Machine learning for medical ventilator control
Abstract
We consider the problem of controlling a medical ventilator for pressure controlled ventilation. The goal is to control airflow in and out of a sedated patient’s lung ac-cording to a trajectory of airway pressures specified by a clinician.
PID, either hand-tuned or using lung-breath simulators based on gas dynamics, is the state-of-the-art control for ventilators.
We consider a data-driven machine learning methodology to tackle this problem via first training a simulator based on collected data and then using this simulator to train controllers based on artificial neural networks. We show that our controller is able to track significantly better than PID controllers on FDA specified benchmarks.
PID, either hand-tuned or using lung-breath simulators based on gas dynamics, is the state-of-the-art control for ventilators.
We consider a data-driven machine learning methodology to tackle this problem via first training a simulator based on collected data and then using this simulator to train controllers based on artificial neural networks. We show that our controller is able to track significantly better than PID controllers on FDA specified benchmarks.
Research Areas
