Improving Patient-Ventilator Synchrony During Pressure Support Ventilation Based on Reinforcement Learning Algorithm.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Critical Care Medicine

Background:

• Mechanical ventilation is crucial for critically ill patients but faces challenges with patient-ventilator asynchrony (PVA).
• Current PVA management relies on clinical experience, leading to inefficiencies and potential delays in ventilator adjustments.
• High rates of PVA are associated with increased mortality and prolonged ventilation.

Purpose of the Study:

• To develop and evaluate a novel deep reinforcement learning (RL) algorithm for enhancing patient-ventilator synchrony.
• To address the complex decision-making problem in optimizing mechanical ventilation settings.
• To reduce the incidence of PVA during pressure support ventilation.

Main Methods:

• A deep Q-learning (DQN) algorithm was employed to create an RL-based strategy for mechanical ventilation.
• A pneumatic model of the ventilation system was developed to simulate diverse patient conditions and PVA types.
• Clinical data were utilized for qualitative and quantitative evaluation of the RL algorithm's performance.

Main Results:

• The RL-optimized ventilation strategy significantly reduced the proportion of breaths with PVA from 37.52% to 7.08%.
• The algorithm demonstrated effectiveness in assisting clinical decision-making for ventilator management.
• The study confirmed the potential for intelligent ventilator control and automatic weaning.

Conclusions:

• Deep reinforcement learning offers a promising approach to improve patient-ventilator synchrony.
• AI-driven strategies can enhance mechanical ventilation, leading to better patient outcomes.
• This technology supports intelligent control, bedside monitoring, and automated weaning processes.