Long-acting insulin management for blood glucose prediction models

Currently available state-of-the-art mathematical models for blood glucose level prediction have been
developed for intensive care departments, and thus they do not support long-acting or ‘basal’ insulins
applied typically once a day. Our goal was to adjust the current models to support basal insulin, and
thus provide a short term blood glucose prediction service applicable in outpatient care, in the form of a
smartphone application. We propose a method that simulates the absorption of basal insulin as a series
of smaller insulin doses according to four alternative ‘dosing profiles’, instead of using a single big dose
of bolus insulin. The corrected model was tested on 18 data sets originating from a clinical trial in which
16 insulin dependent patients (7 female and 9 male) used a continuous glucose monitor device to record
their blood glucose levels for six days, while their meals were recorded. The prediction errors of the
corrected model were compared to the errors of the original model with the usual statistical methods
and the error grid analysis. We also evaluated the night periods separately from the day-time. The
proposed model correction was found to reduce the error of the prediction with respect to all
investigated evaluation criteria by 0.59-1.02 mmol/l, moving the average absolute error close to the error
range on the measurement devices. This reduction could bring online, continuous blood glucose
prediction services closer to mass deployment in lifestyle support applications for diabetics.