Predictive Probabilistic Models for Treatment Planning in Paediatric Cardiology

The planning of clinical treatment actions for children with congenital heart disease requires a subtle trade-o between their immediate and long-term consequences, where most of these consequences cannot be predicted with certainty. It is described how this problem can be cast as a nite-horizon, partially observable Markov decision process. The complexity of the resulting model is reduced by using a graphical representation of state space and transition probabilities; it is shown that such a representation yields a signi cant decrease in the number of model parameters that have to be assessed.