Learn More
i ii The contents of this paper are the author's sole responsibility. They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its members. This publication may be reproduced in part for educational or non-profit purposes without special permission from the copyright holder, provided acknowledgment of the source is(More)
OBJECTIVE Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract(More)
Sport result prediction is nowadays very popular among fans around the world, which particularly contributed to the expansion of sports betting. This makes the problem of predicting the results of sporting events, a new and interesting challenge. Consequently systems dealing with this problem are developed every day. This paper presents one such system,(More)
A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to(More)
A wide-spread integration of Wireless Sensor Networks (WSNs) into daily life applications demands modular and flexible service oriented architectures. Discovering nodes and their services is imperative to any large-scale sensor network deployment. In this paper, we describe nanoSD, a lightweight service discovery protocol, designed for highly dynamic,(More)
BACKGROUND There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning(More)