A Study on Pubmed Search Tag Usage Pattern: Association Rule Mining of a Full-day Pubmed Query Log

Abstract

BackgroundThe 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.MethodsA 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.ResultsThe 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.ConclusionsThe 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.

Design and implementation of Metta, a metasearch engine for biomedical literature retrieval intended for systematic reviewers

“…Certainly Metta was aligned to the needs of the majority of people searching the biomedical literature, who tend to carry out only one or two queries at a time, employ only one or a few search terms, and do not routinely use search tags [7, 8]. To accommodate the needs of users who employ an iterative approach (in which initial results are examined, and the initial query modified and resubmitted), we cached queries so that all previous queries from the same session were visible as users began to type in the query box.…”

Searching PubMed for a broad subject area: how effective are palliative care clinicians in finding the evidence in their field?

“…Relatively few studies have attempted to observe the effect of poor search construction on the identification of evidence in clinical practice . The closest we get to this are studies using database query logs to investigate general search behaviour . These studies show that PubMed and TRIP users tend to construct short searches using one to three search terms and few Boolean operators to broaden or narrow searches .…”

“…These studies show that PubMed and TRIP users tend to construct short searches using one to three search terms and few Boolean operators to broaden or narrow searches . Within the PubMed database, Medical Subject Headings (MeSH) are rarely employed explicitly to improve search precision and search tags seldom feature in search strategies . PubMed and TRIP users are not alone in struggling to create optimal searches.…”

Partial materialization for online analytical processing over multi-tagged document collections

“…PubMed users looking for relevant articles can benefit immensely from searching with the aid of metadata tags [35]. Since PubMed is a very large collection and the sizes of sets of search results are often large, it can certainly benefit from more efficient calculation of aggregate measures that summarize the contents of query results.…”

“…We rely on data from a PubMed query log 4 [35] , with the possible addition of one or more pure text terms. Treating each text conjunct or disjunct as if it were a single tag, these queries involve anywhere from 1 to 46 tags, with the majority of the queries using between 1 and 3 tags (Fig.…”