A Causal Model for Fluctuating Sugar Levels in Diabetes Patients.

BACKGROUND:Causal models of physiological systems can be immensely useful in medicine as they may be used for both diagnostic and therapeutic reasoning.AIM:In this paper we investigate how an agent may use the theory of belief change to rectify simple causal models of changing blood sugar levels in diabetes patients.METHOD:We employ the semantic approach to belief change together with a popular measure of distance called Dalal distance between different state descriptions in order to implement a simple application that simulates the effectiveness of the proposed method in helping an agent rectify a simple causal model.RESULTS:Our simulation results show that distance-based belief change can help in improving the agent's causal knowledge. However, under the current implementation there is no guarantee that the agent will learn the complete model and the agent may at times get stuck in local optima.CONCLUSION:Distance-based belief change can help in refining simple causal models such as the example in this paper. Future work will include larger state-action spaces, better distance measures and strategies for choosing actions.