Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Shaefer says drop child tax credit in favor of universal, direct investment in American children

Buchmueller breaks down partisan views on Obamacare

ISR's Conrad says mobile phone polling faces non-response bias

More News


Gonzalez, Alter, and Dinov win NSF "Big Data Spokes" award for neuroscience network

Post-doc Melanie Wasserman wins dissertation award from Upjohn Institute

ISR kicks off DE&I initiative with lunchtime presentation: Oct 13, noon, 1430 ISR Thompson

U-M ranked #4 in USN&WR's top public universities

More Highlights

Next Brown Bag

Mon, Oct 24 at noon:
Academic innovation & the global public research university, James Hilton

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.

Browse | Search : All Pubs | Next