Shedding light on grey noise in diabetes modelling

Candidate models to capture glycaemia in outpatients with diabetes must be considered in the context of the data needed to identify the models, the ability of the model to adapt to the patient state, and the practical identifiability of the models for a particular data quality. In particular, the outpatient environment presents challenges for acquiring quality data and gold-standard methods of measurement are frequently infeasible. Only models that can be practically identified using the type and quality of data available in an outpatient setting should be considered, thus informing model development. Furthermore, the candidate models should also be capable of capturing inter- and intra- patient variability in the heterogeneous metabolism of individuals with diabetes. Finally, practically identifiable models need to also be identifiable over a clinically acceptable time period so the models are useful in context for managing diabetes.