Clinical diagnosis guidelines aim at specifying the steps that may lead to a diagnosis. Guidelines enable rationalizing and normalizing clinical decisions but suffer drawbacks as they are built to cover the majority of the population and may fail in guiding to the right diagnosis for patients with uncommon conditions or multiple pathologies. Moreover, their updates are long and expensive, making them unsuitable to emerging practices. Inspired by guidelines, we formulate the task of diagnosis as a sequential decision-making problem and study the use of Deep Reinforcement Learning (DRL) algorithms trained on Electronic Health Records (EHRs) to learn the optimal sequence of observations to perform in order to obtain a correct diagnosis. Because of the variety of DRL algorithms and of their sensitivity to the context, we considered several approaches and settings that we compared to each other, and to classical classifiers. We experimented on a synthetic but realistic dataset to differentially diagnose anemia and its subtypes and particularly evaluated the robustness of various approaches to noise and missing data as those are frequent in EHRs. Within the DRL algorithms, Dueling DQN with Prioritized Experience Replay, and Dueling Double DQN with Prioritized Experience Replay show the best and most stable performances. In the presence of imperfect data, the DRL algorithms show competitive, but less stable performances when compared to the classifiers (Random Forest and XGBoost); although they enable the progressive generation of a pathway to the suggested diagnosis, which can both guide or explain the decision process.

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