Abstract
Background and Aims
Most physical activities increase the risk of hypoglycemia for people with T1D. This study assesses the performance of our adaptive personalized multivariable artificial pancreas (AP-mAP) system without any exercises and meal unannouncements.
Methods
AP-mAP utilizes model predictive control, CGM readings, plasma insulin concentration (PIC) estimates and biosignals from a wristband. A patient’s historical data is leveraged to identify daily life activities using machine learning techniques. Incorporating online learning of probable times of significant glycemic disturbances from historical data improves AP performance by proactively mitigating the effects of impending disturbances. AP-mAP parameters such as controller set-point and PIC safety constraints are proactively modified for anticipated types and periods of disturbances.
Results
Simulations with the multivariable simulator mGIPsim illustrate the performance of AP-mAP. Twenty virtual subjects were simulated for 30 days with varying times and quantities of meals and different types, intensities, and durations of physical activities. AP-mAP reduced the 30-day average of total number of rescue carbohydrates from 20 to 6 without any hypoglycemia and improved glycemic control from 68.3% to 82.4% (in 70-180mg/dL range). One subject was simulated for 120 days with randomly varying times and quantities of meals and different types, intensities, and durations of physical activities (two exercise bouts per day). AP-mAP reduced the total number of rescue carbohydrates intakes for 120 days from 494 to 191 and kept good glycemic control (~83% in 70-180mg/dL range).
Conclusions
Integrating fully automated AP with machine leaning to adapt the AP and PIC during physical activities and meals improve diabetes management.
/thumbnails/0.png)
