Type 1 diabetes mellitus is an autoimmune disease resulting in insucient insulin to regulate blood
glucose levels. The condition can be successfully managed through eective blood glucose control,
one aspect of which is the administration of bolus insulin. Formulas exist to estimate the required
bolus, and have been adopted by existing mobile expert systems. These formulas are shown to be
eective but are unable to automatically adapt to an individual.
This research resolves the limitations of existing formula based calculators by using case-based
reasoning to automatically improve bolus advice. Case-based reasoning is a method of articial
intelligence that has successfully been adopted in the diabetes domain previously, but has primarily
been limited to assisting doctors with therapy adjustments. Here case-based reasoning is instead
used to directly assist the patient.
The case-based reasoning process is enhanced for bolus advice through a temporal retrieval algorithm
coupled with domain specic automated adjustment and revision. This temporal retrieval
algorithm includes factors from previous events to improve the prediction of a bolus dose. The
automated adjustment then renes the predicted bolus dose, and automated revision improves the
prediction for future advice through the evaluation of the resulting blood glucose level.
Analysis of the temporal retrieval algorithm found that it is capable of predicting bolus advice
comparable to closed-loop simulation and existing formulas, with adapted advice resulting in
improvements to simulated blood glucose control. The learning potential of the model is made
evident through further improvements in blood glucose control when using revised advice.
The system is implemented on a mobile device with a focus on safety using formal methods
to help ensure actions performed do not violate the system constraints. Performance analysis
demonstrated acceptable response times, providing evidence that this approach is viable. The
research demonstrates how formula based mobile bolus calculators can be replaced by an articially
intelligent alternative which continuously learns to improve advice
