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A Biopython-based method for comprehensively searching for eponyms in Pubmed.

Toby C Cornish1, Larry J Kricka2, Jason Y Park3

  • 1Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA.

Methodsx
|August 26, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a Python script to automate PubMed searches for medical eponyms, significantly reducing manual effort. The method efficiently identifies and characterizes eponym usage across medical literature.

Keywords:
BibliometricsBiopythonCitationEponymGastrointestinal diseasesLiterature searchMedlinePubMed

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Area of Science:

  • Medical Informatics
  • Bioinformatics
  • Computational Biology

Background:

  • Medical eponyms are widely used but their frequency varies by specialty and time.
  • Manual searching of PubMed for eponyms is feasible but time-consuming.
  • Automated methods are needed to efficiently analyze eponym usage in medical literature.

Purpose of the Study:

  • To develop and validate a Python-based method for automating the search and characterization of medical eponyms in PubMed.
  • To provide a tool for rapid and comprehensive analysis of eponym frequency within specific medical fields.
  • To demonstrate the adaptability of the method for searching other medical terms like gene names and chemical compounds.

Main Methods:

  • A Python script was developed using Biopython's Bio.Entrez module to automate PubMed searches.
  • Disease eponyms were manually collected and permutations were generated to account for variations in literature.
  • Automated PubMed searches were performed, followed by duplicate removal and data field enumeration.

Main Results:

  • The developed method enables rapid searching and characterization of medical eponyms.
  • The script successfully automates the process, overcoming the limitations of manual searching.
  • The approach is versatile and applicable to various search terms beyond eponyms.

Conclusions:

  • This automated Python script offers an efficient solution for analyzing medical eponym usage in PubMed.
  • The method significantly reduces the time and effort required for literature searches.
  • The tool's flexibility allows for broad application in medical literature analysis, including gene and chemical compound searches.