Health & Medicine, UK (Commonwealth Union) – Type 1 diabetes, also known as insulin-dependent diabetes or juvenile diabetes, is a chronic autoimmune disease that affects the way your body regulates blood sugar (glucose). In a person with type 1 diabetes, the immune system mistakenly attacks and destroys the insulin-producing cells in the pancreas called beta cells. Type 1 diabetes is believed to involve a combination of genetic predisposition and environmental factors that trigger the autoimmune response.
Scientists have indicated that the same type of machine learning techniques utilized in pilot self-driving cars as well as that beat key chess players may assist type-1 diabetes sufferers maintain their blood glucose levels within a safe level.
Researchers from the University of Bristol have demonstrated that reinforcement learning is a type of machine learning where a computer program gains knowledge to carry out actions by attempting various actions. This can significantly surpass commercial blood glucose controllers in regard to safety as well as effectiveness. With the utilization of offline reinforcement learning, where the algorithm gains knowledge from patient records, the researchers take further previous work, demonstrating that good blood glucose control is achievable by learning from the decisions of the patient as opposed to trial and error.
Type 1 diabetes is a prevalent autoimmune condition in the UK, characterized by a deficiency of the hormone insulin, which is responsible for regulating blood glucose levels.
Managing blood glucose levels can be challenging due to various factors, making it difficult to determine the appropriate insulin dosage for different situations. Although current artificial pancreas devices offer automated insulin dosing, their decision-making algorithms are often simplistic resulting in limitations.
However, a recent study published in the Journal of Biomedical Informatics suggests that offline reinforcement learning could be a significant advancement in care for individuals living with type 1 diabetes. This approach shows promise in improving outcomes, particularly in children, who experienced an additional one-and-a-half hours per day within the target glucose range.
Children with type 1 diabetes are particularly important to consider, as they often require assistance in managing their condition. The observed improvement in their glucose control could lead to significantly better long-term health outcomes.
Lead author Harry Emerson from the University of Bristol, Department of Engineering Mathematics, indicated that his research examines the reinforcement learning that may be utilized to form safer as well as insulin dosing strategies with greater effectiveness.
“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 personalized insulin dosing from pre-collected blood glucose data.”
“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 utilize a process of trial-and-error to identify good actions, which could expose a real-world patient to unsafe insulin doses.”
To mitigate the risks associated with incorrect insulin dosing, a series of experiments was conducted using the UVA/Padova simulator, an FDA-approved tool that generates a virtual patient population for testing type 1 diabetes control algorithms. The evaluation drew its attention to contrasting state-of-the-art offline reinforcement learning algorithms with one of the widely utilized artificial pancreas control algorithms.
This comprehensive comparison involved 30 virtual patients spanning different age groups, including adults, adolescents, and children. The evaluation encompassed a vast dataset of 7,000 days, and the algorithms’ performance was assessed based on adherence to current clinical guidelines.
To simulate real-world scenarios, the simulator was extended to incorporate practical challenges such as measurement errors, inaccurate patient information, and limited availability of data. By considering these factors, the study aimed to assess the algorithms’ robustness and effectiveness under realistic implementation conditions.
The scientists hope to ultimately employ reinforcement learning in real-world artificial pancreas systems. These devices function with restricted patient oversight and consequently will need key evidence of safety and effectiveness for approval.
