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Related Experiment Video

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Web-Based Visualization of MeSH-Based PubMed/MEDLINE Statistics.

Maria I Restrepo1,2, Mary C McGrath1,2, Indra Neil Sarkar1

  • 1Center for Biomedical Informatics, Brown University, Providence, RI, USA.

Studies in Health Technology and Informatics
|August 24, 2019
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Summary
This summary is machine-generated.

Researchers can now visualize Medical Subject Headings (MeSH) descriptor trends with a new web application. This tool enhances biomedical literature searches by offering interactive frequency and co-occurrence statistics for PubMed queries.

Keywords:
Data DisplayData MiningMedical Subject Headings

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Area of Science:

  • Biomedical Informatics
  • Information Science
  • Bibliometrics

Background:

  • Statistical analysis of Medical Subject Headings (MeSH) descriptors is crucial for optimizing biomedical literature searches.
  • Current visualization tools lack the interactive features needed for researchers to compare hypotheses with literature trends.
  • There is a need for accessible methods to analyze MeSH descriptor usage in relation to specific research queries.

Purpose of the Study:

  • To develop and present a web application for interactive visualization of MeSH descriptor statistics.
  • To provide researchers with tools for analyzing descriptor frequencies and co-occurrences within PubMed search results.
  • To bridge the gap between hypothesis-driven research and the analysis of biomedical literature trends.

Main Methods:

  • The study involved the development of a web application.
  • The application computes basic frequencies and co-occurrence statistics of MeSH descriptors.
  • These statistics are visualized interactively for user exploration.

Main Results:

  • A functional web application was created, offering interactive visualizations.
  • The tool successfully computes and displays MeSH descriptor frequencies.
  • Co-occurrence statistics of MeSH descriptors relevant to PubMed queries are readily available.

Conclusions:

  • The developed web application provides an accessible and interactive platform for analyzing MeSH descriptor data.
  • This tool can significantly improve the process of biomedical literature searching and trend analysis.
  • Researchers can better contextualize their work against the backdrop of published research using these visualizations.