacia de la metodología en la gestión pandémica.

Biografía del autor/a

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.

Citas

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Lundberg SM, Lee SI. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems. 2017.

Publicado

2024-11-19

Cómo citar

Ulrichsen, F. C., Sena, A. C., Porto, L. C., & Figueiredo, K. (2024). Análisis de síntomas de la infección por el virus SARS-CoV-2. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1339

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