Name
Abstract | ||
|---|---|---|
Advisory and decision support systems can inundate users with frequent recommendations that are computationally expensive to generate. In this work, we develop a novel event-triggered insulin dosing algorithm for regulating glucose concentrations in people with Type 1 diabetes that is only executed when specified criteria are met. The proposed event-triggered insulin dosing algorithm is designed to function without announcements for meals and physical activity from the users. The algorithms are robust to missing data and signal dropouts that are common in free-living conditions. The safety and efficacy of the proposed event-triggered insulin dosing algorithm are demonstrated with simulation case studies that show the glucose concentrations are within the safe target range for 81.25% of the time, without any hypoglycemia, and the algorithm is only activated 62.67% of the time. The proposed computationally-efficient and robust event-triggered insulin dosing algorithm provides personalized assessment and can regulate glucose concentrations effectively in the presence of unannounced meals and physical activity. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BHI50953.2021.9508572 | 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) |
Keywords | DocType | ISSN |
Event-triggered control,personalized glycemic regulation,latent variable model,model predictive control,type 1 diabetes,missing data | Conference | 2641-3590 |
ISBN | Citations | PageRank |
978-1-6654-4770-6 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyu Sun | 1 | 95 | 16.54 |
| Mudassir M. Rashid | 2 | 0 | 0.34 |
| Mohammad-Reza Askari | 3 | 22 | 5.24 |
| Nicole Hobbs | 4 | 0 | 1.35 |
| Rachel Brandt | 5 | 1 | 2.44 |
| Ali Cinar | 6 | 0 | 0.34 |