Analysis of the open database of the General Directorate of Epidemiology using Deep Learning to predict the need for intubation in patients hospitalized for COVID-19


  • Omar Fabián Rivera Ceniceros Universidad Politécnica de Durango
  • Luis Alberto Ordaz Díaz Universidad Politécnica de Durango



COVID-19, Deep learning, Sequential neural network


Using deep learning, the aim is to determine the possibility that a patient hospitalized by COVID-19 suffers from respiratory failure and needs to be mechanically ventilated in a medical intensive care unit (ICU). The deep analysis is performed by training the Sequential Neural Networks algorithm, since these present good efficiency in the analysis of open data. For this study, the open database of the General Directorate of Epidemiology was used. According to the official decrees of the federation, the historical databases and the information related to the cases associated with COVID-19 are of free use with the purpose of facilitating access, use, reuse and redistribution to all users who require it. The database of the General Directorate of Epidemiology presents various information that, according to an interview with a first-line doctor who works with COVID-19 patients and in his opinion, some data may be irrelevant, as the nationality of people infected, to mention a few; likewise, we worked only with those patients who tested positive for the disease. In the same way, the database can be used to find some other aspects or relevant statistical data about the COVID-19 pandemic in México.


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Author Biography

Luis Alberto Ordaz Díaz, Universidad Politécnica de Durango

Profesor investigador de tiempo completo en la Universidad Politécnica de Durango, pertenece al Sistema Nacional de Investigadores (SNI) Nivel 1. 


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How to Cite

Rivera Ceniceros, O. F., & Ordaz Díaz, L. A. (2021). Analysis of the open database of the General Directorate of Epidemiology using Deep Learning to predict the need for intubation in patients hospitalized for COVID-19. Revista De Ciencias Tecnológicas, 4(3), 195–207.



Research articles