Automated Insulin Delivery for Type 1 Diabetes Mellitus Patients using Gaussian Process-based Model Predictive Control
| Autor: | Berno J. E. Misgeld, Eyal Dassau, Steffen Leonhardt, Lukas Ortmann, Dawei Shi, Francis J. Doyle |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Type 1 diabetes
Schedule Computer science 0206 medical engineering Process (computing) Insulin delivery Insulin sensitivity 030209 endocrinology & metabolism Systems and Control (eess.SY) 02 engineering and technology medicine.disease 020601 biomedical engineering Artificial pancreas Electrical Engineering and Systems Science - Systems and Control 03 medical and health sciences symbols.namesake Model predictive control 0302 clinical medicine Control theory FOS: Electrical engineering electronic engineering information engineering medicine symbols Gaussian process |
| Zdroj: | ACC |
| Popis: | The human insulin-glucose metabolism is a time-varying process, which is partly caused by the changing insulin sensitivity of the body. This insulin sensitivity follows a circadian rhythm and its effects should be anticipated by any automated insulin delivery system. This paper presents an extension of our previous work on automated insulin delivery by developing a controller suitable for humans with Type 1 Diabetes Mellitus. Furthermore, we enhance the controller with a new kernel function for the Gaussian Process and deal with noisy measurements, as well as, the noisy training data for the Gaussian Process, arising therefrom. This enables us to move the proposed control algorithm, a combination of Model Predictive Controller and a Gaussian Process, closer towards clinical application. Simulation results on the University of Virginia/Padova FDA-accepted metabolic simulator are presented for a meal schedule with random carbohydrate sizes and random times of carbohydrate uptake to show the performance of the proposed control scheme. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|