Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes
- de Paula, Mariano
- Avila, Luis Omar
- Martinez, Ernesto Carlos
DOIAbstract
Automated control of blood glucose (BG) concentration with a fully automated artificial pancreas will certainly improve the quality of life for insulin-dependent patients. Closed-loop insulin delivery is challenging due to inter- and intra-patient variability, errors in glucose sensors and delays in insulin absorption. Responding to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and providing the necessary personalized control for individuals is a challenging task for existing control algorithms. A novel approach for controlling glycemic variability using simulation-based learning is presented. A policy iteration algorithm that combines reinforcement learning with Gaussian process approximati...
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