title: Informing sequential clinical decision-making through reinforcement learning: an empirical study.
abstract: This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of a highly variable observation set. Finally, we discuss approaches to summarizing the evidence in the data for recommending a particular action and quantifying the uncertainty around the Q-function of the recommended policy. We present the results of applying these methods to real clinical trial data of patients with schizophrenia.
authors.affiliation: School of Computer Science, McGill University, Montreal, QC, Canada H3A 2T5.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Shortreed
authors.forename: Eric
authors.initials: E
authors.lastname: Laber
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Lizotte
authors.forename: T Scott
authors.initials: TS
authors.lastname: Stroup
authors.forename: Joelle
authors.initials: J
authors.lastname: Pineau
authors.forename: Susan A
authors.initials: SA
authors.lastname: Murphy