Ozek Burcu, Lu Zhenyuan, Pouromran Fatemeh, Radhakrishnan Srinivasan, Kamarthi Sagar
Mechanical and Industrial Engineering Department, Northeastern University, Boston, Massachusetts, United States of America.
PLOS Digit Health. 2023 Sep 7;2(9):e0000331. doi: 10.1371/journal.pdig.0000331. eCollection 2023 Sep.
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 reviews and analyzes 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.
疼痛是一个重大的公共卫生问题，因为全球有疼痛病史的人数持续增加。作为回应，许多协同研究领域正在汇聚在一起，以解决与疼痛相关的问题。这项工作使用关键词共现网络（KCN）方法对大量与疼痛相关的文献进行综述和分析。在这种方法中，通过将关键词视为节点，关键词的共现视为节点之间的链接，构建一组KCN。由于关键词代表研究文章的知识组成部分，对KCN的分析将揭示文献中的知识结构和研究趋势。本研究从2002年至2021年在IEEE、PubMed、Engineering Village和Web of Science上索引的264,560篇与疼痛相关的研究文章中提取并分析了关键词。我们观察到过去二十年中疼痛文献的快速增长：文章数量增长了近三倍，关键词数量增长了7倍。我们确定了传感器/方法、生物医学和治疗领域中新兴和衰退的研究趋势。我们还提取了最常共现的关键词对和聚类，以帮助研究人员认识到不同疼痛相关主题之间的协同作用。
