Shedding light on grey noise in diabetes modelling. (January 2017)

• Record Type: Journal Article
• Authors: Mansell, Erin J.; Docherty, Paul D.; Chase, J. Geoffrey
• Is Part Of: Biomedical signal processing and control. Volume 31(2017)
• Page Start: 16
• Page End: 30
• Publication Date: 2017-01
• Subjects: Diabetes -- Mathematical modelling -- Grey noise -- Practical identifiability -- Physical activity -- Stress; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.06.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Glycaemia of outpatients with diabetes is very difficult to model due to sparse, low quality data, inter- and intra- patient variability and a myriad of other factors that have significant, but intermittent effects. For model-based control strategies, such factors can contribute non-random grey noise that can confound patient-specific models, and reduce prediction confidence. Incorporating such factors in glycaemic models would significantly improve control if the data available allows practical identifiability of these model parameters. This review compares and analyses models that capture the glycaemic grey-noise impact of nutrition, stress and illness, exercise and circadian rhythms are compared and considered in the context of practical application to model-based outpatient diabetes management. 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.