Metrics obtained by the computer models.

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（Sistema Único de Saúde, SUS）的床位调配工作，在统筹住院患者的诊疗照护工作中发挥着至关重要的作用。在巴西北里奥格兰德州，RegulaRN Leitos Gerais平台曾是专为新冠病毒感染病例开发的床位调配申请登记信息系统，后续该平台被扩容，可覆盖各类需住院治疗的疾病。本研究针对2021年10月至2024年1月期间RegulaRN数据库内的47056条床位调配记录，开展了不同机器学习模型的探索性分析。从获取的原始数据中，研究人员从24项可用特征里筛选出12项特征；随后剔除了空白数据、无明确结论的数据，以及除出院与死亡之外的其他结局数据，最终构建二分类分类任务。此外，研究人员还对数据进行了相关性处理、平衡化处理，并划分为训练集与测试集，以供机器学习模型应用。研究结果显示，XGBoost模型的准确率（87.77%）与召回率（87.77%）表现最优；随机森林（Random Forest）与梯度提升树（Gradient Boosting）模型则分别取得了更高的精确率（87.85%）与F1值（87.56%）。在特异度（Specificity，82.94%）与ROC曲线下面积（ROC-AUC，82.13%）指标上，采用随机梯度下降（Stochastic Gradient Descent, SGD）优化器的多层感知机（Multilayer Perceptron）取得了最高得分。本研究结果明确了可在床位调配决策流程中为医疗调配人员提供有效辅助的模型，有助于实现更高效的床位调配，进而提升床位可用率并缩短患者等待时长。