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Emergency department use and Artificial Intelligence in Pelotas: design and baseline results.

Felipe Mendes DelpinoLílian Munhoz FigueiredoÂndria Krolow CostaIoná CarrenoLuan Nascimento da SilvaAlana Duarte FloresMilena Afonso PinheiroEloisa Porciúncula da SilvaGabriela Ávila MarquesMirelle de Oliveira SaesSuele Manjourany Silva DuroLuiz Augusto FacchiniJoão Ricardo Nickening VissociThaynã Ramos FloresFlavio Fernando DemarcoCauane BlumenbergAlexandre Dias Porto Chiavegatto FilhoInácio Crochemore Mohnsam da SilvaSandro Rogério Rodrigues BatistaRicardo Alexandre ArcêncioBruno Pereira Nunes
Published in: Revista brasileira de epidemiologia = Brazilian journal of epidemiology (2023)
The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.
Keyphrases
  • artificial intelligence
  • emergency department
  • machine learning
  • big data
  • healthcare
  • deep learning
  • randomized controlled trial
  • primary care
  • public health
  • mental health
  • adverse drug
  • health insurance
  • emergency medical