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Mesh Analysis01:20

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Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
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How to catch trends using MeSH terms analysis?

Ekaterina V Ilgisonis1, Mikhail A Pyatnitskiy1, Svetlana N Tarbeeva1

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This study introduces a method to analyze PubMed publications by comparing keyword frequencies (MeSH terms). It identifies research trends and shifts in scientific priorities, aiding in predicting future research relevance.

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Area of Science:

  • Bibliometrics and scientometrics
  • Medical informatics
  • Research trend analysis

Background:

  • Comparative analysis of scientific publications is crucial for understanding research landscapes.
  • PubMed is a primary database for biomedical literature, containing millions of abstracts.

Purpose of the Study:

  • To develop and test a method for comparative analysis of publication sets using MeSH term frequencies.
  • To identify MeSH terms characterizing specific research areas and detect emerging trends.
  • To analyze shifts in scientific priorities within medicine and personalized medicine over time.

Main Methods:

  • Comparative analysis of MeSH term frequencies in PubMed abstracts.
  • Analysis of approximately 700,000 abstracts from 2009-2021.
  • Retrospective keyword frequency analysis to identify temporal trends.

Main Results:

  • Identified MeSH terms specific to general medicine and personalized medicine research.
  • Detected topics with significantly increased research interest in recent years.
  • Revealed a shift in scientific priorities over the past decade.

Conclusions:

  • The proposed method effectively identifies research-specific terms and trends.
  • The analysis provides insights into evolving scientific priorities.
  • Findings can inform predictions of research relevance for the next 3-5 years.
  • The method is scalable and adaptable for future analyses.