Identification of Linear Dynamic Models for Type 1 Diabetes: A Simulation Study

Finan, Daniel A.; Zisser, Howard; Jovanovič, Lois; Bevier, Wendy C.; Seborg, Dale E.

doi:10.3182/20060402-4-br-2902.00503

Cited by 31 publications

(28 citation statements)

References 8 publications

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“…Very recently the problem of identifying such a model has been tackled from a data-driven perspective mainly using simulated data from model in the literature [22], [23]. Indeed, fitting actual T1DM subject data to the models has been treated to a much less extent (e.g., [11], [24], [25], [26]) given the difficulties in gathering appropriate patient records.…”

Section: Discussionmentioning

confidence: 99%

Glycemic trend prediction using empirical model identification

Cescon

Johansson

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Abstract-Using methods of system identification and prediction, we investigate near-future prediction of individualspecific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX-and ARMAX-based predictors was done. Predictions over 30 minutes look-ahead were capable to track glucose variation even in sensible ranges for estimation data, but not on validation data.

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Section: Discussionmentioning

confidence: 99%

Glycemic trend prediction using empirical model identification

Cescon

Johansson

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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“…Recently the problem of identifying such a model has been tackled from a data-driven perspective mainly using simulated data from models in the literature [Palerm et al, 2006], [Finan et al, 2006]. Indeed, fitting actual T1DM subject data to the models has been treated to a much less extent (e.g., [Ståhl and Johansson, 2008], , [Finan et al, 2007]) given the difficulties in gathering appropriate patient records.…”

Section: Modelingmentioning

confidence: 99%

Linear Modeling and Prediction in Diabetes Physiology

Cescon

2014

Lecture Notes in Bioengineering

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Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a valuable initiative towards an improved management of the desease.This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects.In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant models were identified from a population of 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models.ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. ARMAX-based predictors and zero-order hold were computed to allow comparison.Finally, preliminary results on subspace-based multi-step-ahead multivariate predictors is presented.

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“…Although this model has been effectively used in several studies [6], [24], it can result in non-physiological responses for some conditions. For example, large (but reasonable) basal infusion rates can produce an extended postprandial phase and meaningless negative G values [36].…”

Section: Physiological Modelmentioning

confidence: 99%

A feedforward–feedback glucose control strategy for type 1 diabetes mellitus

Marchetti

Barolo

Jovanovič

et al. 2008

Journal of Process Control

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As the "artificial pancreas" becomes closer to reality, automated insulin delivery based on real-time glucose measurements becomes feasible for people with diabetes. This paper is concerned with the development of novel feedforward-feedback control strategies for real-time glucose control and type 1 diabetes. Improved post-meal responses can be achieved by a pre-prandial snack or bolus, or by reducing the glucose setpoint prior to the meal. Several feedforward-feedback control strategies provide attractive alternatives to the standard meal insulin bolus and are evaluated in simulations using a physiological model.

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Identification of Linear Dynamic Models for Type 1 Diabetes: A Simulation Study

Cited by 31 publications

References 8 publications

Glycemic trend prediction using empirical model identification

Glycemic trend prediction using empirical model identification

Linear Modeling and Prediction in Diabetes Physiology

A feedforward–feedback glucose control strategy for type 1 diabetes mellitus

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