  Sepsis is the leading cause of mortality in the ICU. It is challenging to
manage because individual patients respond differently to treatment. Thus,
tailoring treatment to the individual patient is essential for the best
outcomes. In this paper, we take steps toward this goal by applying a
mixture-of-experts framework to personalize sepsis treatment. The mixture model
selectively alternates between neighbor-based (kernel) and deep reinforcement
learning (DRL) experts depending on patient's current history. On a large
retrospective cohort, this mixture-based approach outperforms physician, kernel
only, and DRL-only experts.
