Abstract:
Invasive mechanical ventilation is a common medical treatment required for applications
spanning anesthesia, neonatal intensive care, and life support for the current COVID-19
pandemic. Despite its importance, the core technology of medical ventilation has remained
largely unchanged for years. It is common for a clinician to continuously monitor and adjust the
ventilator for a patient manually thus increasing workload. With machine learning (ML) taking
center stage in healthcare recently, the question has been asked whether ML based control
methods can be developed to replace manual intervention. The main challenge however remains
the robustness, safety, and the high cost of development. In addition, the controller must be able
to adapt reliably and quickly across varying clinical conditions and requirements not observable
directly to the clinicians. Proportional-Integral-Derivative (PID) controllers have been to go to
method of choice because of its limited parameters size, fewer samples for tuning, and its ability
to generalize over the dynamic lung conditions. Current ventilator or patient simulators are
trained by an ensemble of multiple models each simulating the parameters of a single lung.
However, human lungs and respectively their parameters or attributes are dynamic and form a
continuous space therefore, based on the physiological differences in patient lungs a parametric
approach is better suited to improve generalization. This work centers around the possibility of
developing an ML-based method which can improve performance simultaneously across a wide
range of lung parameters based upon the ISO standard for performance of ventilatory support
equipment (ISO 80601-2-80:2018). The results are compared against previously published data
and closely match the expected outcome. This is a significant improvement towards a more
robust alternative to PID tuning and more importantly cost-effective mechanical ventilators.
