Visualization Analysis of Studies on Prediction Models in Field of Traditional Chinese Medicine
10.13422/j.cnki.syfjx.20231614
- VernacularTitle:中医药领域预测模型研究的可视化分析
- Author:
Chengyang JING
1
;
Zeqi DAI
1
;
Xue WU
1
;
Le ZHANG
1
;
Lirong LIANG
2
;
Xing LIAO
1
Author Information
1. Center for Evidence-Based Chinese Medicine， Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences， Beijing 100700， China
2. Beijing Institute of Respiratory Medicine， Beijing Chao-Yang Hospital， Capital Medical University， Beijing 100020， China
- Publication Type:Journal Article
- Keywords:
traditional Chinese medicine;
prediction model;
knowledge mapping;
visualization analysis;
CiteSpace;
VOSviewer
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2024;30(14):209-217
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveBased on knowledge mapping， the studies on prediction models in the field of traditional Chinese medicine （TCM） were visually analyzed， which provided a reference basis for the excavation and evolution of the future research direction by combing the development process and summarizing the research hotspots and dynamic trends. MethodChina National Knowledge Infrastructure and Web of Science Core Collection databases were searched to obtain studies on prediction models in the field of TCM from inception to February 28， 2023. Endnote X20 software was used for document management. Knowledge mapping generated by CiteSpace software and VOSviewer software was used to visually analyze the characteristics of publication， institutional cooperation relationship， author cooperation network， co-citation， and keywords. ResultA total of 264 pieces of Chinese literature and 266 pieces of English literature were included， and the overall number of research publications showed an increasing trend year by year. The cooperation relationship between the issuing institutions showed obvious regional characteristics， with the closest cooperation relationship between the universities of TCM and their affiliated hospitals， as well as secondary units subordinate to scientific research institutions. The number of research teams and team members publishing papers in English was higher， and cooperation between different teams was more frequent. Groundbreaking and/or referential studies were widely cited and referred to. The highly cited literature was mainly published in complementary and alternative medicine journals and pharmaceutical journals. Research hotspots mainly focused on clinical prediction models of TCM， quantitative models of TCM， and specific modeling methods. The application of artificial intelligence technologies such as machine learning and deep learning in the field of TCM will be the most cutting-edge research direction in the future. ConclusionThe field of TCM is paying more and more attention to the studies on prediction models， while the research cooperation mode involving multiple organizations and teams has increasingly become the mainstream. With the continuous development of multi-disciplinary integration， studies on prediction models are closely related to the development and rise of innovative techniques and methods， and any breakthrough in theory or application will induce and guide a new round of research upsurge. Systematic reviews of topic-specific prediction models should be carried out in the future to provide evidence-based evidence.
