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EECS500 Fall 2015 Department Colloquium

Presenter: 
Benjamin Vandendriessche
Title: 
Exploiting physiologic time series dynamics to track the state of intensive care patients
Affiliation: 
CWRU
Location: 
White 411
Time: 
11:30am-12:30pm
Date: 
December 1, 2015

Despite the wealth of monitoring devices present in an intensive care unit (ICU), identification of patients at risk for decompensation is still based on snapshot observations, rather than on continuous data from real-time monitoring systems that can generate a high-resolution and comprehensive picture of their dynamical status. The DICE study (Data Integration in a Critical Care Environment) is an ongoing effort at CWRU and UHCMC to archive long-term waveform recordings from ICU patients annotated with other relevant clinical information, and leverage this data to develop biomarkers that can be used as a real-time early warning system for acute or gradual changes in a patients’ status. I will briefly introduce DICE, focusing on techniques for quantification of multiscalar time series dynamics and their translational relevance. Furthermore, I will discuss a dimensionality reducing technique and a visualization tool based on a physiologic phase space we are developing to improve the clinical applicability and relevance of this class of algorithms.

Biography: 

Dr. Benjamin Vandendriessche is a postdoctoral fellow (B.A.E.F) in the Department of Electrical Engineering and Computer Science and the Division of Pulmonary, Critical Care and Sleep Medicine at CWRU. He earned his MSc in Structural Biology in 2009 at Ghent University (Belgium), and his PhD in Molecular and Computational Biology in 2013 at the Flemish Institute for Biotechnology (Belgium). His current research focusses on applying concepts from biological dynamics and nonlinear systems theory to develop novel biomarkers for tracking the status of critical care patients. Furthermore, he is involved in basic research into the neural control mechanisms that drive physiologic time series dynamics, and how these are influenced by systemic inflammation.