Diabetes profoundly affects human life and health, regardless of country, age, or gender, and is one of the leading causes of death and disability worldwide [1]. From 1990 to 2021, the age-standardized prevalence of diabetes increased by 90.5 % globally, with increases of more than 100 % in several regions, and it is projected that by 2050, there will be 1.31 billion people with diabetes worldwide [1]. Furthermore, people with diabetes have more than twice the normal risk of early death, resulting in an estimated 150-500 million deaths around the world each year, while generating approximately 12% of health expenditure ($966 billion) [2, 3]. The rising prevalence and serious health and economic hazards have attracted the attention of scientists around the globe, and as a result, the number of studies on diabetes is increasing. The pancreas of a diabetic does not produce or produces very little insulin, or the insulin produced is not used efficiently, leading to high BG and a variety of life-threatening complications such as cardiovascular disease, nerve damage, kidney damage, lower limb amputations, and eye disease leading to decreased vision and even blindness [3]. BG control is their basic treatment, as well as the basis for preventing and treating diabetic complications. Patients mainly maintain the stability of BG by injecting insulin. However, this traditional self-management is usually cumbersome and challenging, as it requires patients to measure their BG levels several times a day, while they suffer from many of the aforementioned complications [2].

Scientists at the University of Bristol have shown that reinforcement learning, a type of machine learning in which a computer program learns to make decisions by trying different actions, significantly outperforms commercial blood glucose controllers in terms of safety and effectiveness. By using offline reinforcement learning, where the algorithm learns from patient records, the researchers improve on prior work, showing that good blood glucose control can be achieved by learning from the decisions of the patient rather than by trial and error. Type 1 diabetes is one of the most prevalent auto-immune conditions in the UK and is characterised by an insufficiency of the hormone insulin, which is responsible for blood glucose regulation. Many factors affect a person's blood glucose and therefore it can be a challenging and burdensome task to select the correct insulin dose for a given scenario. Current artificial pancreas devices provide automated insulin dosing but are limited by their simplistic decision-making algorithms.

People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-administer to adequately control their blood glucose levels. Longitudinal data streams captured from wearables, like continuous glucose monitors, can help these individuals manage their health, but currently the majority of the decision burden remains on the user. To relieve this burden, researchers are working on closed-loop solutions that combine a continuous glucose monitor and an insulin pump with a control algorithm in an `artificial pancreas.' Such systems aim to estimate and deliver the appropriate amount of insulin. Here, we develop reinforcement learning (RL) techniques for automated blood glucose control. Through a series of experiments, we compare the performance of different deep RL approaches to non-RL approaches. We highlight the flexibility of RL approaches, demonstrating how they can adapt to new individuals with little additional data. On over 2.1 million hours of data from 30 simulated patients, our RL approach outperforms baseline control algorithms: leading to a decrease in median glycemic risk of nearly 50% from 8.34 to 4.24 and a decrease in total time hypoglycemic of 99.8%, from 4,610 days to 6. Moreover, these approaches are able to adapt to predictable meal times (decreasing average risk by an additional 24% as meals increase in predictability). This work demonstrates the potential of deep RL to help people with T1D manage their blood glucose levels without requiring expert knowledge. All of our code is publicly available, allowing for replication and extension.

Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++. This optimized neural network was tasked with predicting the level of glycated hemoglobin (HbA1c) from non-invasive markers. The rate of increase in the prevalence of Diabetes Mellitus has resulted in an urgent need for early detection and accurate diagnosis. However, due to the invasiveness and limitations of conventional tests, alternate means are being considered. Limited Joint Mobility (LJM) has been reported as an indicator for poor glycemic control. LJM of the fingers is quantified and its link to HbA1c is investigated along with other potential non-invasive markers of HbA1c. We collected readings of 33 potential markers from 120 participants at a clinic in south Trinidad. Our neural network achieved 95.65% accuracy on the training and 86.67% accuracy on the testing set for male participants and 97.73% and 66.67% accuracy on the training and testing sets for female participants. Using 960 CUDA cores from a Nvidia GeForce GTX 660, our parallelized neural network was trained 50 times faster on both subsets, than its corresponding CPU implementation on an Intel Core (TM) i7-3630QM 2.40 GHz CPU.