Efficient Concept-based Document Ranking

Recently, there is increased interest in searching and computing the similarity between Electronic Medical Records (EMRs). A unique characteristic of EMRs is that they consist of ontological concepts derived from biomedical ontologies such as UMLS or SNOMEDCT. Medical researchers have found that it is effective to search and find similar EMRs using their concepts, and have proposed sophisticated similarity measures. However, they have not addressed the performance and scalability challenges to support searching and computing similar EMRs using ontological concepts. In this paper, we formally define these important problems and show that they pose unique algorithmic challenges due to the nature of the search and similarity semantics and the multi-level relationships between the concepts. In particular, the similarity between two EMRs is a function of the minimum semantic distance from each concept of one document to a concept of the other and vice versa. We present an efficient algorithm to compute the similarity between two EMRs. Then, we propose an early-termination algorithm to search for the top-k most relevant EMRs to a set of concepts, and to find the top-k most similar EMRs to a given EMR. We experimentally evaluate the performance and scalability of our methods on a large real EMR data set.