talk: A multi-scale approach to analyze large clinical datasets, Noon Thr 4/10

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, Bethesda, MD

Noon Thursday, 10 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 (NIH), 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 (COR) for the DoD program on “Watson-Like Technologies for TBI/PTSD Clinical Decision Support and Predictive Analytics”.

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