Applying Neural Networks to Recover Values of Monitoring Parameters for COVID-19 Patients in the ICU

Sergio Celada-Bernal (), Guillermo Pérez-Acosta, Carlos M. Travieso-González, José Blanco-López and Luciano Santana-Cabrera
Additional contact information
Sergio Celada-Bernal: Signals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria, Campus de Tafira, E-35017 Las Palmas de Gran Canaria, Spain
Guillermo Pérez-Acosta: Intensive Care Unit, Complejo Hospitalario Universitario Insular-Materno Infantil, Avenida Marítima del Sur s/n, E-35016 Las Palmas de Gran Canaria, Spain
Carlos M. Travieso-González: Signals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria, Campus de Tafira, E-35017 Las Palmas de Gran Canaria, Spain
José Blanco-López: Intensive Care Unit, Complejo Hospitalario Universitario Insular-Materno Infantil, Avenida Marítima del Sur s/n, E-35016 Las Palmas de Gran Canaria, Spain
Luciano Santana-Cabrera: Intensive Care Unit, Complejo Hospitalario Universitario Insular-Materno Infantil, Avenida Marítima del Sur s/n, E-35016 Las Palmas de Gran Canaria, Spain

Mathematics, 2023, vol. 11, issue 15, 1-19

Abstract: From the moment a patient is admitted to the hospital, monitoring begins, and specific information is collected. The continuous flow of parameters, including clinical and analytical data, serves as a significant source of information. However, there are situations in which not all values from medical tests can be obtained. This paper aims to predict the medical test values of COVID-19 patients in the intensive care unit (ICU). By retrieving the missing medical test values, the model provides healthcare professionals with an additional tool and more information with which to combat COVID-19. The proposed approach utilizes a customizable deep learning model. Three types of neural networks, namely Multilayer Perceptron (MLP), Long/Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), are employed. The parameters of these neural networks are configured to determine the model that delivers the optimal performance. Evaluation of the model’s performance is conducted using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The application of the proposed model achieves predictions of the retrieved medical test values, resulting in RMSE = 7.237, MAPE = 5.572, and MAE = 4.791. Moreover, the article explores various scenarios in which the model exhibits higher accuracy. This model can be adapted and utilized in the diagnosis of future infectious diseases that share characteristics with Coronavirus Disease 2019 (COVID-19).

Keywords: COVID-19; data recovery; ICU; patients; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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