Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol.

ABSTRACT: Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state-of-the-art tools used to inform dosing in drug development. In particular, we used pharmacokinetic-pharmacodynamic (PK-PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be

SUBMITTER: Ribba B 

PROVIDER: S-EPMC9662205 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature