How to catch trends using MeSH terms analysis?

Ekaterina V. Ilgisonis (), Mikhail A. Pyatnitskiy (), Svetlana N. Tarbeeva (), Artem A. Aldushin () and Elena A. Ponomarenko ()
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Ekaterina V. Ilgisonis: Institute of Biomedical Chemistry
Mikhail A. Pyatnitskiy: Institute of Biomedical Chemistry
Svetlana N. Tarbeeva: Institute of Biomedical Chemistry
Artem A. Aldushin: A.S. Puchkov Station of Emergency Medical Assistance
Elena A. Ponomarenko: Institute of Biomedical Chemistry

Scientometrics, 2022, vol. 127, issue 4, No 14, 1953-1967

Abstract: Abstract The paper describes a scheme for the comparative analysis of the sets of Pubmed publications. The proposed analysis is based on the comparison of the frequencies of occurrence of keywords—MeSH terms. The purpose of the analysis is to identify MeSH terms that characterize research areas specific to each group of articles, as well as to identify trends—topics on which the number of published works has changed significantly in recent years. The proposed approach was tested by comparing a set of medical publications and a group of articles in the field of personalized medicine. We analyzed about 700 thousand abstracts published in the period 2009–2021 and indexed them with MeSH terms. Topics with increasing research interest have been identified both in the field of medicine in general and specific to personalized medicine. Retrospective analysis of the keywords frequency of occurrence changes has shown the shift of the scientific priorities in this area over the past 10 years. The revealed patterns can be used to predict the relevance and significance of the scientific work direction in the horizon of 3–5 years. The proposed analysis can be scaled in the future for a larger number of groups of publications, as well as adjusted by introducing filters at the stage of sampling (scientific centers, journals, availability of full texts, etc.) or selecting a list of keywords (frequency threshold, use of qualifiers, category of generalizations).

Keywords: Text-mining; PubMed; MEDLINE; MeSH; Trends; Personalized medicine; Precision medicine; Automatic text analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-022-04292-y

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