Medical Diagnosis Decision-Support System: Optimizing Pattern
Recognition of Medical Data
Dr. W. Art Chaovalitwongse
Assistant Professor, Department of Industrial and Systems Engineering
Rutgers University
Faculty Fellow, Center for Supply Chain Management, Rutgers Business School
Faculty Member, Center for Advanced Infrastructure & Transportation (CAIT)
Member, Center for Discrete Mathematics & Theoretical Computer Science (DIMACS)
Abstract: In this presentation, I will discuss some recent advances in optimization and data mining
used to develop a new pattern recognition framework. This work relates to medical data signal
processing apparatus and computational framework, where optimization and data mining techniques
are employed to analyze medical data as advanced medical decision-support systems. The ultimate
goal of this research is to improve the current medical diagnosis and prognosis by assisting the
physicians in recognizing (data-mining) abnormality patterns in medical data, which are usually
encrypted spatially and temporally. The diagnosis of epilepsy and brain disorders is a case point in this
study. We have developed several optimization approaches in attempt to predict seizures and localize
the abnormal brain area initiating the seizures as well as identify if the patients have epilepsy or other
brain disorders. If time permits, I will discuss other applications of this framework.
Bio: Dr. Wanpracha Art Chaovalitwongse is an Assistant Professor of Industrial and Systems
Engineering at Rutgers University where he has been on the faculty since 2005. He received M.S. and
Ph.D. degrees in Industrial and Systems Engineering from University of Florida in 2000 and 2003. He
previously worked as a Post-Doctoral Associate in the NIH-funded Brain Dynamics Laboratory, Brain
Institute and in the departments of Neuroscience and Industrial and Systems Engineering at University
of Florida. Before joining Rutgers, he worked for one year at the Corporate Strategic Research,
ExxonMobil Research & Engineering, where he managed research in developing efficient
mathematical models and novel statistical data analyses for upstream and downstream business
operations.
His research interests include optimization, data mining, and decision making models with applications
in brain diagnosis, computational biology, information retrieval, network routing, supply chain and
logistics. His research has been supported by the National Science Foundation, Cisco, ExxonMobil,
Rutgers Academic Excellence Fund, Rutgers Computing Coordination Council, and NJ Schools
Development Authority. His academic honors include 2006 National Science Foundation (NSF)
CAREER Award, 2008 & 2004 William Pierskalla Best Paper Award for research excellence in
Operations Research and Health Care applications by INFORMS (Institute for Operations Research
and the Management Sciences), 2007 Notable Alumni of King Mongut’s Institute Technology at
Ladkrabang, and 2003 Annual Award for Excellence in Research by the Industrial and Systems
Engineering Department, University of Florida. He holds one US patent and one international patent in
new seizure prediction algorithms.