# Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients

arXiv: https://arxiv.org/abs/1712.00654 · HTML: https://mlsanity.com/paper/1712.00654/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2017.

## Built on by (most cited first, 13 of 13)

- [Challenges for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2103.05612/) (2021)
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- [On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects](https://mlsanity.com/paper/2301.00512/) (2023)
- [MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas](https://mlsanity.com/paper/2003.01283/) (2020)
- [An Analysis of Reinforcement Learning for Malaria Control](https://mlsanity.com/paper/2107.08988/) (2021)
- [Deep Attention Q-Network for Personalized Treatment Recommendation](https://mlsanity.com/paper/2307.01519/) (2023)
- [Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation](https://mlsanity.com/paper/2310.14976/) (2023)
- [MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation](https://mlsanity.com/paper/2408.01445/) (2024)
- [How Should We Represent History in Interpretable Models of Clinical Policies?](https://mlsanity.com/paper/2412.07895/) (2024)
- [DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care](https://mlsanity.com/paper/2510.08350/) (2025)

## Abstract (arXiv)

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.