Machine Learning Research Trends in Traditional Chinese Medicine: A Bibliometric Review.

IF 2.1 4区医学 Q2 MEDICINE, GENERAL & INTERNAL

International Journal of General Medicine Pub Date : 2024-11-19 eCollection Date: 2024-01-01 DOI:10.2147/IJGM.S495663

Jiekee Lim, Jieyun Li, Mi Zhou, Xinang Xiao, Zhaoxia Xu

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引用次数: 0

Abstract

Background: Integrating Traditional Chinese Medicine (TCM) knowledge with modern technology, especially machine learning (ML), has shown immense potential in enhancing TCM diagnostics and treatment. This study aims to systematically review and analyze the trends and developments in ML applications in TCM through a bibliometric analysis.

Methods: Data for this study were sourced from the Web of Science Core Collection. Data were analyzed and visualized using Microsoft Office Excel, Bibliometrix, and VOSviewer.

Results: 474 documents were identified. The analysis revealed a significant increase in research output from 2000 to 2023, with China leading in both the number of publications and research impact. Key research institutions include the Shanghai University of Traditional Chinese Medicine and the China Academy of Chinese Medical Sciences. Major research hotspots identified include ML applications in TCM diagnosis, network pharmacology, and tongue diagnosis. Additionally, chemometrics with ML are highlighted for their roles in quality control and authentication of TCM products.

Conclusion: This study provides a comprehensive overview of ML applications' development trends and research landscape in TCM. The integration of ML has led to significant advancements in TCM diagnostics, personalized medicine, and quality control, paving the way for the modernization and internationalization of TCM practices. Future research should focus on improving model interpretability, fostering international collaborations, and standardized reporting protocols.

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中医药中的机器学习研究趋势：文献计量学综述。

背景：将传统中医（TCM）知识与现代技术，特别是机器学习（ML）相结合，在提高中医诊断和治疗方面显示出巨大的潜力。本研究旨在通过文献计量学分析，系统回顾和分析 ML 在中医药领域应用的趋势和发展：本研究的数据来源于 Web of Science Core Collection。数据使用 Microsoft Office Excel、Bibliometrix 和 VOSviewer 进行分析和可视化：确定了 474 篇文献。分析表明，从 2000 年到 2023 年，中国的研究成果大幅增加，在论文数量和研究影响力方面均处于领先地位。主要研究机构包括上海中医药大学和中国中医科学院。主要研究热点包括中医诊断、网络药理学和舌诊中的 ML 应用。此外，研究还强调了化学计量学与 ML 在中药产品质量控制和鉴定中的作用：本研究全面概述了 ML 在中医药领域的应用发展趋势和研究现状。ML 的集成在中医诊断、个性化医疗和质量控制方面取得了重大进展，为中医药实践的现代化和国际化铺平了道路。未来的研究应侧重于提高模型的可解释性、促进国际合作和标准化报告协议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of General Medicine Medicine-General Medicine

自引率

0.00%

发文量

1113

审稿时长

16 weeks

期刊介绍： The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas. A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal. As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.