Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation

arXiv:2404.03105v1 Announce Type: new
Abstract: 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 …

abstract agreement arxiv control cs.lg data data-driven general interpretability life machine math.oc methodology patient reinforcement reinforcement learning strategies support type