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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Published on: October 24, 2019

Developing topic-specific search filters for PubMed with click-through data.

J Li1, Z Lu

  • 1Dr. Zhiyong Lu, NCBI/NLM/NIH, 8600 Rockville Pike Building 38A, Room 1003A Bethesda, MD, 20894 USA,

Methods of Information in Medicine
|May 14, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to generate biomedical search filters using PubMed search logs. The new approach significantly improves efficiency and achieves high performance for topic-specific information retrieval.

Keywords:
Information retrievalPubMed log analysisPubMed search filterclinical topic

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Area of Science:

  • Biomedical Informatics
  • Information Retrieval
  • Computational Biology

Background:

  • Biomedical literature is vast and growing, necessitating efficient information access tools.
  • Current search filter development relies on manual methods, which are time-consuming and labor-intensive.
  • Limited availability of effective search filters hinders efficient access to relevant biomedical publications.

Purpose of the Study:

  • To investigate and develop automatic methods for generating topic-specific search filters.
  • To overcome the limitations of manual filter development by leveraging user search behavior.
  • To enhance the efficiency and performance of biomedical literature search.

Main Methods:

  • An automated method was developed using user search logs from PubMed.
  • Topic-relevant queries and their clicked articles were identified to form a document set.
  • Informative terms were statistically identified using a background set of irrelevant articles.
  • Terms were combined with Boolean operators and evaluated to create high-performance filters.

Main Results:

  • The automated method successfully generated topic-specific filters for nephrology, diabetes, pregnancy, and depression.
  • The nephrology filter achieved performance comparable to state-of-the-art methods (91.3% sensitivity, 98.7% specificity).
  • High performance metrics (over 90% in sensitivity, specificity, precision, and accuracy) were consistently obtained for all tested filters.

Conclusions:

  • A high-performance, automated method for generating topic-specific search filters was successfully developed.
  • This method significantly improves efficiency compared to existing manual approaches.
  • The developed method utilizes PubMed click-through data and offers a more scalable solution for biomedical information retrieval.