Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation

TL;DR

This paper introduces an interpretable reinforcement learning approach using decision trees to optimize mechanical ventilation, balancing patient outcomes with safety, and demonstrating comparable performance to deep RL methods on real ICU data.

Contribution

It develops a causal, nonparametric model-based off-policy evaluation method for interpretable RL policies in mechanical ventilation, advancing clinical applicability.

Findings

Interpretable decision tree policies match deep RL performance.

The approach improves safety by avoiding aggressive ventilator settings.

Demonstrates effectiveness on real-world ICU data from MIMIC-III.

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.