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.
Highlights
Medicine is a systematic discipline from the prevention to the treatment of disease, be divided into many different research directions and research fields (Iserson and Moskop 2007)
(3) After confirming the label results’ consistency, we divided the rest of included articles into four groups and each group was in charge by one experienced medical worker; (4) If there were several different disease themes existed in one article, they would try to classify each article into two or fewer main disease themes; if some articles clearly indicate more than two main disease themes, they would discuss with others to sum up into two or fewer themes, more than two disease themes were allowed sometimes if the agreement could be reached after discussion
We introduced a strategic diagram to present the current hotspots and trends of disease themes based on medical data science and to predict future developments
Summary
Medicine is a systematic discipline from the prevention to the treatment of disease, be divided into many different research directions and research fields (Iserson and Moskop 2007). Medical scholars had adopted the bibliometric method to find theme trends and knowledge structures in different medical fields. Zhang visualized knowledge domain of patient adherence by co-word analysis and social network analysis (Zhang et al 2012); Fu utilized bibliometric analysis to research malaria in China during 2004 to 2014 (Fu et al 2015); Gu, Hsu, and Liao all visualized the big data research in medicine (Gu et al 2017; Hsu and Li 2019; Liao et al 2018); Huang analyzed the pelvic organ prolapse during 2007 to 2016 by bibliometric and social network analysis (Huang et al 2018); Jin and Xin visualized the hotspots and trends of multimedia big data (Jin and Li 2018); Zhao used co-word analysis to study the theme trends and knowledge structure on choroidal neovascularization (Zhao et al 2018); Saheb adopted bibliometric analysis to exploring IoT big data analytics in the healthcare industry (Saheb and Izadi 2019); Wang applied co-word analysis to investigate the professional-patient relations in the Internet era (Wang et al 2019); and Yang used the same method to identify the trends in research on Vitamin D (Yang et al 2019)
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