论文标题
通过关键字共同出现网络对疼痛研究文献进行审查和分析
Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks
论文作者
论文摘要
疼痛是一个重大的公共卫生问题，因为全球疼痛病史的个体数量不断增长。作为回应，许多协同研究领域已经聚集在一起，以解决与疼痛有关的问题。这项工作使用关键字共发生网络（KCN）方法对大量与疼痛有关的文献进行了综述和分析。在这种方法中，通过将关键字视为节点和关键字的共发生作为节点之间的链接来构建一组KCN。由于关键字代表研究文章的知识组成部分，因此对KCN的分析将揭示文献中的知识结构和研究趋势。这项研究从2002年至2021年之间发表的IEEE，PubMed，Engineering Village和Web of Science索引的264,560个与疼痛相关的研究文章提取和分析了关键字。我们观察到了过去的两十年中疼痛文献的快速增长：近三个曲目的数量已经增长了近三种，并且通过Quighords的数量增长了一个Questions的数量。生物医学和治疗轨道。我们还提取了最常见的共同存在的关键字对和簇，以帮助研究人员认识到不同疼痛相关的主题之间的协同作用。
Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work conducts a review and analysis of a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.
