Li Qun, Chen Guolin, Li Qiongjie, Guo Dongna
School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China.
Department of Emergency, First Hospital of Shanxi Medical University, Taiyuan, China.
Front Med (Lausanne). 2024 Nov 18;11:1490757. doi: 10.3389/fmed.2024.1490757. eCollection 2024.
In the past decade, the application of machine learning (ML) in the clinical management of acute upper gastrointestinal bleeding (AUGIB) has received much attention and has become a hot research topic. However, no scientometric report has systematically summarized and outlined the research progress in this field.
This study aims to utilize bibliometric analysis methods to delve into the applications of machine learning in AUGIB and the collaborative network behind it over the past decade. Through a thorough analysis of relevant literature, we uncover the research trends and collaboration patterns in this field, which can provide valuable references and insights for further in-depth exploration in the same field.
Using the Web of Science (WOS) as the data source, this study explores academic development in a specific field from December 2013 to December 2023. The search strategy included terms related to "Machine Learning" and "Acute Upper Gastrointestinal Bleeding". Only original articles in English focusing on ML in AUGIB were included. The analysis of downloaded literature with Citespace software, including keyword co-occurrence, author collaboration networks, and citation relationship networks, reveals academic dynamics, research hotspots, and collaboration trends.
After sorting and compiling, we have collected 73 academic papers written by 217 authors from 133 institutions in 29 countries worldwide. Among them, China and AM J GASTROENTEROL have made significant contributions in this field, providing many high-quality research achievements. The study found that these papers mainly focus on three core research hotspots: deepening clinical consensus, precise analysis of medical images, and optimization of data integration and decision support systems.
This study summarizes the latest advancements in the application of machine learning to AUGIB research. Through bibliometric analysis and network visualization, it reveals emerging trends, origins, leading institutions, and hot topics in this field. While this area has already demonstrated significant potential in medical artificial intelligence, our findings will provide valuable insights for future research directions and clinical practices.
在过去十年中，机器学习（ML）在急性上消化道出血（AUGIB）临床管理中的应用备受关注，已成为一个热门研究话题。然而，尚无科学计量学报告系统总结和概述该领域的研究进展。
本研究旨在利用文献计量分析方法，深入探究过去十年机器学习在AUGIB中的应用及其背后的合作网络。通过对相关文献的全面分析，揭示该领域的研究趋势和合作模式，为同一领域的进一步深入探索提供有价值的参考和见解。
本研究以Web of Science（WOS）为数据源，探索2013年12月至2023年12月特定领域的学术发展。搜索策略包括与“机器学习”和“急性上消化道出血”相关的术语。仅纳入专注于AUGIB中ML的英文原创文章。使用Citespace软件对下载的文献进行分析，包括关键词共现、作者合作网络和引文关系网络，揭示学术动态、研究热点和合作趋势。
经过整理，我们收集了来自全球29个国家133个机构的217位作者撰写的73篇学术论文。其中，中国和《美国胃肠病学杂志》在该领域做出了重大贡献，提供了许多高质量的研究成果。研究发现，这些论文主要集中在三个核心研究热点：深化临床共识、医学图像的精确分析以及数据集成和决策支持系统的优化。
本研究总结了机器学习应用于AUGIB研究的最新进展。通过文献计量分析和网络可视化，揭示了该领域的新兴趋势、起源、领先机构和热点话题。虽然该领域在医学人工智能方面已经展现出巨大潜力，但我们的研究结果将为未来的研究方向和临床实践提供有价值的见解。
