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 (), a novel non‐dimensional number that quantifies the relative magnitude of a drug's therapeutic action timescale to its transport timescale within infusion manifolds. SMART is useful when , where drug transport becomes the rate limiting step in achieving the desired therapeutic effects. When activated, SMART monitors multidrug concentrations within infusion manifolds and leverages this information to perform end‐to‐end management of drug delivery using an ensemble of deterministic and deep reinforcement learning (RL) decision networks. Notably, SMART RL networks employ differentially sampled split buffer architecture that accelerates learning and improves performance by seamlessly combining deterministic predictions with RL experience during training. SMART deployed in standalone infusion pumps under simulated clinical conditions outperformed state‐of‐the‐art manual control protocols. This framework has the potential to revolutionize critical care by enhancing accuracy of medication delivery and reducing cognitive workloads. Beyond critical care, the ability to accurately manage multi‐liquid delivery via complex manifolds will have important bearings for manufacturing and process control.