  Our aim is to establish a framework where reinforcement learning (RL) of
optimizing interventions retrospectively allows us a regulatory compliant
pathway to prospective clinical testing of the learned policies in a clinical
deployment. We focus on infections in intensive care units which are one of the
major causes of death and difficult to treat because of the complex and opaque
patient dynamics, and the clinically debated, highly-divergent set of
intervention policies required by each individual patient, yet intensive care
units are naturally data rich. In our work, we build on RL approaches in
healthcare ("AI Clinicians"), and learn off-policy continuous dosing policy of
pharmaceuticals for sepsis treatment using historical intensive care data under
partially observable MDPs (POMDPs). POMPDs capture uncertainty in patient state
better by taking in all historical information, yielding an efficient
representation, which we investigate through ablations. We compensate for the
lack of exploration in our retrospective data by evaluating each encountered
state with a best-first tree search. We mitigate state distributional shift by
optimizing our policy in the vicinity of the clinicians' compound policy.
Crucially, we evaluate our model recommendations using not only conventional
policy evaluations but a novel framework that incorporates human experts: a
model-agnostic pre-clinical evaluation method to estimate the accuracy and
uncertainty of clinician's decisions versus our system recommendations when
confronted with the same individual patient history ("shadow mode").
