IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Invasive mechanical ventilation (MV) is a life-sustaining therapy for
critically ill patients in the intensive care unit (ICU). However, optimizing
its settings remains a complex and error-prone process due to patient-specific
variability. While Offline Reinforcement Learning (RL) shows promise for MV
control, current stateof-the-art (SOTA) methods struggle with the hybrid
(continuous and discrete) nature of MV actions. Discretizing the action space
limits available actions due to exponential growth in combinations and
introduces distribution shifts that can compromise safety. In this paper, we
propose optimizations that build upon prior work in action space reduction to
address the challenges of discrete action spaces. We also adapt SOTA offline RL
algorithms (IQL and EDAC) to operate directly on hybrid action spaces, thereby
avoiding the pitfalls of discretization. Additionally, we introduce a
clinically grounded reward function based on ventilator-free days and
physiological targets, which provides a more meaningful optimization objective
compared to traditional sparse mortality-based rewards. Our findings
demonstrate that AI-assisted MV optimization may enhance patient safety and
enable individualized lung support, representing a significant advancement
toward intelligent, data-driven critical care solutions.
