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Task-oriented search for evidence-based medicine


Research on how clinicians search shows that they pose queries according to three common clinical tasks: searching for diagnoses, searching for treatments and searching for tests. We hypothesise, therefore, that structuring an information retrieval system around these three tasks would be beneficial when searching for evidence-based medicine (EBM) resources in medical digital libraries. Task-oriented (diagnosis, test and treatment) information was extracted from free-text medical articles using a natural language processing pipeline. This information was integrated into a retrieval and visualisation system for EBM search that allowed searchers to interact with the system via task-oriented filters. The effectiveness of the system was empirically evaluated using TREC CDS—a gold standard of medical articles and queries designed for EBM search. Task-oriented information was successfully extracted from 733,138 articles taken from a medical digital library. Task-oriented search led to improvements in the quality of search results and savings in searcher workload. An analysis of how different tasks affected retrieval showed that searching for treatments was the most challenging and that the task-oriented approach improved search for treatments. The most savings in terms of workload were observed when searching for treatments and tests. Overall, taking into account different clinical tasks can improve search according to these tasks. Each task displayed different results, making systems that are more adaptive to the clinical task type desirable. A future user study would help quantify the actual cost-saving estimates.

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  1. Thirty topics from TREC 2014 and thirty topics from TREC 2015.

  2. The full body was not included as it contained large amounts of HTML formatting that QuickUMLS could not interpret.

  3. Elasticsearch version 2.2.0:

  4. We used the default snippet generation provided by Elasticsearch.

  5. Formally, \(\text {precision}@n = \frac{|\text {Rel} \cap \text {Ret}_n|}{|\text {Ret}_n|}\), where \(\text {Rel}\) is the set of relevant documents and \(\text {Ret}_n\) is the set of top n retrieved documents.

  6. Formally, \(\text {recip. rank} = \frac{1}{\text {rank}}\), where \(\text {rank}\) is the rank position of the first correct result in a ranked list of results.


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Correspondence to Bevan Koopman.

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Koopman, B., Russell, J. & Zuccon, G. Task-oriented search for evidence-based medicine. Int J Digit Libr 19, 217–229 (2018).

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