Related papers: Machine Learning for Mechanical Ventilation Control

Machine Learning for Mechanical Ventilation Control

URL: http://arxiv.org/abs/2102.06779v1
Date: Fri, 12 Feb 2021 21:23:33 GMT
Title: Machine Learning for Mechanical Ventilation Control
Authors: Daniel Suo, Udaya Ghai, Edgar Minasyan, Paula Gradu, Xinyi Chen, Naman Agarwal, Cyril Zhang, Karan Singh, Julienne LaChance, Tom Zadjel, Manuel Schottdorf, Daniel Cohen, Elad Hazan
Abstract summary: We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation. A PID controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician. We show that our controllers are able to track target pressure waveforms significantly better than PID controllers.
Score: 52.65490904484772
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician. Hand-tuned PID controllers and similar variants have comprised the industry standard for decades, yet can behave poorly by over- or under-shooting their target or oscillating rapidly. We consider a data-driven machine learning approach: First, we train a simulator based on data we collect from an artificial lung. Then, we train deep neural network controllers on these simulators.We show that our controllers are able to track target pressure waveforms significantly better than PID controllers. We further show that a learned controller generalizes across lungs with varying characteristics much more readily than PID controllers do.

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