RegulaRN Leitos Gerais 数据概况
基于巴西RegulaRN Leitos Gerais平台的床位调控记录，涵盖COVID-19等多种住院疾病，共47,056条数据，用于二分类预测出院或死亡，辅助医疗决策。
基本信息
资源简介
RegulaRN Leitos Gerais 数据概况
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 17.92 KB，主题涵盖 national health system、information system developed、gradient boosting models、artificial intelligence applied、sgd optimizer obtained，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Data profile from the RegulaRN Leitos Gerais.》。配套论文《Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the “RegulaRN Leitos Gerais” platform》发表于 PLOS ONE（2024），DOI：10.1371/journal.pone.0315379。源站首次发布：2024-12-30。
【数据内容】
源站原始描述：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 J…
【数据结构与技术规格】
文件数 1 个，合计 17.92 KB。Table 3.xls：application/vnd.ms-excel，17.92 KB。数据集 DOI：10.1371/journal.pone.0315379.t003。
【主题与分类】
源站学科分类：Cell Biology、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：national health system、information system developed、gradient boosting models、artificial intelligence applied、sgd optimizer obtained、machine learning models、machine learning、data obtained、xgboost model、waiting time。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Data_profile_from_the_RegulaRN_Leitos_Gerais_/28108903
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：de Oliveira Barreto, Tiago; de Oliveira Farias, Fernando Lucas; Veras, Nicolas Vinícius Rodrigues; Cardoso, Pablo Holanda; Silva, Gleyson José Pinheiro Caldeira; de Oliveira Pinheiro, Chander; et al. (2024). Data profile from the RegulaRN Leitos Gerais.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Data profile from the RegulaRN Leitos Gerais.
- DOI：10.1371/journal.pone.0315379.t003
- 发布日期：2024-12-30
- 文件数：1 个
- 文件总体积：17.92 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Data_profile_from_the_RegulaRN_Leitos_Gerais_/28108903
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/51419201"
文件清单
Table 3.xls（17.92 KB）
文件总体积 17.92 KB（源站 API 实测）。
数据说明
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.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0315379.t003（source: https://plos.figshare.com/articles/dataset/Data_profile_from_the_RegulaRN_Leitos_Gerais_/28108903）。
数据缺失？
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