A Document-Based Neural Relevance Model for Effective Clinical Decision Support

Clinical Decision Support (CDS) can be regarded as an information retrieval (IR) task, where medical records are used to retrieve the 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 modeled, resulting in non-optimal results in that a medial 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 document-based neural relevance model (DNRM), addressing the mismatch by utilizing the content of relevant articles to complement the medical records. 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.