Optimal Control of Mechanical Ventilators with Learned Respiratory Dynamics

• Main Authors: Ward, Isaac Ronald, Asmar, Dylan M, Arief, Mansur, Mike, Jana Krystofova, Kochenderfer, Mykel J
• Format: Journal Article
• Language: English
• Published: 12-11-2024
• Subjects: Computer Science - Learning; Computer Science - Systems and Control
• DOI: 10.48550/arxiv.2411.07971

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).