Optimal Control of Mechanical Ventilators with Learned Respiratory Dynamics

TL;DR

This paper explores using learned neural network models and optimal control theory to automatically develop effective mechanical ventilator management strategies for ARDS patients, outperforming traditional guideline-based methods in simulated environments.

Contribution

It introduces a novel approach combining neural networks and optimal control within a Markov decision process framework for ventilator management, validated on a respiratory dynamics simulator.

Findings

Neural network-based controllers outperform guideline-based protocols.

Optimal control techniques discover effective ventilation strategies.

Controllers improve respiratory health markers in simulation.

Abstract

Deciding on appropriate mechanical ventilator management strategies significantly impacts the health outcomes for patients with respiratory diseases. Acute Respiratory Distress Syndrome (ARDS) is one such disease that requires careful ventilator operation to be effectively treated. In this work, we frame the management of ventilators for patients with ARDS as a sequential decision making problem using the Markov decision process framework. We implement and compare controllers based on clinical guidelines contained in the ARDSnet protocol, optimal control theory, and learned latent dynamics represented as neural networks. The Pulse Physiology Engine's respiratory dynamics simulator is used to establish a repeatable benchmark, gather simulated data, and quantitatively compare these controllers. We score performance in terms of measured improvement in established ARDS health markers (pertaining to improved respiratory rate, oxygenation, and vital signs). Our results demonstrate that techniques leveraging neural networks and optimal control can automatically discover effective ventilation management strategies without access to explicit ventilator management procedures or guidelines (such as those defined in the ARDSnet protocol).