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Machine learning for medical ventilator control

Cyril Zhang
Daniel Cohen
Edgar Minasyan
Elad Hazan
Julienne LaChance
Karan Singh
Manuel Schottdorf
Naman Agarwal
Paula Nicoleta Gradu
Tom Zajdel
Udaya Ghai
ML4H (2021) (to appear)
Google Scholar


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.