Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.

ABSTRACT:

Background

In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic.

Methods

This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter s

SUBMITTER: Villegas M 

PROVIDER: S-EPMC9798667 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature