Data-driven stochastic dynamic programming approaches for optimal planning of disease screening and chronic disorder management

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Given the transformation towards evidence-based and personalized decision making, powerful data-driven modeling approaches are needed to obtain personalized optimal intervention plans for practical engineering problems. We propose developing novel engineering tools based on stochastic dynamic programming and data analytics to address practical sequential decision making problems. Although proposed research will be applied to cancer screening and chronic-disease management, the focus is on developing engineering methodology.First, we propose a novel bicriteria partially observable Markov decision process (POMDP) to derive the optimal Pareto-efficient policies for particular screening problems. There is limited research on multicriteria POMDPs; the existing approaches are approximations based on methods including state discretization and machine learning. To solve our POMDP exactly, we propose a novel reformulation of the model as a constrained Markov decision process (MDP) by replacing the state space with a limited collection of historical screening observations. Under reasonable conditions, this reformulation provides a tractable model for which we aim to develop efficient solution procedures by reducing the state/action space via structural properties. Using clinical data from literature and Mayo Clinic, Rochester-MN, we will apply this formulation to determine the Pareto-efficient policies for colorectal cancer screening and surveillance. The proposed model may help develop insights to improve and personalize cancer screening practices, a significant contribution as cancer is the leading cause of death in Canada. The proposed approach will also improve the engineering knowledge on POMDPs for other engineering applications.Second, we propose a novel data-driven approach for modeling the progression of irreversibly deteriorating systems (e.g., chronic diseases) and their management. Many systems are monitored by complex scoring systems based on test/inspection results or scores from technical assessments. The proposed data analytics approach will process data from longitudinal records of inspections to identify critical events (tollgates) and estimate future progression of deterioration through these tollgates, by using methods including supervised machine learning, classification, and prediction models. The findings from these proposed prescriptive/predictive analytics tools will then be used to derive an MDP model to optimize the timing of palliative/assistive interventions to decrease the disutility due to deterioration. The proposed methodology will be applied to predict amyotrophic lateral sclerosis (ALS) progression, optimize the timing of ordering assistive devices, and maximize wellbeing of ALS patients using data from Mayo Clinic, Rochester.The proposed research will be extended to consider more general settings.