The goal is to optimize and personalize insulin treatment and thanks JDRF grant, a team led by Stavroula Mougiakakou will investigate a large, real-world dataset to develop advanced algorithms for automated insulin delivery that are capable of predicting dangerously low or high blood sugar levels. Read all about the grant and the team’s end goal in the content ahead.
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Where the problem lies
People with diabetes have a need to control their blood sugar levels to a normal range at all times. Today, scientifically validated automated insulin delivery (AID) systems are available as tools to enhance self-management. These systems empower people with diabetes to more successfully control their condition to prevent hypoglycemia, or low-glucose, and hyperglycemic, or high-glucose, events. However, these tools still have some shortcomings, because the algorithms used in these systems do not react adequately to variables influencing the blood sugar fluctuations in individuals, such as food intake or physical activity.
Where the help steps in
The laboratory Artificial Intelligence in Health and Nutrition of the ARTORG Center for Biomedical Engineering Research of the University of Bern proposes to use big data and deep and reinforcement learning technologies (machine learning tools) to improve the prediction accuracy of AID algorithms. The Artificial Intelligence (AI) algorithms will be trained to foresee dangerously low or high blood sugar levels in real-life situations.
Committed to making diabetes data more accessible
As one of only eight laboratories to receive the prestigious research grants awarded through a request for applications (RFA) by the US-based diabetes research foundation JDRF, Prof. Mougiakakou´s team lies highly successful in its endeavors, especially considering that the grant of about 144,000 USD most importantly provides access to big data, containing diabetes-specific patients’ information from thousands of glucose monitors and insulin pumps.
