  Clinical diagnosis guidelines aim at specifying the steps that may lead to a
diagnosis. Inspired by guidelines, we aim to learn the optimal sequence of
actions to perform in order to obtain a correct diagnosis from electronic
health records. We apply various deep reinforcement learning algorithms to this
task and experiment on a synthetic but realistic dataset to differentially
diagnose anemia and its subtypes and particularly evaluate the robustness of
various approaches to noise and missing data. Experimental results show that
the deep reinforcement learning algorithms show competitive performance
compared to the state-of-the-art methods with the added advantage that they
enable the progressive generation of a pathway to the suggested diagnosis,
which can both guide and explain the decision process.
