Presented By: U-M Industrial & Operations Engineering
IOE 899 Seminar: Gian-Gabriel Garcia, University of Michigan
Data-driven Optimization Models for Concussion Management Decisions
Title: Data-driven Optimization Models for Concussion Management Decisions
Abstract: Concussion, the most common type of traumatic brain injury, is an emerging public health issue. It is characterized by an alteration of neurologic function and wide-ranging symptoms including memory loss and confusion. Furthermore, recent research has begun to shed light on the relationship between concussion and long-term health consequences including cognitive impairment, neurodegenerative disease, increased risk for depression, and early onset dementia. Concussion management plays a critical role in long and short-term health outcomes for those with concussion. A major challenge in concussion management is using large observational data sets to design guidelines for concussion diagnosis decisions. We address this challenge by formulating a data- driven framework which combines predictive modeling and stochastic programming to guide diagnosis decisions. We show that for concussion, our framework can accurately identify those with and without concussion while limiting misdiagnoses. Furthermore, our framework facilitates the identification of key characteristics shared by patients who are the most difficult to diagnose accurately. Finally, we discuss ongoing extensions to this work. The models developed in this research provide valuable insights to clinicians and can be extended to application in other disease areas.
Bio: Gian-Gabriel Garcia is a PhD Candidate in the Industrial and Operations Engineering Department at the University of Michigan. He holds a Bachelor’s degree in Industrial Engineering from the University of Pittsburgh and a Master’s degree in Industrial and Operations Engineering from the University of Michigan. His primary research interest is in improving medical decision making through the development and analysis of models which incorporate optimization under uncertainty, stochastic modeling, game theory, and predictive modeling. His most recent work includes applications to concussion, glaucoma, and cardiovascular disease. Among others, Gian is the recipient of the National Science Foundation Graduate Research Fellowship, the INFORMS Bonder Scholarship for Applied Operations Research in Health Services, the Rackham Merit Fellowship, and first prize at the INFORMS Minority Issues Forum Poster Competition. He has also received honorable mention for the Ford Foundation Pre-doctoral Fellowship.
Abstract: Concussion, the most common type of traumatic brain injury, is an emerging public health issue. It is characterized by an alteration of neurologic function and wide-ranging symptoms including memory loss and confusion. Furthermore, recent research has begun to shed light on the relationship between concussion and long-term health consequences including cognitive impairment, neurodegenerative disease, increased risk for depression, and early onset dementia. Concussion management plays a critical role in long and short-term health outcomes for those with concussion. A major challenge in concussion management is using large observational data sets to design guidelines for concussion diagnosis decisions. We address this challenge by formulating a data- driven framework which combines predictive modeling and stochastic programming to guide diagnosis decisions. We show that for concussion, our framework can accurately identify those with and without concussion while limiting misdiagnoses. Furthermore, our framework facilitates the identification of key characteristics shared by patients who are the most difficult to diagnose accurately. Finally, we discuss ongoing extensions to this work. The models developed in this research provide valuable insights to clinicians and can be extended to application in other disease areas.
Bio: Gian-Gabriel Garcia is a PhD Candidate in the Industrial and Operations Engineering Department at the University of Michigan. He holds a Bachelor’s degree in Industrial Engineering from the University of Pittsburgh and a Master’s degree in Industrial and Operations Engineering from the University of Michigan. His primary research interest is in improving medical decision making through the development and analysis of models which incorporate optimization under uncertainty, stochastic modeling, game theory, and predictive modeling. His most recent work includes applications to concussion, glaucoma, and cardiovascular disease. Among others, Gian is the recipient of the National Science Foundation Graduate Research Fellowship, the INFORMS Bonder Scholarship for Applied Operations Research in Health Services, the Rackham Merit Fellowship, and first prize at the INFORMS Minority Issues Forum Poster Competition. He has also received honorable mention for the Ford Foundation Pre-doctoral Fellowship.
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