Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery
Abstract (arXiv)
Mechanical ventilation is a critical life support intervention that delivers 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 alignment with domain knowledge, hindering clinical adoption. This paper presents a methodology for interpretable reinforcement learning (RL) aimed at improving mechanical ventilation control as part of connected health systems. Using a causal, nonparametric model-based off-policy evaluation, we assess RL policies for their ability to enhance patient-specific outcomes-specifically, increasing blood oxygen levels (SpO2), while avoiding aggressive ventilator settings that may cause ventilator-induced lung injuries and other complications. Through numerical experiments on real-world ICU data from the MIMIC-III database, we demonstrate that our interpretable decision tree policy achieves performance comparable to state-of-the-art deep RL methods while outperforming standard behavior cloning approaches. The results highlight the potential of interpretable, data-driven decision support systems to improve safety and efficiency in personalized ventilation strategies, paving the way for seamless integration into connected healthcare environments.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 32 references that are in the map. First posted on arXiv 3 Apr 2024.
Builds on
10 of 32 references.
References · 10
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Citing papers per year: 2024: 1.
1 paper.