Online redesign of clinical tests for the identification of type 1 diabetes models in the presence of continuous glucose monitoring systems.

Model-Based Design of Experiments (MBDoE) techniques represent a valuable tool to increase the information content of clinical tests with the purpose to identify the set of parameters of physiological models of type 1 diabetes mellitus. However, conventional MBDoE techniques are affected by some limitations. Prior uncertainty in the model parameters and model mismatch may lead the constrained design procedure to predict clinical tests that turn out to be suboptimal or, even worse, unsafe for the subject. Advanced MBDoE techniques, including online model-based redesign of experiments can be used to preserve the effectiveness of the experiment design sessions, exploiting in a more efficient way the nearly-continuous information flux coming from continuous glucose monitoring systems (CGMSs). In this paper a simulated case study is used to assess the impact of advanced redesign techniques on exploiting CGMSs data in the experiment design and successive parameter estimation for the identification of a complex physiological model of glucose homeostasis