Reinforcement Learning and Fuzzy Logic Modelling for Personalized Dynamic Treatment

The treatment of patients suffering from chronic diseases is a difficult problem to be tackled. Its complexity mainly originates from the following sources: the patient-specific response to the prescribed therapy, the impact of the interplay between disease and therapy on the quality of life of the patient and relatives, and the economic costs incurred by the healthcare system. Recently, there has been considerable interest in developing, studying, and applying artificial intelligence methods to diagnosis, prognosis and treatment personalization. This paper combines two techniques from artificial intelligence, namely fuzzy logic and reinforcement learning, to develop optimal dynamic treatment for patients suffering from a chronic disease. In this paper, we focus on cancer as a chronic disease and leverage a biologically validated fuzzy logic model from the literature. Different problem settings, of increasing complexity, are taken into account, presented and analyzed. Results of an extensive numerical experimental plan confirm the potential of non-myopic decision-making when treating chronic disease patients.