Potentiality of healthcare big data: Improving search by automatic query reformulation

Clinical Decision Systems utilize patient profiles to search for relevant medical support. Existing work on medical search has a primary topic on query expansion, which enriches queries by adding more useful terms. However, it performs well when queries are concise. In this paper, we aim to tackle verbose queries and propose a new automatic query reformulation method, which not only considers query expansion, but also includes query reduction. This improves the searching performance by refining the query to effectively avoid irrelevant results. The method is achieved by classifying each sentence into expansion and reduction categories with a weighted score model, which depends on the occurrence of medical and negative terms. The refined queries show promising results on experiments with TREC CDS datasets. In particular, the final performance makes improvements in terms of NDCG as 8.2% compared to the prevailing query expansion method, and 22.07% compared to the baseline of original query.