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Call for papers: Conference on computational social science, April 2017, U-M

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Mon, Feb 13, 2017, noon:
Daniel Almirall, "Getting SMART about adaptive interventions"

Informing sequential clinical decision-making through reinforcement learning: an empirical study

Publication Abstract

Shortreed, Susan M., Eric Laber, Daniel J. Lizotte, T. Scott Stroup, Joelle Pineau , and Susan A. Murphy. 2011. "Informing sequential clinical decision-making through reinforcement learning: an empirical study." Machine Learning, 84(1-2): 109-136.

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.

DOI:10.1007/s10994-010-5229-0 (Full Text)

PMCID: PMC3143507. (Pub Med Central)

Country of focus: United States of America.

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