  Reinforcement learning (RL) is a promising approach to generate treatment
policies for sepsis patients in intensive care. While retrospective evaluation
metrics show decreased mortality when these policies are followed, studies with
clinicians suggest their recommendations are often spurious. We propose that
these shortcomings may be due to lack of diversity in observed actions and
outcomes in the training data, and we construct experiments to investigate the
feasibility of predicting sepsis disease severity changes due to clinician
actions. Preliminary results suggest incorporating action information does not
significantly improve model performance, indicating that clinician actions may
not be sufficiently variable to yield measurable effects on disease
progression. We discuss the implications of these findings for optimizing
sepsis treatment.
