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Dynamic Characterization and Optimal Self-Management of the Emergence Trajectories of Multiple Chronic Conditions
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. However, fundamental knowledge gaps remain in our understanding of how MCC evolves at the individual and population levels. 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.

Dec 6, 2022 01:00 PM in Eastern Time (US and Canada)

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Adel Alaeddini, Ph.D.
Associate Professor @The University of Texas at San Antonio (UTSA)
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.