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. 2023 Dec 8:6:1290022.
doi: 10.3389/frai.2023.1290022. eCollection 2023.

Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil

Tiago de Oliveira Barreto  1 , Nícolas Vinícius Rodrigues Veras  1   2 , Pablo Holanda Cardoso  1   2 , Felipe Ricardo Dos Santos Fernandes  1 , Luiz Paulo de Souza Medeiros  3 , Maria Valéria Bezerra  4 , Filomena Marques Queiroz de Andrade  4 , Chander de Oliveira Pinheiro  4 , Ignacio Sánchez-Gendriz  1 , Gleyson José Pinheiro Caldeira Silva  1   2 , Leandro Farias Rodrigues  5 , Antonio Higor Freire de Morais  1   2 , João Paulo Queiroz Dos Santos  1   2 , Jailton Carlos Paiva  1   2 , Ion Garcia Mascarenhas de Andrade  1 , Ricardo Alexsandro de Medeiros Valentim  1
Affiliations

Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil

Tiago de Oliveira Barreto et al. Front Artif Intell. .

Abstract

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.

Keywords: COVID-19; RegulaRN; bed regulation; computational methods; deep learning; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Pipeline for the use of RegulaRN data.
Figure 2
Figure 2
Structure of the confusion matrix.
Figure 3
Figure 3
Boxplot representation of numerical data.
Figure 4
Figure 4
Correlation between dataset features. (A) Correlations between the characteristics of the dataset involving discharge and death results. (B) Correlations between the characteristics of the dataset involving only the discharge results. (C) Correlations between the characteristics of the dataset involving only the death results.
Figure 5
Figure 5
Features importances of machine learning models. The figure initially shows decision tree and then Random Forest.
Figure 6
Figure 6
Features importances of multiperceptron layers models (deep learning). The figure shows the SGD optimizer, followed by Adam, RMSProp, and Adagrad.
Figure 7
Figure 7
ROC curve and AUC value of all models.
See this image and copyright information in PMC

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