Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation

• URL: http://arxiv.org/abs/2404.03105v2
• Date: Thu, 09 Jan 2025 11:24:56 GMT
• Title: Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
• Authors: Joo Seung Lee, Malini Mahendra, Anil Aswani,
• Abstract summary: Mechanical ventilation is a critical life support intervention that delivers controlled air and oxygen to a patient's lungs. While several data-driven approaches have been proposed to optimize ventilator control strategies, they often lack interpretability and alignment with domain knowledge. This paper presents a methodology for interpretable reinforcement learning (RL) aimed at improving mechanical ventilation control as part of connected health systems.
• Score: 2.3349787245442966
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• Abstract: 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.

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