PSECMAC intelligent insulin schedule for diabetic blood glucose management under nonmeal announcement.
Therapeutically, the closed-loop blood glucose-insulin regulation paradigm via a controllable insulin pump offers a potential solution to the management of diabetes. However, the development of such a closed-loop regulatory system to date has been hampered by two main issues: 1) the limited knowledge on the complex human physiological process of glucose-insulin metabolism that prevents a precise modeling of the biological blood glucose control loop; and 2) the vast metabolic biodiversity of the diabetic population due to varying exogneous and endogenous disturbances such as food intake, exercise, stress, and hormonal factors, etc. In addition, current attempts of closed-loop glucose regulatory techniques generally require some form of prior meal announcement and this constitutes a severe limitation to the applicability of such systems. In this paper, we present a novel intelligent insulin schedule based on the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative learning memory model that emulates the healthy human insulin response to food ingestion. The proposed PSECMAC intelligent insulin schedule requires no prior meal announcement and delivers the necessary insulin dosage based only on the observed blood glucose fluctuations. Using a simulated healthy subject, the proposed PSECMAC insulin schedule is demonstrated to be able to accurately capture the complex human glucose-insulin dynamics and robustly addresses the intraperson metabolic variability. Subsequently, the PSECMAC intelligent insulin schedule is employed on a group of type-1 diabetic patients to regulate their impaired blood glucose levels. Preliminary simulation results are highly encouraging. The work reported in this paper represents a major paradigm shift in the management of diabetes where patient compliance is poor and the need for prior meal announcement under current treatment regimes poses a significant challenge to an active lifestyle.
Teddy
S D
SD
Data Mining Department, Institute for Infocomm Research, A STAR, Singapore 138632, Singapore. sdteddy@i2r.a-star.edu.sg
Quek
C
C
Lai
E M-K
EM
Cinar
A
A
D000328:Adult; D001185:Artificial Intelligence; D001245:Association Learning / Q000502:physiology*; D001786:Blood Glucose / Q000378:metabolism*; D004334:Drug Administration Schedule; D016503:Drug Delivery Systems / Q000379:methods; D004435:Eating / Q000187:drug effects; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000378:metabolism; D007328:Insulin / Q000008:administration & dosage* / Q000378:metabolism; D008297:Male; D008568:Memory / Q000502:physiology; D008954:Models, Biological; D013997:Time Factors
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin