Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients

no code implementations • 2 Dec 2017 • Wei-Hung Weng, Mingwu Gao, Ze He, Susu Yan, Peter Szolovits

This work aims to learn personalized optimal glycemic trajectories for severely ill septic patients by learning data-driven policies to identify optimal targeted blood glucose levels as a reference for clinicians.

reinforcement-learning Reinforcement Learning (RL)