[Iefac.list] IISE QCRE and DAIS Joint Webinar -- Dr. Adel Alaeddini--Dynamic Characterization and Optimal Self-Management of the Emergence Trajectories of Multiple Chronic Conditions

[Xiaolei Fang]

IISE Quality Control & Reliability Engineering (QCRE) division and Data 
Analytics and Information Systems (DAIS) Division would like to invite 
you to attend our webinar on Thursday, November 17, 12 p.m.-1 p.m., 
Eastern Time.

*Webinar 
Registration:*https://us06web.zoom.us/webinar/register/WN_GLzGvI6XS-q1OcW105n8OQ 
<https://us06web.zoom.us/webinar/register/WN_GLzGvI6XS-q1OcW105n8OQ>

*Time:*Dec. 6, Tuesday, 1-2 p.m., Eastern Time.

Click to add this event to your***Google calendar* 
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*Title: Dynamic Characterization and Optimal Self-Management of the 
Emergence Trajectories of Multiple Chronic Conditions***

*Presenter:* Dr. Adel Alaeddini, Associate Professor, Department of 
Mechanical Engineering, The University of Texas at San Antonio (UTSA)

*Abstract**:*More than a quarter of all Americans and two out of three 
older Americans are estimated to have at least two chronic health 
problems. Treatment for people living with multiple chronic conditions 
(MCC) consume an estimated 66 percent of U.S. healthcare costs, and as 
the population ages, the number of MCC patients will increase. This 
mounting challenge is a major public health issue linked to suboptimal 
health outcomes and rising healthcare costs. However, fundamental 
knowledge gaps remain in our understanding of how MCC evolves at the 
individual and population levels. It is known that shared modifiable 
lifestyle behaviors, such as poor diet and physical inactivity, account 
for a large proportion of common MCC, and the progression of those 
conditions is associated with the development of additional 
comorbidities. What is not known is the dynamic effect of an 
individual’s behavioral lifestyle changes on the trajectories of MCC 
emergence. This presentation introduces functional and deep continuous 
time Bayesian networks to model the relationship among MCC and 
non/modifiable risk factors to characterize major patterns of MCC 
emergence in individuals based on a dataset from the US Department of 
Veteran Affairs. It also presents the extension of the presented 
methodology to nonlinear state-space models and tensor-based control 
charting to capture the dynamics of modifiable risk factors and their 
impact on the timing of the emergence of new chronic conditions using a 
live dataset from the Cameron County Hispanic Cohort (CCHC). Finally, it 
demonstrates the application of optimal control and reinforcement 
learning to identify the changes in modifiable behavioral lifestyle 
factors to reduce the risk of new chronic conditions. The findings aim 
to give healthcare professionals and patients the ability to identify 
effective preventive policies, including self-management strategies, to 
decelerate the evolution of new chronic conditions.

*Biography*: Dr. Adel Alaeddini is an Associate Professor of Mechanical 
Engineering at the University of Texas at San Antonio (UTSA). He is also 
the Director of the Center for Advanced Manufacturing and Lean Systems 
(CAMLS) at UTSA. Before joining UTSA, he was a Postdoctoral Scholar at 
the University of Michigan. He received his Ph.D. in Industrial and 
Systems Engineering from Wayne State University. Dr. Alaeddini’s 
research interests involve both theoretical and applied aspects of 
machine learning integrated with engineering knowledge with applications 
in healthcare and manufacturing. His research has been mainly supported 
by AFOSR (including Young Investigator Award), AFRL, NIH, DHS, and VA 
among others. Dr. Alaeddini is an associate editor of the Journal of 
Applied Statistics, Healthcare Management Science, and IISE Transactions 
on Healthcare Systems Engineering. He is the past chair of the Quality, 
Statistics, and Reliability (QSR) Section of INFORMS, and Past-President 
of the Quality Control & Reliability Engineering (QCRE) Division of the 
Institute for Industrial & Systems Engineers (IISE).

---------------------------------------------------------

QCRE and DAIS Webinar Committee Members

  Dr. Xiaowei Yue, xwy at vt.edu <mailto:xwy at vt.edu>

Assistant Professor, Grado Department of Industrial and Systems 
Engineering, Virginia Tech (DAIS)

  Dr. Xiao Liu, xl027 at uark.edu <mailto:xl027 at uark.edu>

Assistant Professor, Department of Industrial Engineering, University of 
Arkansas (DAIS)

  Dr. Na Zou, nzou1 at tamu.edu <mailto:nzou1 at tamu.edu>

Assistant Professor, Department of Engineering Technology & Industrial 
Distribution, Texas A&M University (QCRE)

  Dr. Xiaolei Fang, xfang8 at ncsu.edu <mailto:xfang8 at ncsu.edu>

Assistant Professor, Edward P. Fitts Department of Industrial and 
Systems Engineering, North Carolina State University (QCRE)

-- 

Assistant Professor
Edward P. Fitts Department of Industrial and Systems Engineering
North Carolina State University

/Member, IISE Quality Control and Reliability Engineering/
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