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Title: Toward an intelligent computing solution for endotracheal obstruction prediction in COVID-19 patients in ICU
Authors: Fernández-López, Pablo 
Suárez-Araujo, Carmen Paz 
García Báez, Patricio 
Suárez Díaz, Francisco 
Navarro-Mesa, Juan L. 
Pérez-Acosta, Guillermo
Blanco-López, José
UNESCO Clasification: 3314 Tecnología médica
Keywords: Covid-19
Deep Learning
Endotracheal Obstruction
LSTM, et al
Issue Date: 2021
Publisher: Springer 
Project: Aplicación de técnicas de machine learning para la detección temprana de obstrucción del tubo endotracheal en pacientes COVID-19 en UCI
Journal: Lecture Notes in Computer Science 
Conference: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021 
Abstract: Nowadays there is a world pandemic of a challenging respiratory illness, COVID-19. A large part of COVID-19 patients evolves to severe or fatal complications and require an ICU admission. COVID-19 mortality rate approaches 30% due to complications such as obstruction of the trachea and bronchi of patients during the ICU stay. An endotracheal obstruction occurring during any moment in a COVID-19 patient ICU stay is one of the most complicated situations that clinicians must face and solve. Therefore, it is very important to know in advance when a COVID-19 patient could enter in the pre-obstruction zone. In this work we present an intelligent computing solution to predict endotracheal obstruction for COVID-19 patients in ICU. It is called the Binomial Gate LSTM (BigLSTM), a new and innovative deep modular neural architecture based on the recurrent neural network LSTM. Its main feature is its ability to handle missing data and to deal with time series with no regular sample frequency. These are the main characteristics of the BigLSTM information environment. This ability is implemented in BigLSTM by an information redundancy injection mechanism and how it copes with time control. We applied BigLSTM with first wave COVID-19 patients in ICU of Complejo Hospitalario Universitario Insular Materno Infantil. Encouraging results, even while working with a very small data set, indicate that our developed computing solution is going forwards towards an efficient intelligent prediction system which is very appropriate for this kind of problem.
ISBN: 978-3-030-85029-6
ISSN: 0302-9743
DOI: 10.1007/978-3-030-85030-2_6
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 12861 LNCS, p. 61-73, (Enero 2021)
Appears in Collections:Actas de congresos
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