  Hypotension in critical care settings is a life-threatening emergency that
must be recognized and treated early. While fluid bolus therapy and
vasopressors are common treatments, it is often unclear which interventions to
give, in what amounts, and for how long. Observational data in the form of
electronic health records can provide a source for helping inform these choices
from past events, but often it is not possible to identify a single best
strategy from observational data alone. In such situations, we argue it is
important to expose the collection of plausible options to a provider. To this
end, we develop SODA-RL: Safely Optimized, Diverse, and Accurate Reinforcement
Learning, to identify distinct treatment options that are supported in the
data. We demonstrate SODA-RL on a cohort of 10,142 ICU stays where hypotension
presented. Our learned policies perform comparably to the observed physician
behaviors, while providing different, plausible alternatives for treatment
decisions.
