使用文本网络分析探索严重COVID-19风险因素的知识结构和趋势
1Department of Nursing, Keimyung College University, Daegu, South Korea.
Studies in health technology and informatics
|January 25, 2024
概括
这项研究使用超过22,000篇论文的文本网络分析绘制了严重的COVID-19风险因素. 关键主题包括生物医学,职业,人口,行为因素和并发症,揭示了严重COVID-19风险研究的时间趋势.
科学领域:
- 公共卫生和流行病学
- 数据科学和网络分析 数据科学和网络分析
- 传染性疾病 传染性疾病
背景情况:
- 严重的COVID-19风险因素是复杂的和多因素的.
- 了解不断变化的知识格局对于有效的公共卫生战略至关重要.
- 以前的研究还没有系统地分析严重的COVID-19风险因素的结构和趋势.
研究的目的:
- 确定严重的COVID-19风险因素的知识结构和新兴趋势.
- 分析不同类别的风险因素之间的关系.
- 提供关于严重COVID-19风险的研究的系统概述.
主要方法:
- 对2020年1月至2021年12月期间发表的22628篇研究论文进行了文字网络分析.
- 使用文本排名分析器和Gephi软件进行分析和可视化.
- 他们将已识别的风险因素分为五个中心主题:生物医学,职业/环境,人口,健康行为和并发症.
主要成果:
- 确定了五个核心主题,包括生物医学,职业/环境,人口,健康行为因素和并发症.
- 揭示了不同风险因素随着时间的推移而出现和聚焦的时间趋势.
- 建立了一个知识结构,说明各种严重的COVID-19风险因素的相互联系.
结论:
- 该研究提供了对严重的COVID-19风险因素复杂格局的系统理解.
- 确定的主题和趋势可以为未来的研究和公共卫生干预提供信息.
- 文本网络分析是绘制知识结构在COVID-19等快速发展的研究领域的有价值的工具.
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