Predicting Plasma Glucose From Interstitial Glucose Observations Using Bayesian Methods

Alexander Hildenbrand Hansen, Anne Katrine Duun-Henriksen, Rune Juhl, Signe Schmidt, Kirsten Nørgaard, John Bagterp Jørgensen, Henrik Madsen

Research output: Contribution to journalJournal articleResearchpeer-review


One way of constructing a control algorithm for an artificial pancreas is to identify a model capable of predicting plasma glucose (PG) from interstitial glucose (IG) observations. Stochastic differential equations (SDEs) make it possible to account both for the unknown influence of the continuous glucose monitor (CGM) and for unknown physiological influences. Combined with prior knowledge about the measurement devices, this approach can be used to obtain a robust predictive model. A stochastic-differential-equation-based gray box (SDE-GB) model is formulated on the basis of an identifiable physiological model of the glucoregulatory system for type 1 diabetes mellitus (T1DM) patients. A Bayesian method is used to estimate robust parameters from clinical data. The models are then used to predict PG from IG observations from 2 separate study occasions on the same patient. First, all statistically significant diffusion terms of the model are identified using likelihood ratio tests, yielding inclusion of σIsc, σGp, and σGsc . Second, estimates using maximum likelihood are obtained, but prediction capability is poor. Finally a Bayesian method is implemented. Using this method the identified models are able to predict PG using only IG observations. These predictions are assessed visually. We are also able to validate these estimates on a separate data set from the same patient. This study shows that SDE-GBs and a Bayesian method can be used to identify a reliable model for prediction of PG using IG observations obtained with a CGM. The model could eventually be used in an artificial pancreas.
Original languageEnglish
JournalJournal of Diabetes Science and Technology
Issue number2
Pages (from-to)321-330
Publication statusPublished - 2014


  • Bayesian methods
  • Plasma glucose dynamic
  • PG-IG dynamic
  • Stochastic differential equations
  • Stochastic gray-box modeling
  • Type 1 diabetes mellitus


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