Sean McMillan

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Conventional algorithms for modeling clinical events focus on characterizing the differences between patients with varying outcomes in historical data sets used for the model derivation. For many clinical conditions with low prevalence and where small data sets are available, this approach to developing models is challenging due to the limited number of(More)
In this paper, we explore the application of motif discovery (i.e., the discovery of short characteristic patterns in a time series) to the clinical challenge of predicting intensive care unit (ICU) mortality. As part of the Physionet/CinC 2012 challenge, we present an approach that identifies and integrates information in motifs that are statistically(More)
This year, the University of Central Florida participated in the high level feature extraction task (HLF). The goal of high level feature extraction is to identify in videos specific shots that contain concepts such as “bus,” “person playing soccer,” and “boat/ship.” In our submissions, we focused on addressing the large imbalance between the positive and(More)
Atrial fibrillation is a common occurrence in intensive care units (ICUs) and is associated with a significant increase in patient mortality and morbidity, healthcare costs, and length of hospital stay. This burden can be significantly reduced through clinical tools to identify patients at increased risk of developing atrial fibrillation during ICU(More)
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