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Related Experiment Video

Updated: Jul 14, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

A document clustering and ranking system for exploring MEDLINE citations.

Yongjing Lin1, Wenyuan Li, Keke Chen

  • 1Laboratory for Bioinformatics and Medical Informatics, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688, USA.

Journal of the American Medical Informatics Association : JAMIA
|June 30, 2007
PubMed
Summary

This study developed a text mining system to organize and rank MEDLINE citations, improving information retrieval by grouping results into relevant topics and highlighting key articles.

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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Area of Science:

  • Biomedical Informatics
  • Text Mining
  • Information Retrieval

Background:

  • Presenting numerous information retrieval results from biomedical queries is challenging.
  • Long lists of citations offer poor overviews, hindering efficient literature review.

Purpose of the Study:

  • To develop an approach for organizing MEDLINE citations into topical groups.
  • To prioritize important citations within each group for better user overview.

Main Methods:

  • A text mining system was developed for automatic document clustering and ranking of PubMed query results.
  • Citations were grouped, ranked using Citation Count Per Year (CCPY), Citation Count (CC), and Journal Impact Factor (JIF), and described with keywords and MeSH terms.

Main Results:

  • The Citation Count Per Year (CCPY) ranking function outperformed Citation Count (CC) and Journal Impact Factor (JIF) in identifying important articles.
  • Text clustering and knowledge extraction effectively organized results into informative topical groups.

Conclusions:

  • The text mining system successfully integrated text clustering, summarization, and ranking.
  • This approach organizes MEDLINE retrieval results into distinct, informative topical groups.