outperforms strong off-the-shelf baselines, while also being more interpretable.

Our contributions are threefold: From a technical perspective, we apply reinforcement learning for adaptive feature selection, and propose a novel environment design to support it. Doing so, we propose a non-stationary Markov Decision Process (MDP) setting, in which the reward function is learned. From a medical perspective, we show how to design a human-like AI interface which adaptively selects information pieces in order to predict 4-year mortality. From an experimental perspective, we show that our approach outperforms strong off-the-shelf baselines, while also being more interpretable.

The remainder of this manuscript is organized as follows: In Section \[sec:related\] we review related literature. The data cohort is described in Section \[sec:cohort\]. Our proposed approach is presented in Section \[sec:afs\]. In Section \[sec:experimental\] we report experimental results. Section \[sec:conclusions\] briefly concludes the manuscript.

Related Work {#sec:related}
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As the main motivation behind choosing a RL approach for the current adaptive feature selection task serve several recent applications of RL to the 20 questions game. In this game the player’s goal is to predict the identity of an unknown character, where in each step of the game the player chooses a Yes/No question to ask 