{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:31:22Z","timestamp":1771612282502,"version":"3.50.1"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/frai.2023.1290022","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T11:33:56Z","timestamp":1702035236000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil"],"prefix":"10.3389","volume":"6","author":[{"given":"Tiago de Oliveira","family":"Barreto","sequence":"first","affiliation":[]},{"given":"N\u00edcolas Vin\u00edcius Rodrigues","family":"Veras","sequence":"additional","affiliation":[]},{"given":"Pablo Holanda","family":"Cardoso","sequence":"additional","affiliation":[]},{"given":"Felipe Ricardo dos Santos","family":"Fernandes","sequence":"additional","affiliation":[]},{"given":"Luiz Paulo de Souza","family":"Medeiros","sequence":"additional","affiliation":[]},{"given":"Maria Val\u00e9ria","family":"Bezerra","sequence":"additional","affiliation":[]},{"given":"Filomena Marques Queiroz de","family":"Andrade","sequence":"additional","affiliation":[]},{"given":"Chander de Oliveira","family":"Pinheiro","sequence":"additional","affiliation":[]},{"given":"Ignacio","family":"S\u00e1nchez-Gendriz","sequence":"additional","affiliation":[]},{"given":"Gleyson Jos\u00e9 Pinheiro Caldeira","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Leandro Farias","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Antonio Higor Freire de","family":"Morais","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o Paulo Queiroz","family":"dos Santos","sequence":"additional","affiliation":[]},{"given":"Jailton Carlos","family":"Paiva","sequence":"additional","affiliation":[]},{"given":"Ion Garcia Mascarenhas de","family":"Andrade","sequence":"additional","affiliation":[]},{"given":"Ricardo Alexsandro de Medeiros","family":"Valentim","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.3390\/sym12091526","article-title":"Deep mlp-cnn model using mixed-data to distinguish between COVID-19 and non-COVID-19 patients","volume":"12","author":"Ahsan","year":"2020","journal-title":"Symmetry"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/app12188939","article-title":"Towards machine learning algorithms in predicting the clinical evolution of patients diagnosed with covid-19","author":"Andrade","year":"2022","journal-title":"Appl. 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