Name
Abstract | ||
|---|---|---|
By employing bibliometric method, this study aimed to visualize the research hotspots and correlations among clinical medicine subjects. Literatures were retrieved from the PubMed database based on MeSH words and free-text phrases and screened based on inclusion and exclusion criteria. The disease themes were manually marked according to ICD-10. Co-word analysis and strategic diagram methods were applied to explore the hot topics and development trends of disease themes. 2551 articles were included after literature screening. The amount of paper showed an increasing trend and reached a peak in 2013. The subjects of adults and the elderly accounted for 45.0% and 27.0% respectively. The United States of America had the most publication, with Massachusetts and California being the most prevalent states, and Harvard University was the most prolific institution. Co-word analysis revealed that research hot topics of diseases were divided into 8 themes, among which the most was "disease of the circulatory system" and "injury, poisoning and certain other consequences of external causes". The strategic diagram showed that the above two topics were mature but relatively independent, while the "physical fitness" topic was not mature enough but was closely related to the others. There are more and more data-driven studies in the field of medicine and health, while, huge development spaces in the full spectrum of the diseases do exist. Mining the published researches through bibliometrics and visualized methods could come up with valuable results to inform further study. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11192-020-03646-8 | SCIENTOMETRICS |
Keywords | DocType | Volume |
Medical data science,Clinical medicine research,Bibliometrics,Co-word analysis,Strategic diagram,Visualization | Journal | 125.0 |
Issue | ISSN | Citations |
1.0 | 0138-9130 | 1 |
PageRank | References | Authors |
0.35 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilong Chen | 1 | 1 | 0.35 |
| Yiting Dong | 2 | 1 | 0.35 |
| Yu Zeng | 3 | 1 | 0.35 |
| Xiaoyan Yang | 4 | 1 | 0.35 |
| Jiantong Shen | 5 | 3 | 0.72 |
| Lang Zheng | 6 | 1 | 0.35 |
| Jingwen Jiang | 7 | 1 | 0.35 |
| Liming Pu | 8 | 1 | 0.35 |
| Qilin Bao | 9 | 1 | 0.35 |