$D$ is a $n \times d$ matrix, where $D_{i,j}$ corresponds to the answer of patient $i$ to question $j$. Each episode is performed on a single patient. Let $x$ correspond to the ($d$-dimensional) feature vector of the patient and let $y \in \{0,1\}$ be the corresponding label. A key component in our design is a guesser function $G: \mathcal{S} \rightarrow [0,1]$ whose goal is to predict the outcome $y$ from any state $s$, which corresponds to the unmasked entries of $x$. At the beginning of each episode, the patient’s feature vector $x$ is completely masked. In each time step throughout the episode, the agent chooses to unmask a certain entry of $x$. When ready, the guesser may choose to predict the outcome $y$ from the unmasked features. When this is the case, the agent is rewarded according to the quality of the guesser’s prediction. Therefore, it learns to select features that will allow the guesser to make an accurate guess. During training, two separate optimization procedures take place, where both the guesser and the agent are being trained. Our approach is depicted in Figure \[fig:1\].

![The proposed approach. The agent selects features to unmask. The guesser uses the unmasked features 