Fifth Benelux Bioinformatics Conference, Liège, 1415 December 2009
Dynamic Treatment Regimes using Reinforcement Learning: a Cautious Generalization Approach Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst University of Liège, University of Michigan
The treatment of chroniclike illnesses such has HIV infection, cancer or chronic depression implies longlasting treatments that can be associated with low quality outcome, painful side effects and expensive costs. To enhance these treatments, clinicians often adopt what we call Dynamic Treatment Regimes (DTRs). DTRs are sets of sequential decision rules defining what actions should be taken at a specific instant to treat a patient based on information observed up to that instant. Ideally, DTRs should lead to treatments which result in the most favorable clinical outcome possible. Since a few years, a growing research community is working on the development of formal methods (mainly issued from mathematics, statistics and control theory) that allow to infer from clinical data highquality DTRs. We propose in this framework an algorithm of quadratic complexity that infer from clinical data a sequence of treatment actions. The algorithm (called CGRL for Cautious Generalization for Reinforcement Learning) has cautious generalization properties, i.e. it avoids taking treatment actions for which the sample of clinical data is too sparse to make safe generalization. The algorithm also has consistency properties, which means that when the sparsity of the set of clinical data decreases to zeros, the inferred sequence of treatment actions is actually optimal. Moreover, in some favorable cases, some tight performance guarantees on the inferred sequence of treatment actions can be computed. The algorithm is illustrated using some simulated data dealing with the HIV infection.
Dec 15, 2009 - Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst. University of Liège, University of Michigan. The treatment of chroniclike illnesses such has HIV infection, cancer or chronic depression implies longlasting treatments that
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