  In this work, we present our various contributions to the objective of
building a decision support tool for the diagnosis of rare diseases. Our goal
is to achieve a state of knowledge where the uncertainty about the patient's
disease is below a predetermined threshold. We aim to reach such states while
minimizing the average number of medical tests to perform. In doing so, we take
into account the need, in many medical applications, to avoid, as much as
possible, any misdiagnosis. To solve this optimization task, we investigate
several reinforcement learning algorithm and make them operable in our
high-dimensional and sparse-reward setting. We also present a way to combine
expert knowledge, expressed as conditional probabilities, with real clinical
data. This is crucial because the scarcity of data in the field of rare
diseases prevents any approach based solely on clinical data. Finally we show
that it is possible to integrate the ontological information about symptoms
while remaining in our probabilistic reasoning. It enables our decision support
tool to process information given at different level of precision by the user.
