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How to catch trends using MeSH terms analysis?

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 e...

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Autores principales: Ilgisonis, Ekaterina V., Pyatnitskiy, Mikhail A., Tarbeeva, Svetlana N., Aldushin, Artem A., Ponomarenko, Elena A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859845/
https://www.ncbi.nlm.nih.gov/pubmed/35221395
http://dx.doi.org/10.1007/s11192-022-04292-y
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author Ilgisonis, Ekaterina V.
Pyatnitskiy, Mikhail A.
Tarbeeva, Svetlana N.
Aldushin, Artem A.
Ponomarenko, Elena A.
author_facet Ilgisonis, Ekaterina V.
Pyatnitskiy, Mikhail A.
Tarbeeva, Svetlana N.
Aldushin, Artem A.
Ponomarenko, Elena A.
author_sort Ilgisonis, Ekaterina V.
collection PubMed
description 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11192-022-04292-y.
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spelling pubmed-88598452022-02-22 How to catch trends using MeSH terms analysis? Ilgisonis, Ekaterina V. Pyatnitskiy, Mikhail A. Tarbeeva, Svetlana N. Aldushin, Artem A. Ponomarenko, Elena A. Scientometrics Article 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11192-022-04292-y. Springer International Publishing 2022-02-21 2022 /pmc/articles/PMC8859845/ /pubmed/35221395 http://dx.doi.org/10.1007/s11192-022-04292-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ilgisonis, Ekaterina V.
Pyatnitskiy, Mikhail A.
Tarbeeva, Svetlana N.
Aldushin, Artem A.
Ponomarenko, Elena A.
How to catch trends using MeSH terms analysis?
title How to catch trends using MeSH terms analysis?
title_full How to catch trends using MeSH terms analysis?
title_fullStr How to catch trends using MeSH terms analysis?
title_full_unstemmed How to catch trends using MeSH terms analysis?
title_short How to catch trends using MeSH terms analysis?
title_sort how to catch trends using mesh terms analysis?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859845/
https://www.ncbi.nlm.nih.gov/pubmed/35221395
http://dx.doi.org/10.1007/s11192-022-04292-y
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