Reducing Conservatism in Model-Invariant Safety-Preserving Control of Propofol Anesthesia Using Falsification

TL;DR

This paper introduces a safety system for closed-loop anesthesia that uses data-driven model falsification to reduce conservatism, ensuring safe propofol plasma levels and blood pressure despite patient variability.

Contribution

It combines model-invariant verification with falsification techniques to dynamically reduce conservatism in safety-critical control of anesthesia.

Findings

Falsification reduces conservatism in safety systems.

The approach maintains safety bounds despite model uncertainty.

Model falsification improves responsiveness to patient variability.

Abstract

This work provides a formalized model-invariant safety system for closed-loop anesthesia that uses feedback from measured data for model falsification to reduce conservatism. The safety system maintains predicted propofol plasma concentrations, as well as the patient's blood pressure, within safety bounds despite uncertainty in patient responses to propofol. Model-invariant formal verification is used to formalize the safety system. This technique requires a multi-model description of model-uncertainty. Model-invariant verification considers all possible dynamics of an uncertain system, and the resulting safety system may be conservative for systems that do not exhibit the worst-case dynamical response. In this work, we employ model falsification to reduce conservatism of the model-invariant safety system. Members of a model set that characterizes model- uncertainty are falsified if discrepancy between predictions of those models and measured responses of the uncertain system is established, thereby reducing model uncertainty. We show that including falsification in a model-invariant safety system reduces conservatism of the safety system.