Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform.
Bed regulation within Brazil's National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-1...
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| Format: | Article |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315379 |
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| author | Tiago de Oliveira Barreto Fernando Lucas de Oliveira Farias Nicolas Vinícius Rodrigues Veras Pablo Holanda Cardoso Gleyson José Pinheiro Caldeira Silva Chander de Oliveira Pinheiro Maria Valéria Bezerra Medina Felipe Ricardo Dos Santos Fernandes Ingridy Marina Pierre Barbalho Lyane Ramalho Cortez João Paulo Queiroz Dos Santos Antonio Higor Freire de Morais Gustavo Fontoura de Souza Guilherme Medeiros Machado Márcia Jacyntha Nunes Rodrigues Lucena Ricardo Alexsandro de Medeiros Valentim |
| author_facet | Tiago de Oliveira Barreto Fernando Lucas de Oliveira Farias Nicolas Vinícius Rodrigues Veras Pablo Holanda Cardoso Gleyson José Pinheiro Caldeira Silva Chander de Oliveira Pinheiro Maria Valéria Bezerra Medina Felipe Ricardo Dos Santos Fernandes Ingridy Marina Pierre Barbalho Lyane Ramalho Cortez João Paulo Queiroz Dos Santos Antonio Higor Freire de Morais Gustavo Fontoura de Souza Guilherme Medeiros Machado Márcia Jacyntha Nunes Rodrigues Lucena Ricardo Alexsandro de Medeiros Valentim |
| author_sort | Tiago de Oliveira Barreto |
| collection | DOAJ |
| description | Bed regulation within Brazil's National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-19 cases. However, the platform was expanded to cover a range of diseases that require hospitalization. This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. From the data obtained, 12 features were selected from the 24 available. After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients. |
| format | Article |
| id | doaj-art-6c8ff82c980e46f1ba29b60774ecdfd1 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-6c8ff82c980e46f1ba29b60774ecdfd12025-01-17T05:31:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031537910.1371/journal.pone.0315379Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform.Tiago de Oliveira BarretoFernando Lucas de Oliveira FariasNicolas Vinícius Rodrigues VerasPablo Holanda CardosoGleyson José Pinheiro Caldeira SilvaChander de Oliveira PinheiroMaria Valéria Bezerra MedinaFelipe Ricardo Dos Santos FernandesIngridy Marina Pierre BarbalhoLyane Ramalho CortezJoão Paulo Queiroz Dos SantosAntonio Higor Freire de MoraisGustavo Fontoura de SouzaGuilherme Medeiros MachadoMárcia Jacyntha Nunes Rodrigues LucenaRicardo Alexsandro de Medeiros ValentimBed regulation within Brazil's National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-19 cases. However, the platform was expanded to cover a range of diseases that require hospitalization. This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. From the data obtained, 12 features were selected from the 24 available. After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.https://doi.org/10.1371/journal.pone.0315379 |
| spellingShingle | Tiago de Oliveira Barreto Fernando Lucas de Oliveira Farias Nicolas Vinícius Rodrigues Veras Pablo Holanda Cardoso Gleyson José Pinheiro Caldeira Silva Chander de Oliveira Pinheiro Maria Valéria Bezerra Medina Felipe Ricardo Dos Santos Fernandes Ingridy Marina Pierre Barbalho Lyane Ramalho Cortez João Paulo Queiroz Dos Santos Antonio Higor Freire de Morais Gustavo Fontoura de Souza Guilherme Medeiros Machado Márcia Jacyntha Nunes Rodrigues Lucena Ricardo Alexsandro de Medeiros Valentim Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. PLoS ONE |
| title | Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. |
| title_full | Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. |
| title_fullStr | Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. |
| title_full_unstemmed | Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. |
| title_short | Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. |
| title_sort | artificial intelligence applied to bed regulation in rio grande do norte data analysis and application of machine learning on the regularn leitos gerais platform |
| url | https://doi.org/10.1371/journal.pone.0315379 |
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