A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes.

ABSTRACT: Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This paper describes a new approach for optimizing dynamic treatment regimes that bridges the gap between Bayesian inference and existing approaches, like Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model uses as a response variable the remaining payoff assuming optimal actions are taken at subsequent stages, and as covariates the current history and relevant actions at that stage. The key difficulty is that the optimal decision rules at subsequent stages are unknown,

SUBMITTER: Murray TA 

PROVIDER: S-EPMC6366650 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature