Aggregating semantic information nuggets for answering clinical queries

In this paper, we address the issue of answering PICO clinical queries formulated within the Evidence Based Medicine framework. Answering clinical questions gives raise to numerous challenges among wich term ambiguity and relevane estimation based on the distribution of the query facets in the documents. The contributions of this work include (1) a new algorithm for query refinement based on the semantic mapping of each facet of the query to a reference terminology and (2) a new document ranking model based on a prioritized aggregation operator that leverages the importance of each facet with regard to a candidate relevant document. The effectiveness of our PICO-based search approach is empirically evaluated using a clinical retrieval collection including 423 queries and more than 1.2 million of medical abstracts from PubMed. The experimental results show that our approach for PICO query answering significantly overpasses state-of-the-art document ranking models