HIV infection dynamics; drug-scheduling strategies; reinforcement learning; fitted Q iteration
Abstract :
[en] This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate 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.
Disciplines :
Computer science Immunology & infectious disease
Author, co-author :
Ernst, Damien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Stan, Guy-Bart; University of Cambridge > Department of Engineering > Control Group
Gonçalves, Jorge; University of Cambridge > Department of Engineering > Control Group
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
Publication date :
2006
Event name :
45th IEEE Conference on Decision and Control (CDC 2006)
Event place :
San Diego, United States
Event date :
13-15 December 2006
Audience :
International
Main work title :
Proceedings of the 45th IEEE Conference on Decision and Control (CDC 2006)
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Bibliography
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Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 36, 3-42.
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