Abstract
BackgroundPubMed is the main access to medical literature on the Internet. In order to enhance the performance of its information retrieval tools, primarily non-indexed citations, the authors propose a method: expanding users' queries using Unified Medical Language System' (UMLS) synonyms i.e. all the terms gathered under one unique Concept Unique Identifier.MethodsThis method was evaluated using queries constructed to emphasize the differences between this new method and the current PubMed automatic term mapping. Four experts assessed citation relevance.ResultsUsing UMLS, we were able to retrieve new citations in 45.5% of queries, which implies a small increase in recall. The new strategy led to a heterogeneous 23.7% mean increase in non-indexed citation retrieved. Of these, 82% have been published less than 4 months earlier. The overall mean precision was 48.4% but differed according to the evaluators, ranging from 36.7% to 88.1% (Inter rater agreement was poor: kappa = 0.34).ConclusionsThis study highlights the need for specific search tools for each type of user and use-cases. The proposed strategy may be useful to retrieve recent scientific advancement.
Highlights
PubMed is the main access to medical literature on the Internet
We defined as Unified Medical Language System’ (UMLS) synonyms all the different terms from different T/O gathered under the same UMLS concept, e.g. “myocardial infarction” from the Medical subject heading (MeSH), “myocardial infarction” from the World health organization (WHO) Adverse Reaction Terminology (WHO-ART) and “heart attack” from the WHOART, etc. are UMLS synonyms as they are within the same UMLS concept
We propose a new strategy in order to increase recall: adding to the mapped queries all the UMLS synonyms with “ORs": q4 = “preferred term"[MeSH term] OR
Summary
PubMed is the main access to medical literature on the Internet. In order to enhance the performance of its information retrieval tools, primarily non-indexed citations, the authors propose a method: expanding users’ queries using Unified Medical Language System’ (UMLS) synonyms i.e. all the terms gathered under one unique Concept Unique Identifier. Because one third of Medline queries are performed by members of the general public [3] and because most health professionals [4] are not aware of this thesaurus, they run free-text queries, as they do when using GoogleTM This allows searching the entire PubMed collection but does not at all exploit the PubMed ATM query is continuously improved, a recent review [6] has counted 28 different entities that have devoted themselves to develop Web tools for helping users to quickly and effectively search and retrieve relevant publications on MEDLINE. The aim of this paper was to propose an extension to this previous optimization, using Unified Medical Language System® (UMLS) synonyms, and to assess its performance
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