Item (e.g., product) reviews are one of the most popular types of user-generated content in Web 2.0. Reviews have been effectively used in collaborative filtering to recommend products to users based on similar users, and also to compute a product's star rating. However, little work has studied how reviews can be used to perform query-specific ranking of items. In this paper, we present efficient top-k algorithms to rank items, by weighing each review's rating by its relevance to the user query. We propose a non-random access algorithm and perform a comprehensive evaluation of our method on multiple datasets. We show that our solution significantly outperforms the baseline approach in terms of query response time.

In the United States, about 600,000 people die of heart disease every year. The annual cost of care services, medications, and lost productivity reportedly exceeds 108.9 billion dollars. Effective disease risk assessment is critical to prevention, care, and treatment planning. Recent advancements in text analytics have opened up new possibilities of using the rich information in electronic medical records (EMRs) to identify relevant risk factors. The 2014 i2b2/UTHealth Challenge brought together researchers and practitioners of clinical natural language processing (NLP) to tackle the identification of heart disease risk factors reported in EMRs. We participated in this track and developed an NLP system by leveraging existing tools and resources, both public and proprietary. Our system was a hybrid of several machine-learning and rule-based components. The system achieved an overall F1 score of 0.9185, with a recall of 0.9409 and a precision of 0.8972.

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 SNOMED-CT. Medical researchers have found that it is effective to search and find similar EMRs using their concepts, and have proposed so-phisticated 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 experi-mentally evaluate the performance and scalability of our methods on a large real EMR data set. 1.