  Mechanical ventilation is a critical life-support intervention that uses a
machine to deliver controlled air and oxygen to a patient's lungs, assisting or
replacing spontaneous breathing. While several data-driven approaches have been
proposed to optimize ventilator control strategies, they often lack
interpretability and agreement with general domain knowledge. This paper
proposes a methodology for interpretable reinforcement learning (RL) using
decision trees for mechanical ventilation control. Using a causal,
nonparametric model-based off-policy evaluation, we evaluate the policies in
their ability to gain increases in SpO2 while avoiding aggressive ventilator
settings which are known to cause ventilator induced lung injuries and other
complications. Numerical experiments using MIMIC-III data on the stays of real
patients' intensive care unit stays demonstrate that the decision tree policy
outperforms the behavior cloning policy and is comparable to state-of-the-art
RL policy. Future work concerns better aligning the cost function with medical
objectives to generate deeper clinical insights.
