Personalization of a Glucose-Insulin Model for Diabetic Patients

Adaptive model predictive control methods are used to estimate the model parameters of an FDA-approved model of glucose-insulin interaction given patient blood glucose concentration data. This process allows for personalization of the model for a given patient and allows the model to be used for further analysis such as medication dosing. The parameters are ranked in terms of the sensitivity of the model's output (glucose and insulin) to parameter variation. Using this ranking, adaptation is performed for the full set of parameters as well as a subset of the most sensitive parameters. The simulated glucose responses for the adapted parameters are compared to the patient data and to simulated glucose responses using the average diabetic parameters listed in the original model paper.

• Publication year

  2018

• Venue

  American Control Conference

• Publication date

  2018-06-01

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.23919/ACC.2018.8431057
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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