ArticleIET systems biology2019
Parameter estimation of a meal glucose-insulin model for TIDM patients from therapy historical data.
Article in IET systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Model predictive control optimisation using the metaheuristic optimisation for blood pressure control.IET systems biology · 2021Article
- Sliding mode control for a fractional-order non-linear glucose-insulin system.IET systems biology · 2020Article
- Postprandial Glucose Control in Type 1 Diabetes: Importance of the Gastric Emptying Rate.Nutrients · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed-loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data-based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.
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