Barreto Tiago de Oliveira, Farias Fernando Lucas de Oliveira, Veras Nicolas Vinícius Rodrigues, Cardoso Pablo Holanda, Silva Gleyson José Pinheiro Caldeira, Pinheiro Chander de Oliveira, Medina Maria Valéria Bezerra, Fernandes Felipe Ricardo Dos Santos, Barbalho Ingridy Marina Pierre, Cortez Lyane Ramalho, Santos João Paulo Queiroz Dos, Morais Antonio Higor Freire de, Souza Gustavo Fontoura de, Machado Guilherme Medeiros, Lucena Márcia Jacyntha Nunes Rodrigues, Valentim Ricardo Alexsandro de Medeiros
Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil.
Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil.
PLoS One. 2024 Dec 30;19(12):e0315379. doi: 10.1371/journal.pone.0315379. eCollection 2024.
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.
巴西国家卫生系统（SUS）中的床位调配在管理需要住院治疗的患者护理方面发挥着关键作用。在巴西的北里奥格兰德州，RegulaRN Leitos Gerais平台是为登记COVID-19病例的床位调配请求而开发的信息系统。然而，该平台已扩展到涵盖一系列需要住院治疗的疾病。本研究在RegulaRN数据库中探索了不同的机器学习模型，时间跨度为2021年10月至2024年1月，共有47056次调配记录。从获得的数据中，从24个可用特征中选择了12个。之后，去除了空白和不确定的数据，以及除出院和死亡以外有其他值的结果，进行二元分类。数据还进行了相关性分析、平衡处理，并分为训练和测试部分，以应用于机器学习模型。结果显示，XGBoost模型的准确率（87.77%）和召回率（87.77%）更高，随机森林模型和梯度提升模型的精确率（分别为87.85%）和F1分数（87.56%）更高。至于特异性（82.94%）和ROC-AUC（82.13%），使用SGD优化器的多层感知器得分最高。结果证明了哪些模型可以在床位调配的决策过程中充分协助医疗调配人员，实现更有效的调配，从而增加床位供应，减少患者等待时间。
