Health AI Central
Peer-Reviewed Publication
Comput Biol Med2026;215111922.October 1, 2026Journal Article

A hierarchical reinforcement learning and predictive control framework for automated multi-variable anesthesia.

Sara Hosseinirad1, Manuela Merlo2, Francesco Trovò3, Emiliano Tognoli4, Alberto M Metelli3, Guy A Dumont5
1University of British Columbia, Vancouver, V6T 1Z4, BC, Canada. Electronic address: sarahrad@ece.ubc.ca.
2Politecnico di Milano, Milano, 20133, MI, Italy; University of Zurich, Zürich, 8092, Zürich, Switzerland.
3Politecnico di Milano, Milano, 20133, MI, Italy.
4Istituto Nazionale dei Tumori, Milano, 20133, MI, Italy.
5University of British Columbia, Vancouver, V6T 1Z4, BC, Canada.
PubMed DOI

Abstract

BACKGROUND AND OBJECTIVE: Automated multi-variable anesthesia must balance conflicting objectives amid patient variability, safety constraints, and partial observability. The state-of-the-art robust approach uses a Genetic Algorithm (GA) to optimize Generalized Predictive Control (GPC) parameters across a population, but the resulting fixed-parameter controller is conservative and slow to adapt. H…

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A hierarchical reinforcement learning and predictive control framework for automated multi-variable anesthesia | Health AI Central