On-line policy learning and adaptation for real-time personalization of an artificial pancreas

作者：

Highlights：

• The importance of tailoring an artificial pancreas to a given patient is addressed.

• On-line policy learning integrates reinforcement learning with Gaussian processes.

• Only relevant data is used to update the control policy.

• Fast policy adaptation allows dealing with patient-specific glycemic variability.

摘要

•The importance of tailoring an artificial pancreas to a given patient is addressed.•On-line policy learning integrates reinforcement learning with Gaussian processes.•Only relevant data is used to update the control policy.•Fast policy adaptation allows dealing with patient-specific glycemic variability.

论文关键词：Diabetes,Gaussian processes,Glycemic variability,On-line sparsification,Policy learning,Reinforcement learning

论文评审过程：Available online 31 October 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.10.038