  Machine learning has successfully framed many sequential decision making
problems as either supervised prediction, or optimal decision-making policy
identification via reinforcement learning. In data-constrained offline
settings, both approaches may fail as they assume fully optimal behavior or
rely on exploring alternatives that may not exist. We introduce an inherently
different approach that identifies possible "dead-ends" of a state space. We
focus on the condition of patients in the intensive care unit, where a "medical
dead-end" indicates that a patient will expire, regardless of all potential
future treatment sequences. We postulate "treatment security" as avoiding
treatments with probability proportional to their chance of leading to
dead-ends, present a formal proof, and frame discovery as an RL problem. We
then train three independent deep neural models for automated state
construction, dead-end discovery and confirmation. Our empirical results
discover that dead-ends exist in real clinical data among septic patients, and
further reveal gaps between secure treatments and those that were administered.
