Alexander Van Esbroeck

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Sleep analysis is critical for the diagnosis, treatment, and understanding of sleep disorders. However, the current standards for sleep analysis are widely considered oversimplified and problematic. The ability to automatically annotate different states during a night of sleep in a manner that is more descriptive than current standards, as well as the(More)
OBJECTIVE To investigate the use of machine learning to empirically determine the risk of individual surgical procedures and to improve surgical models with this information. METHODS American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) data from 2005 to 2009 were used to train support vector machine (SVM) classifiers to(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)
Recent work on heart rate motifs (HRM) has demonstrated that information in short heart rate patterns may be useful in identifying patients at elevated risk of cardiovascular death (CVD) following acute coronary syndrome. The information in HRM complements a variety of other clinical metrics including electrocardiographic (ECG) measures. While the HRM(More)
BACKGROUND Use of the trauma and injury severity score (TRISS) for quality and outcomes assessment is challenged by the need for laborious collection of demographic and physiological data. We hypothesize that a novel stratification approach based on International Statistical Classification for Diseases, Ninth Revision (ICD-9) data that are readily available(More)
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