Background: The amount of information available about millions of different subjects is growing every day. This has led to the birth of new search tools specialized in different domains, because classical information retrieval models have trouble dealing with the special characteristics of some of these domains. Evidence-based Medicine is a case of a complex domain where classical information retrieval models can help search engines retrieve documents by considering the presence or absence of terms, but these must be complemented with other specific strategies which allow retrieving and ranking documents including the best current evidence and methodological quality.
Objective: The goal is to present a ranking algorithm able to select the best documents for clinicians considering aspects related to the relevance and the quality of said documents.
Methods: In order to assess the effectiveness of this proposal, an experimental methodology has been followed by using Medline as a data set and the Cochrane Library as a gold standard.
Results: Applying the evaluation methodology proposed, and after submitting 40 queries on the platform developed, the MAP (Mean Average Precision) obtained was 20.26%.
Conclusions: Successful results have been achieved with the experiments, improving on other studies, but under different and even more complex circumstances.
Keywords: Clustering; Evidence-based medicine; Quality ranking; Relevance ranking.
Copyright © 2020. Published by Elsevier B.V.