Name
Title | ||
|---|---|---|
Reinforcement Learning-Based Adaptive Insulin Advisor For Individuals With Type 1 Diabetes Patients Under Multiple Daily Injections Therapy |
Abstract | ||
|---|---|---|
The existing adaptive basal-bolus advisor (ABBA) was further developed to benefit patients under insulin therapy with multiple daily injections (MDI). Three different in silico experiments were conducted with the DMMS.R simulator to validate the approach of combined use of self-monitoring of blood glucose (SMBG) and insulin injection devices, e.g. insulin pen, as are used by the majority of type 1 diabetes patients under insulin therapy. The proposed approach outperforms the conventional method, as it increases the time spent within the target range and simultaneously reduces the risks of hyperglycaemic and hypoglycaemic events. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857178 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Diabetes mellitus,Computer science,Internal medicine,Artificial intelligence,Insulin pen,Type 1 diabetes,Insulin,Reinforcement learning,Injections therapy | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingnan Sun | 1 | 2 | 1.05 |
| Marko V. Jankovic | 2 | 2 | 0.72 |
| Stavroula G Mougiakakou | 3 | 342 | 28.61 |