Alternative Frameworks for Personalized Insulin–Glucose Models

Abstract

The description of the insulin–glucose metabolism has attracted much attention in the past decades, and several models based on physiology have been proposed. While these models provide a precious insight in the involved processes, they are seldom able to replicate and much less to predict the blood glucose (BG) value arising as a reaction of the metabolism of a specific patient to a given amount of insulin or food at a given time. Data-based models have proven to work better for prediction, but predicted and measured values tend to diverge strongly with increasing prediction horizon. Different approaches, for instance the use of vital signs, have been proposed to reduce the uncertainty, albeit with limited success. The key assumption hidden behind these methods is the existence of a single “correct” model disturbed by some stochastic phenomena. In this chapter, instead, we suggest using a different paradigm and to interpret uncertainty as an unknown part of the process. As a consequence, we are interested in models which yield a similar prediction performance for all measured data of a single patient, even if they do not yield a precise representation of any of them. This chapter summarizes two possible approaches to this end: interval models, which provide a suitable range; and probabilistic models, which provide the probability that the BG lies in predetermined ranges. Both approaches can be used in the framework of automated personalized insulin delivery, e.g., artificial pancreas or adaptive bolus calculators.