Living with type 1 diabetes and insulin resistance means making around 100 decisions daily to maintain blood sugar levels within a target range. Missing or mistiming an insulin dose can lead to dizziness, weakness, or even fainting. This struggle is all too common, as studies in Diabetes Technology & Therapeutics have shown varying rates of missed or mistimed doses among diabetics.
For instance, one study revealed that over half (56.2%) of adults missed at least one bolus insulin dose in the past month, while another found 21.4% missed a dose in the last week. A third study reported 12% missed a bolus dose daily. These gaps in dosing highlight the need for innovative solutions.
Scientists are now developing machine learning (ML) systems to transform how insulin is administered. These systems aim to alleviate the daily burden of glucose regulation through automation and precision. Here’s what these advancements could mean for individuals like me.
Enhancing the Artificial Pancreas with Machine Learning
Automated insulin delivery systems require users to input meal carbohydrate counts and manually adjust for exercise. These processes take time and effort and can be prone to errors, especially for children and older adults. Current systems also fail to account for variables like sleep or stress, which can significantly impact blood sugar.
Machine learning can potentially change this by automating the entire decision-making process. A fully automated insulin delivery system, often referred to as an "artificial pancreas," could dramatically improve users' quality of life.
Professor Ali Cinar and his team at the Illinois Institute of Technology have been pioneering this field for years. Supported by the National Institutes of Health funding, they are working on integrating machine learning into artificial pancreas systems to improve their accuracy. The team has already incorporated data from wearable devices like fitness trackers into their control systems, and now they are refining these systems further using ML.
By analyzing past behaviors, ML personalizes decision-making algorithms in artificial pancreases. These systems predict behaviors that affect glucose levels, such as meal patterns or physical activity, and respond accordingly. For example, the system might recognize a pattern suggesting I’ll eat lunch soon and administer an appropriate insulin dose in advance. It continuously monitors glucose levels, adjusting doses as needed while factoring in variables like stress or exercise.
Even with inconsistent routines, the system can forecast my behavior based on sufficient historical data, ensuring more precise glucose control.
Learning Ratios for Different Foods
Certain foods affect blood sugar more significantly than others, requiring varied insulin amounts. For instance, eating a large bowl of pasta without vegetables often spikes my glucose levels. Machine learning can simplify calculating insulin doses based on specific meals.
A project by Neil Thistlethwaite and Sarkis Ter Martirosyan led to developing a DiaHelp app. This app calculates precise insulin doses using a linear regression, multi-featured ML algorithm. Data about meal type and quantity determines carbohydrate content and refines dosage recommendations over time.
The app underwent extensive testing with simulated meal data and demonstrated significant improvements in glucose control. Compared to traditional carb counting, DiaHelp provides a more accurate and user-friendly way to manage diabetes, reducing the risks of complications and enhancing quality of life.
Improving Insulin Dosing for Hospitalized Patients
When I’m hospitalized, medical professionals can find it challenging to determine the right insulin dose. They often rely on my input and past practices to stabilize my condition. Machine learning solves this issue by predicting whether hospitalized patients require higher or lower insulin doses.
ML systems can analyze patient data more accurately than traditional weight-based methods. A study in the Journal of the American Medical Informatics Association states that ML approaches provide more precise initial insulin dose estimates, helping doctors make better-informed decisions. Despite challenges due to individual variability, this method represents a promising step forward in improving glucose control for hospitalized patients.
The Future of Diabetes Management with Machine Learning
Machine learning holds immense potential for transforming how people with diabetes manage their condition. By automating insulin delivery, personalizing treatment, and improving precision in both everyday and clinical settings, ML can reduce the burden of diabetes management and improve overall outcomes. For those of us living with diabetes, these advancements bring hope for a healthier, more manageable future.
