Optimization of Sequential Decision Making for Chronic Diseases: From Data to Decisions
Abstract
Rapid advances in healthcare for chronic diseases such as cardiovascular disease, cancer, and diabetes have made it possible to detect diseases at early stages and tailor treatment based on individual patient risk factors including demographic factors and disease-specific biomarkers. However, a large number of relevant risk factors, combined with uncertainty in future health outcomes and the side effects of health interventions, makes clinical management of diseases challenging for physicians and patients. Data-driven operations research methods have the potential to help improve medical decision making by using observational data that are now routinely collected in many health systems. Optimization methods in particular, such as Markov decision processes and partially observable Markov decision processes, have the potential to improve the protracted sequential decision-making process that is common to many chronic diseases. This tutorial provides an introduction to some of the most commonly used methods for building and solving models to optimize sequential decision making. The context of chronic diseases is emphasized, but the methods apply broadly to sequential decision making under uncertainty. We pay special attention to the challenges associated with using observational data and the influence of model parameter uncertainty and ambiguity.
Video of this TutORial from the 2018 INFORMS Annual Meeting in Phoenix, Arizona, November 5, 2018, is available at https://youtu.be/1MZ-vVUxtw8.
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