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Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge

Abstract : AbstractBackgroundDespite international initiatives like Orphanet, it remains difficult to find up-to-date information about rare diseases. The aim of this study is to propose an exhaustive set of queries for PubMed based on terminological knowledge and to evaluate it versus the queries based on expertise provided by the most frequently used resource in Europe: Orphanet.MethodsFour rare disease terminologies (MeSH, OMIM, HPO and HRDO) were manually mapped to each other permitting the automatic creation of expended terminological queries for rare diseases. For 30 rare diseases, 30 citations retrieved by Orphanet expert query and/or query based on terminological knowledge were assessed for relevance by two independent reviewers unaware of the query’s origin. An adjudication procedure was used to resolve any discrepancy. Precision, relative recall and F-measure were all computed.ResultsFor each Orphanet rare disease (n = 8982), there was a corresponding terminological query, in contrast with only 2284 queries provided by Orphanet. Only 553 citations were evaluated due to queries with 0 or only a few hits. There were no significant differences between the Orpha query and terminological query in terms of precision, respectively 0.61 vs 0.52 (p = 0.13). Nevertheless, terminological queries retrieved more citations more often than Orpha queries (0.57 vs. 0.33; p = 0.01). Interestingly, Orpha queries seemed to retrieve older citations than terminological queries (p < 0.0001).ConclusionThe terminological queries proposed in this study are now currently available for all rare diseases. They may be a useful tool for both precision or recall oriented literature search.
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Nicolas Griffon, Matthieu Schuers, Ferdinand Dhombres, Tayeb Merabti, Gaetan Kerdelhué, et al.. Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge. BMC Medical Informatics and Decision Making, BioMed Central, 2015, 16 (1), pp.101. ⟨10.1186/s12911-016-0333-0⟩. ⟨inserm-01350880⟩

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