2024/04/03 by Joo Seung Lee, Lee, Joo Seung, Malini Mahendra +3
Engineering · #Building Energy and Comfort Optimization #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Refrigeration and Air Conditioning Technologies
paper · pdf · doi:10.48550/arxiv.2404.03105
openalex publication_date 2024/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.