Neural Relevance Model Using Similarities with Elite Documents for Effective Clinical Decision Support

Abstract

Clinical Decision Support (CDS) is regarded as an information retrieval (IR) task, where medical records are used to retrieve full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modelled, resulting in non-optimal results in that a medical record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a neural relevance model (DNRM) based on similarities to a set of elite documents, addressing the information mismatch by utilising the content of relevant articles as a complete picture of the given medical record. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model.