Transport physics-informed reinforcement learning agents deployed in standalone infusion pumps for managing multidrug delivery in critical care.

ABSTRACT: Managing delivery of complex multidrug infusions in anesthesia and critical care presents a significant clinical challenge. Current practices relying on manual control of infusion pumps often result in unpredictable drug delivery profiles and dosing errors-key issues highlighted by the United States Food and Drug Administration (FDA). To address these issues, we introduce the SMART (synchronized-pump management algorithms for reliable therapies) framework, a novel approach that leverages low Reynolds number drug transport physics and machine learning to accurately manage multidrug infusions in real-time. SMART is activated based on the Shafer number ( Sh ), a novel non-dimensional number that quantifies the relative magnitude of a drug's therapeutic action timescale to its transport timesc

SUBMITTER: Chandran Suja V 

PROVIDER: S-EPMC12478332 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature