Reinforcement learning shows promise for diabetes dosing

Known as reinforcement learning, the method uses an algorithm that learns from patient records, using decisions made by patients rather than trial and error to inform dosing decisions. The Bristol team found that the approach significantly outperformed commercial blood glucose controllers in terms of safety and effectiveness to refine insulin dosing and controlling blood sugar. The work is published in the Journal of Biomedical Informatics.

“These machine learning driven algorithms have demonstrated superhuman performance in playing chess and piloting self-driving cars, and therefore could feasibly learn to perform highly personalised insulin dosing from pre-collected blood glucose data,” said lead author Harry Emerson from Bristol’s Department of Engineering Mathematics.

“This particular piece of work focuses specifically on offline reinforcement learning, in which the algorithm learns to act by observing examples of good and bad blood glucose control. Prior reinforcement learning methods in this area predominantly utilise a process of trial-and-error to identify good actions, which could expose a real-world patient to unsafe insulin doses.”