用于创建预测模型的派生变量
用于预测冬季儿科呼吸道疾病（RSV/iGAS）医疗需求的派生变量数据集，含远程医疗咳嗽电话和急诊支气管炎就诊等指标。
基本信息
资源简介
用于创建预测模型的派生变量
【数据集背景】
本数据集源站标题为《Derived variables used to create forecast models.》。源站首次发布：2025-01-27。
【数据内容】
源站原始描述：During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022–23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0292829.t001。
【主题与分类】
源站学科分类：Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Infectious Diseases。主题标签：using machine learning、new higher peak、health care systems、enabled prediction intervals、28 days ahead、2022 &# 8211、respiratory syncytial virus、new forecasting pipeline、would best forecast、temperature climates due。
【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/Derived_variables_used_to_create_forecast_models_/28289427
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Morbey, Roger A.; Todkill, Dan; Moura, Phil; Tollinton, Liam; Charlett, Andre; Watson, Conall; et al. (2025). Derived variables used to create forecast models.. 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：Derived variables used to create forecast models.
- DOI：10.1371/journal.pone.0292829.t001
- 发布日期：2025-01-27
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Derived_variables_used_to_create_forecast_models_/28289427
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/51959532"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022–23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model for location, scale and shape (gamlss) approach which enabled prediction intervals to vary according to the level of the forecast activity. The winter of 2022–23 was atypical because the demand for healthcare services in children was exceptionally high, due to RSV circulating in the community and increased concerns around invasive group A streptococcal (iGAS) infections. However, our short-term forecasts proved to be adaptive forecasting a new higher peak once the increasing demand due to iGAS started. Thus, we have demonstrated the utility of our approach, adding forecasts to existing surveillance systems.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0292829.t001（source: https://plos.figshare.com/articles/dataset/Derived_variables_used_to_create_forecast_models_/28289427）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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