R. Besson

Frédéric Logé, Rémi Besson, Stéphanie Allassonnière

A patient suffering from a rare disease in France has to wait an average of two years before being diagnosed. This medical wandering is highly detrimental both for the health system and for patients whose pathology may worsen. There exists an efficient network of Centres of Reference for Rare Diseases (CRMR), but patients are often referred to these structures too late. We are considering a probabilistic modelling of the patient pathway in order to create a simulator that will allow us to create an alert system that detects wandering patients and refers them to a CRMR while considering the potential additional costs associated with these decisions.

Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière et al.

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.