Section \[sec:proposed\].

Several works have focused on integrating feature selection methods with deep learning, e.g.,  [@li2016deep; @roy2015feature; @zhao2015heterogeneous; @louizos2017learning; @yamada2018deep; @balin2019concrete]. However, these approaches are neither adaptive nor iterative, which are core requirements in the setting we consider in this manuscript.

Lastly, the topic of intrinsic reward design (see, for example [@zheng2018learning]), used also by [@hu2018playing], has drawn much interest in the RL community. It involves design of intrinsic reward functions, which guiding the agent towards maximizing expected external reward (which may be sparse, or obtained at late time steps, for example). This has relations with our proposed approach, where we train and use use a guesser network to provide rewards to the RL agent, as will be explained in Section \[sec:proposed\].

Cohort {#sec:cohort}
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In this section we describe the data cohort we apply our method to. Detailed instructions for data acquisition and preprocessing can be found in Appendix \[sec:dpw\].

Data Source {#sec:dsource}
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We used 10-year data (2002 to 2011) from the National Health Interview Survey (NHIS), which is an annual cross-sectional survey conducted by the National Center for Health Statistics (NCHS) that provides estimates on the health status, health-care access, and behaviors of the non-institutionalized US population[^1]. 