Learning sparse symbolic policies for sepsis treatment

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Despite its severity, no FDA-approved drug treatments exists. Recent work controlling sepsis simulations with deep reinforcement learning have successfully discovered effective cytokine mediation strategies. However, the performance of these neural-network based policies comes at the expense of their deployability in clinical settings, where sparsity and interpretability are required characteristics. To this end, we propose a pipeline to learn simple, sparse symbolic policies represented by constants and/or succinct, human-readable expressions. We demonstrate our approach by learning a sparse symbolic policy that is efficacious on simulated sepsis patients.