Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients

TL;DR

This study develops a reinforcement learning approach to personalize glycemic control strategies for septic patients, aiming to improve 90-day mortality outcomes by learning from patient data.

Contribution

It introduces a data-driven, reinforcement learning framework with patient state encoding to optimize glycemic trajectories in septic critical care.

Findings

Potential 6.3% reduction in 90-day mortality

Personalized policies outperform standard approaches

Reinforcement learning effectively models patient-specific responses

Abstract

Glycemic control is essential for critical care. However, it is a challenging task because there has been no study on personalized optimal strategies for glycemic control. 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. We encoded patient states using a sparse autoencoder and adopted a reinforcement learning paradigm using policy iteration to learn the optimal policy from data. We also estimated the expected return following the policy learned from the recorded glycemic trajectories, which yielded a function indicating the relationship between real blood glucose values and 90-day mortality rates. This suggests that the learned optimal policy could reduce the patients' estimated 90-day mortality rate by 6.3%, from 31% to 24.7%. The result demonstrates that reinforcement learning with appropriate patient state encoding can potentially provide optimal glycemic trajectories and allow clinicians to design a personalized strategy for glycemic control in septic patients.