Patient-Specific Sedation Management via Deep Reinforcement Learning.

ABSTRACT: Introduction: Developing reliable medication dosing guidelines is challenging because individual dose-response relationships are mitigated by both static (e. g., demographic) and dynamic factors (e.g., kidney function). In recent years, several data-driven medication dosing models have been proposed for sedatives, but these approaches have been limited in their ability to assess interindividual differences and compute individualized doses. Objective: The primary objective of this study is to develop an individualized framework for sedative-hypnotics dosing. Method: Using publicly available data (1,757 patients) from the MIMIC IV intensive care unit database, we developed a sedation management agent using deep reinforcement learning. More specifically, we modeled the se

SUBMITTER: Eghbali N 

PROVIDER: S-EPMC8521809 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature