Seminar by Oguzhan Alagoz

Seminar by
Oguzhan AlagozUniversity of Wisconsin-Madison
Visiting Associate Professor of Industrial EngineeringBreast cancer is the most common non-skin cancer and the second leading cause of cancer-death in US women. Although mammography is the most effective modality for breast cancer diagnosis, it has several potential risks, including high false positive rates, which are not very rare. Therefore, the balance of benefits and risks, which dependon personal characteristics, is critical in designing a mammography screeningschedule. In contrast to prior research and existing guidelines which consider population-based screening recommendations, we propose a personalized mammography screening policy based on the prior screening history and personal riskcharacteristics of women.

 

We formulate a finite-horizon partially observable Markov decision process (POMDP) model for this problem. Our POMDP model incorporates two methods of detection (self or screen), age-specific unobservable disease progression, and age-specific mammography test characteristics. We use a validated micro-simulation model based on real data in estimating the parameters and solve this POMDP model optimally for individual patients. Our results show that our proposed personalized screening schedules outperform the existing guidelines with respect to the total expected quality-adjusted life years, while significantly decreasing the number of mammograms. We further find that the mammography screening threshold risk increases with age. We derive several structural properties of the model, including the sufficiency conditions that ensure the existence of a control-limit policy.

Biographical Sketch
Oguzhan Alagoz is currently a visiting associate professor at Bilkent University and associate professor of Industrial and Systems Engineering at the University of Wisconsin-Madison. In addition, he is an associate professor at the Department of Population Health Sciences and serves as the director of National Institute of Health (NIH)-funded Institute for Clinical and Translational Research-Simulation Center at UW-Madison School of Medicine and Public Health. His research interests include stochastic optimization, medical decision making, completely and partially observable Markov decision processes, simulation, risk-prediction modeling, health technology assessment, and scheduling. He is on the editorial boards of Operations Research, IIE Transactions, Medical Decision Making, and IIETransactions on Healthcare Engineering. He has received various awards including a CAREER award from National Science Foundation (NSF), Dantzig Dissertation Honorable Mention Award from INFORMS, 2nd place award from INFORMS Junior Faculty Interest Group best paper competition, best paper award from INFORMS Service Science Section, best poster award from UW Carbone Comprehensive Cancer Center. He has been the principal investigator and co-investigator on grants more than $3 million funded by NSF and the National Cancer Institute of NIH. He is a member of INFORMS, IIE, SMDM, and CISNET.