Please use this identifier to cite or link to this item:
Title: Aplicación de Deep Neural Network para la predicción de incidencias en COVID-19
Author: Gallegos Serrano, Samuel Paul
Tutor: Sánchez Bocanegra, Carlos Luis
Keywords: research
deep neural networks
machine learning
evidence-based medicine
Issue Date: Jan-2021
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The COVID-19 pandemic is not only causing high mortality in vulnerable populations around the world, it is also exerting considerable stress on health systems with large numbers of cases that must be treated, with intensive care units being the most affected. Taking this panorama into consideration, it is necessary to have systems that allow the prediction of the potential number of infected patients to reinforce institutional needs and effectively attack the disease, providing the best possible care. Some institutions have made efforts to have indicators of the disease, for example, the John Hopkins Hospital has a real-time map fed by multiple sources of information. Internet sites such as Worldometers, shows updated data every day of infected, deaths, recovered and tests carried out on the population. Unfortunately, these types of statistics are a mixture of individuals from different nations, with different comorbidities, with uneven and non-predictive methods of detection or intervention. This work seeks through the Deep Neural Network the predictive analysis of COVID-19 and its comparison with traditional statistical methods.
Language: Spanish
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

Files in This Item:
File Description SizeFormat 
Gallegos_Serrano_TFM_PPT.pdf4,4 MBAdobe PDFThumbnail
CovidItaliaMaster.xlsx20,03 kBMicrosoft Excel XMLView/Open
TFM_Código.R11,35 kBUnknownView/Open
sgallegossTFM0121memoria.pdfMemoria del TFM2,83 MBAdobe PDFThumbnail