Offline reinforcement learning with uncertainty for treatment strategies in sepsis

Understand

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.