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

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

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, s...

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Hlavní autoři: Rivera-Ceniceros, Omar Fabián, Ordaz-Díaz, Luis Alberto
Médium: Online
Jazyk:spa
Vydáno: Universidad Autónoma de Baja California 2021
On-line přístup:https://recit.uabc.mx/index.php/revista/article/view/134
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spelling recit-article-1342022-10-26T18:24:31Z 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 Análisis de la base de datos abierta de Dirección General de Epidemiología haciendo uso de Deep Learning para la predicción de la necesidad de intubación en pacientes hospitalizados por COVID-19 Rivera-Ceniceros, Omar Fabián Ordaz-Díaz, Luis Alberto COVID-19 Lenguaje profundo Redes neuronales secuenciales 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. Haciendo el uso de aprendizaje profundo se busca determinar la probabilidad de que un paciente hospitalizado por COVID-19 padezca insuficiencia respiratoria y precise ser ventilado mecánicamente en una Unidad de Cuidados Intensivos (UCI). El análisis profundo se realiza mediante el entrenamiento del algoritmo de Redes Neuronales Secuenciales, ya que estas presentan una buena eficiencia en el análisis de datos abiertos. Para este estudio se tomó la base de datos abiertos de la Dirección General de Epidemiología. De acuerdo a los decretos oficiales de la federación las bases históricas y la información referente a los casos asociados a COVID-19 son de uso libre con el propósito de facilitar a todos los usuarios que la requieran, el acceso, uso, reutilización y redistribución de la misma. La base de datos de la Dirección General de Epidemiología presenta información varia que de acuerdo a entrevista con un médico de primera línea que trabaja con pacientes de COVID-19 y a su consideración algunos datos pueden ser irrelevantes, tal es el caso de la nacionalidad de los infectados, por mencionar alguno; de igual manera se trabajó solo con aquellos pacientes que dieron positivo a la enfermedad. Así mismo la base de datos puede servir para encontrar algunos otros aspectos o datos estadísticos relevantes sobre la pandemia en México. Universidad Autónoma de Baja California 2021-09-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/html text/xml https://recit.uabc.mx/index.php/revista/article/view/134 10.37636/recit.v43195207 REVISTA DE CIENCIAS TECNOLÓGICAS; Vol. 4 No. 3 (2021): July-September; 195-207 REVISTA DE CIENCIAS TECNOLÓGICAS; Vol. 4 Núm. 3 (2021): Julio-Septiembre; 195-207 2594-1925 spa https://recit.uabc.mx/index.php/revista/article/view/134/291 https://recit.uabc.mx/index.php/revista/article/view/134/292 https://recit.uabc.mx/index.php/revista/article/view/134/293 Copyright (c) 2021 Omar Fabián Rivera Ceniceros, Luis Alberto Ordaz Díaz https://creativecommons.org/licenses/by/4.0
institution RECIT
collection OJS
language spa
format Online
author Rivera-Ceniceros, Omar Fabián
Ordaz-Díaz, Luis Alberto
spellingShingle Rivera-Ceniceros, Omar Fabián
Ordaz-Díaz, Luis Alberto
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
author_facet Rivera-Ceniceros, Omar Fabián
Ordaz-Díaz, Luis Alberto
author_sort Rivera-Ceniceros, Omar Fabián
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
description 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.
publisher Universidad Autónoma de Baja California
publishDate 2021
url https://recit.uabc.mx/index.php/revista/article/view/134
_version_ 1792095341521141760

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