title: A study on PubMed search tag usage pattern: association rule mining of a full-day PubMed query log.
abstract: BACKGROUND
  The practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search.
  
  
  METHODS
  A PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm.
  
  
  RESULTS
  The percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches.
  
  
  CONCLUSIONS
  The low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed's Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.
authors.affiliation: University of Missouri Informatics Institute, Columbia, MO 65211, USA.
authors.forename: Abu Saleh Mohammad
authors.initials: AS
authors.lastname: Mosa
authors.forename: Illhoi
authors.initials: I
authors.lastname: Yoo
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D016247:Information Storage and Retrieval; D016239:MEDLINE; D009317:National Library of Medicine (U.S.); D039781:PubMed; D014481:United States