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 tted 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
Gonçalves, Jorge
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 :
15th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2006)
Audience :
International
Main work title :
Proceedings of the 15th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2006)
