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There is an emerging trend in the reinforcement learning for healthcareliterature. In order to prepare longitudinal, irregularly sampled, clinicaldatasets for reinforcement learning algorithms, many researchers will resamplethe time series data to short, regular intervals and uselast-observation-carried-forward (LOCF) imputation to fill in these gaps.Typically, they will not maintain any explicit information about which valueswere imputed. In this work, we (1) call attention to this practice and discussits potential implications; (2) propose an alternative representation of thepatient state that addresses some of these issues; and (3) demonstrate in anovel but representative clinical dataset that our alternative representationyields consistently better results for achieving optimal control, as measuredby off-policy policy evaluation, compared to representations that do notincorporate missingness information.

Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare