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Date: Thursday October the 18th, 2007
Start Time: 2pm, Place: Building 59 (Zepler), Seminar Room 2 Speaker: Guy-Bart Stan, University of Cambridge This research addresses the problem of computing optimal structured treatment interruption strategies (STI) for HIV infected patients. STI represent a class of treatments in which patients are cycled on and off drug therapy at specific time instants. The problem that we consider consists in designing efficient drug-scheduling strategies, i.e. strategies which bring the immune system into a state that allows it to independently (without help from any drug) maintain immune control over the virus. Also, this transfer to a drug-independent viral control situation should be done with as low as possible drug-related systemic effects for the patients. In this presentation, we show that reinforcement learning may be useful to extract (close-to) optimal STI strategies directly from clinical data, without the need of identifying a mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as fitted Q iteration, on numerically generated data. The corresponding paper can be found at http://www.montefiore.ulg.ac.be/~stan/CDC_2006.pdf |
