Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning.

ABSTRACT: Background: Markov decision process (MDP) models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians. Methods: In an effort to improve usability and interpretability, we examined whether Poisson regression can approximate optimal hypertension treatment policies derived by an MDP for maximizing a patient's expected discounted quality-adjusted life years. Results: We found that our Poisson approximation to the optimal treatment policy matched the optimal policy in 99% of cases. This high accuracy translates to nearly identical health outcomes for patients. Furthermore, the Poisson approximation results in 104 additional quality-adjus

SUBMITTER: Schell GJ 

PROVIDER: S-EPMC6124941 | biostudies-literature | 2016 Jul-Dec

REPOSITORIES: biostudies-literature