Optimization of a sequential decision making problem for a rare disease diagnostic application

In this work, we propose a new optimization formulation for a sequential decision making problem for a rare disease diagnostic application. We aim to minimize the number of medical tests necessary to achieve a state where the uncertainty regarding the patient's disease is less than a predetermined threshold. 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 algorithms and make them operable in our high-dimensional setting: the strategies learned are much more efficient than classical greedy strategies.