Research hotspot and trend analysis in the diagnosis of inflammatory bowel disease: A machine learning bibliometric analysis from 2012 to 2021

炎症性肠病诊断研究热点及趋势分析:基于机器学习的2012年至2021年文献计量分析

阅读:1

Abstract

AIMS: This study aimed to conduct a bibliometric analysis of the relevant literature on the diagnosis of inflammatory bowel disease (IBD), and show its current status, hot spots, and development trends. METHODS: The literature on IBD diagnosis was acquired from the Science Citation Index Expanded of the Web of Science Core Collection. Co-occurrence and cooperation relationship analysis of authors, institutions, countries, journals, references, and keywords in the literature were carried out through CiteSpace software and the Online Analysis platform of Literature Metrology. At the same time, the relevant knowledge maps were drawn, and the keywords cluster analysis and emergence analysis were performed. RESULTS: 14,742 related articles were included, showing that the number of articles in this field has increased in recent years. The results showed that PEYRIN-BIROULET L from the University Hospital of Nancy-Brabois was the author with the most cumulative number of articles. The institution with the most articles was Mayo Clin, and the United States was far ahead in the article output and had a dominant role. Keywords analysis showed that there was a total of 818 keywords, which were mainly focused on the research of related diseases caused or coexisted by IBD, such as colorectal cancer and autoimmune diseases, and the diagnosis and treatment methods of IBD. Emerging analysis showed that future research hotspots and trends might be the treatment of IBD and precision medicine. CONCLUSION: This research was the first bibliometric analysis of publications in the field of IBD diagnosis using visualization software and data information mining, and obtained the current status, hotspots, and development of this field. The future research hotspot might be the precision medicine of IBD, and the mechanism needed to be explored in depth to provide a theoretical basis for its clinical application.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。