(764e) Subject-Specific Multiple Input Block-Oriented Glucose Modeling of Several Type 1 Diabetic Subjects

Type 1 diabetics often experience extreme variations in
glucose concentrations which can have adverse long- and short-term effects such
as severe hypoglycemia, hyperglycemia and organ degeneration. Studies have
established that there is a need to maintain the glucose levels within a normal
range to avoid complications caused by diabetes. However, initial attempts to
regulate blood glucose levels using insulin infusion, multiple injections or a
combination of the two have had limited success as they lack the ability to
decide the appropriate rate and/or amount of insulin infusion based on the
current metabolic state of the body. An ?artificial pancreas? consisting of a
continuous glucose monitor, an insulin infusion pump, and a control algorithm
has the potential to improve glucose regulation by intelligently deciding the
proper amount of insulin delivery at the proper time. However, a critical key
in the success of the electro-mechanical pancreas is the ability to effectively
model and use this model to improve closed-loop control. Thus, the objective of
this talk is to propose a modeling method that takes into account the
simultaneous and multiple effects of food, activity, stress and their
interactions in developing subject-specific models for several type 1 diabetic
subjects.

This talk will present the results on 10 to 15 type 1
diabetic subjects where food (3 variables), activity (7 variables), insulin
infusion (2 variables), and time of day (TOD) (a total of 13 variables) are
collected for two weeks and modeled using the Wiener block-oriented method of
Rollins et al. (2010). The data sets are split two ways. One way is into
training and validation sets of 1 week each. The second way is into training (1
week), validation (4 days) and testing (3 days). The method of Rollins et al.
is modified to use an approach that determines dynamic parameters separately
from the static parameters such that consistency in the fitted models are
maintained over all the data sets to guard against over-fitting the data. Three
types of models are compared: input only (Model 1), input/output (Model 2), and
output only (Model 3). Results are given for forecast predictions K-steps ahead (KSA) from 5 minutes to 3 hours. Model 1 uses only the
13 input variables for prediction and technically it is not a KSA forecast
model. Model 2 is a pre-whitened model for forecasting one step ahead and uses
measured glucose and the 13 input variables. Model 3 is an auto-regressive-integrated
moving average (ARIMA) model that forecasts KSA using only glucose
measurements. The results show that Model 2 approaches Model 1 as k increases and that this approach can
vary considerably from subject to subject. They also show that Model 3
consistently performs worse that Model 2 but its decay in performance as k increases can also vary considerably
from subject to subject. For each data set, results are given for models with
only food variables, only activity variables and only insulin variables.
Results showing the importance of modeling interactions between input variables
are also given.

Rollins, D. K., N. Bhandari,
J. Kleinedler, K. Kotz, A. Strohbehn, L. Boland, M. Murphy, D. Andre, N. Vyas, G.Welk and W. Franke, "Free-living inferential modeling
of blood glucose level using only noninvasive inputs," Journal of Process
Control 20 95-107 (2010).