Interpretable Batch IRL to Extract Clinician Goals in ICU Hypotension Management.

Exposing and understanding the motivations of clinicians is an important step for building robust assistive agents as well as improving care. In this work, we focus on understanding the motivations for clinicians managing hypotension in the ICU. We model the ICU interventions as a batch, sequential decision making problem and develop a novel interpretable batch variant of Adversarial Inverse Reinforcement Learning algorithm that not only learns rewards which induce treatment policies similar to clinical treatments, but also ensure that the learned functional form of rewards is consistent with the decision mechanisms of clinicians in the ICU. We apply our approach to understanding vasopressor and IVfluid administration in the ICU and posit that this interpretability enables inspection and validation of the rewards robustly.