  Guideline-based treatment for sepsis and septic shock is difficult because
sepsis is a disparate range of life-threatening organ dysfunctions whose
pathophysiology is not fully understood. Early intervention in sepsis is
crucial for patient outcome, yet those interventions have adverse effects and
are frequently overadministered. Greater personalization is necessary, as no
single action is suitable for all patients. We present a novel application of
reinforcement learning in which we identify optimal recommendations for sepsis
treatment from data, estimate their confidence level, and identify treatment
options infrequently observed in training data. Rather than a single
recommendation, our method can present several treatment options. We examine
learned policies and discover that reinforcement learning is biased against
aggressive intervention due to the confounding relationship between mortality
and level of treatment received. We mitigate this bias using subspace learning,
and develop methodology that can yield more accurate learning policies across
healthcare applications.
