This Article Full Text Free Full Text (PDF) Free Data Supplement All Versions of this Article: M2215v1 14/5/651    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Lin, Y. Articles by Liu, Y. Search for Related Content PubMed PubMed Citation Articles by Lin, Y. Articles by Liu, Y.

A Document Clustering and Ranking System for Exploring MEDLINE Citations

^a Laboratory for Bioinformatics and Medical Informatics, Department of Computer Science, University of Texas at Dallas, Richardson, TX
^b Department of Molecular and Cell Biology, University of Texas at Dallas, Richardson, TX
^c Yahoo!, Santa Clara, CA.

^_* Correspondence and reprints: Ying Liu, PhD, Department of Computer Science, P.O. Box 830688; MS EC31, University of Texas at Dallas, Richardson, TX 75083-0688 (Email: ying.liu{at}utdallas.edu).

Received for publication: 07/17/06; accepted for publication: 05/20/07.

Objective: A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group.

Design: A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster.

Measurements: Several possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as "important" those articles selected by the Surgical Oncology Society.

Results: Our results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster.

Conclusions: The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.