[CSEE Talk] talk: multi-scale approach to analyze large clinical datasets, 1pm Fri 4/11 ITE325b

[Tim Finin]

	     Computer Science and Electrical Engineering
	       University of Maryland, Baltimore County

       A multi-scale approach to analyze large clinical datasets:
    Towards the understanding of the complex effects of concussions

			   Dr. Jesus Caban
		National Intrepid Center of Excellence
			     Walter Reed

		1:00pm Friday, 11 April 2014, ITE325b

Mild traumatic brain injuries (mTBIs) or concussions are invisible
injuries that are poorly understood and their sequelae can be
difficult to diagnose.  Individuals who have had concussions are at an
increased risk of depression, post-traumatic stress disorder (PTSD),
headaches, concentration difficulties, and other problems.  During the
last decade, a significant amount of attention has been given to the
acquisition of clinical data from patients suffering from mTBI.
Unfortunately, most of the data collection and analysis have focused
on individual aspects of the injury, not necessarily on comprehensive
and multi-modal analytical techniques to capture the complex
biological state of mTBI patients.

This talk will discuss a large-scale informatics database that has
been developed to enable interdisciplinary research on mTBI and will
introduce a multi-scale approach to mine complex clinical datasets.
The millions of multi-modal elements originated from different
clinical disciplines are treated as weak features and modeled
independently to generate stronger features.  Three cases of going
from weak to stronger features will be discussed including (a) an
inductive/transductive model to extract stable image features from
multi-modal MRI scans, (b) a rule-based model used to infer knowledge
from blood measurements, and (c) a sentiment analysis-based model to
extract behavioral signals from writing samples.  Once stronger
features are obtained, a relational model is used to integrate the
data and extract new knowledge from such a complex dataset.


Dr. Caban is the Acting Chief of Clinical & Research Informatics at
the National Intrepid Center of Excellence (NICoE) at Walter Reed
Bethesda.  He received a Ph.D. in Computer Science from UMBC (2009),
his M.S. degree in Computer Science from the University of Kentucky
(2005), and his B.S. in Computer Science from the University of Puerto
Rico (2002).  Over the last eight years Dr. Caban's research has
focused on the design and development of techniques to analyze
clinical and imaging data.  His research and experience has given him
the opportunity to work at top research and healthcare organizations
including the National Institutes of Health, John Hopkins
University, the University of Maryland Medical Center, and IBM
Research.  Dr. Caban is presently an adjunct faculty member at John
Hopkins University Applied Physics Lab and a part-time instructor at
the Department of Computer Science at UMBC.  Recently, he received the
2013-14 JHU/APL Junior faculty award for his commitment to teaching.
Currently, he is serving as the Associate Editor of the JAMIA special
issue on Visual Analytics in Healthcare and as the contracting officer
representative for the DoD program on "Watson-Like Technologies for
TBI/PTSD Clinical Decision Support and Predictive Analytics".

Host: Tim Oates (oates at umbc.edu)

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