ApliCação de deep learnIng na epidemiologia Paisagística utilizando dados do DataSUS para previsão e análise espacial de dOenças endêmicas e crônicas não transmissíveis no BRAsil (CAIPORA)

  • Ref: 444761/2023-3
  • Funding agency: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
  • Realization: 2023 - 2025
  • PI: Gregori de Arruda Moreira (Instituto Federal de São Paulo, Brasil)
  • Reasearchers: Alexandre Cacheffo, Andrés M. Vélez-Pereira, Ediclê de Souza Fernandes Duarte, Eduardo Landulfo, Ezequiel Zamora Ledesma, Fábio Juliano da Silva Lopes, Jonatan João da Silva, Juan Luis Guerrero Rascado, Maria Florencia Tames, Maria João Tavares Costa, Marlon Miguel Cedeno Puig, Oscar Arnulfo Fajardo Montaña, Paloma Carinaños González, Samara Carbone, Stephanie Marina Díaz López

Abstract

The main objective of the project is to research, test and validate a predictive model that, based on a time series of meteorological variables, pollutant concentrations and social indicators, is capable of predicting, for all Brazilian capitals, mortality and hospital admissions for cardiovascular, cerebrovascular and respiratory diseases, as well as admissions of patients with mental disorders, recorded in various systems of the SUS Informatics Department Data Bank (DATASUS), using CID-10 as a reference.