一个比较数据集:通过表观学和CORD-19证据弥合COVID-19和其他疾病
Andrés Carvallo1, Denis Parra2, Hans Lobel2
1Centro Nacional de Inteligencia Artificial, Vicuña Mackenna 4686, Macul, Santiago, Región Metropolitana, Chile.
Data in brief
|November 15, 2023
概括
一个新的20 047份COVID-19文件数据集通过对研究类型进行分类来帮助基于证据的医学 (EBM). 这种精心策划的收藏支持在流行病期间可靠的临床决策和公共卫生政策.
科学领域:
- 生物医学信息学 生物医学信息学
- 公共卫生 公共卫生
- 信息科学 信息科学 信息科学
背景情况:
- 随着COVID-19的爆发,人们越来越需要准确的信息来指导临床决策和公共卫生战略.
- 基于证据的医学 (EBM) 依赖于有效识别和评估科学文献,特别是在新出现的疾病方面.
- 准确的生物医学文本分类是EBM过程的基础.
研究的目的:
- 引入一个全面的,精心策划的COVID-19相关文档数据集,以支持基于证据的医学.
- 为对新型疾病相关的生物医学文本进行分类提供一个有价值的资源.
- 为未来的证据综合和信息检索研究提供一个基准.
主要方法:
- 一个包含20 047份COVID-19文件的数据集被细致地分为五个类别:系统审查 (SR),初级研究随机对照试验 (PS-RCT),初级研究非随机对照试验 (PS-NRCT),广泛合成 (BS) 和排除 (EXC).
- 收集了包括类型,标题,摘要和元数据 (PubMed ID,作者,期刊,出版日期) 在内的文档细节.
- 作为一个基准,还编制了427,870个非COVID-19文件的额外存储库,也被分类为类别.
主要成果:
- 策划的数据集包含20 047个标记为COVID-19的文件,分类为EBM应用程序.
- 有一个大型的427,870个非COVID-19文件存储库可用于比较分析和基准测试.
- 数据集是独一无二的,由Epistemonikos基金会策划,并且无法通过标准的网络抓取来访问.
结论:
- 这一开放访问数据集通过为COVID-19研究提供结构化资源,显著推进了基于证据的医学.
- 分类的COVID-19和非COVID-19文件的可用性有助于进一步研究证据合成和生物医学文本分类.
- 该数据集有望提高临床决策和公共卫生政策制定方面的信息可靠性.
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