A Document Clustering and Ranking System for Exploring MEDLINE Citations

@article{citeulike:1719303, abstract = {ObjectiveA 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. DesignA 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. MeasurementsSeveral 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. ResultsOur 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. ConclusionsThe text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups. 10.1197/jamia.M2215}, author = {Lin, Yongjing and Li, Wenyuan and Chen, Keke and Liu, Ying }, citeulike-article-id = {1719303}, doi = {http://dx.doi.org/10.1197/jamia.M2215}, journal = {J Am Med Inform Assoc}, keywords = {clustering, medline, ranking}, month = {September}, number = {5}, pages = {651--661}, posted-at = {2008-05-12 04:46:55}, priority = {2}, title = {A Document Clustering and Ranking System for Exploring MEDLINE Citations}, url = {http://dx.doi.org/10.1197/jamia.M2215}, volume = {14}, year = {2007} }

TY - JOUR ID - citeulike:1719303 L3 - citeulike-article-id:1719303 TI - A Document Clustering and Ranking System for Exploring MEDLINE Citations JF - J Am Med Inform Assoc VL - 14 IS - 5 SP - 651 EP - 661 N2 - ObjectiveA 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. DesignA 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. MeasurementsSeveral 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. ResultsOur 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. ConclusionsThe text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups. 10.1197/jamia.M2215 KW - clustering KW - medline KW - ranking AU - Lin, Yongjing AU - Li, Wenyuan AU - Chen, Keke AU - Liu, Ying PY - 2007/09/01/ UR - http://dx.doi.org/10.1197/jamia.M2215 ER -