This was part of Analytics for Improved Healthcare
Stochastic Modeling to Personalize Disease Screening Decisions
Oguzhan Alagoz, University of Wisconsin, Madison
Monday, April 3, 2023
Abstract: This talk describes the use of partially observable Markov decision processes (POMDPs) for personalizing cancer screening decisions. POMDP models can be used to address several controversial open research questions in cancer screening, such as when to start and stop screening and how often to screening. We demonstrate the development and application of a POMDP-based personalized cancer screening policy using breast cancer as an example. In addition, we briefly describe how nonadherence to the screening recommendations, limited screening resources, and existence of chronic conditions could be addressed using the POMDP modeling framework. Finally, we describe successful POMDP applications in other cancers including colorectal and lung cancer screening.