Assessment of type II diabetes mellitus using irregularly sampled measurements with missing data

Diabetes mellitus is one of the leading diseases in the developed world. In order to better regulate blood glucose in a diabetic patient, improved modelling of insulin-glucose dynamics is a key factor in the treatment of diabetes mellitus. In the current work, the insulin-glucose dynamics in type II diabetes mellitus can be modelled by using a stochastic nonlinear state-space model. Estimating the parameters of such a model is difficult as only a few blood glucose and insulin measurements per day are available in a non-clinical setting. Therefore, developing a predictive model of the blood glucose of a person with type II diabetes mellitus is important when the glucose and insulin concentrations are only available at irregular intervals. To overcome these difficulties, we resort to online sequential Monte Carlo (SMC) estimation of states and parameters of the state-space model for type II diabetic patients under various levels of randomly missing clinical data. Our results show that this method is efficient in monitoring and estimating the dynamics of the peripheral glucose, insulin and incretins concentration when 10, 25 and 50 % of the simulated clinical data were randomly removed.

[1]  L. Groop,et al.  Insulin resistance and insulin deficiency in the pathogenesis of Type 2 (non-insulin-dependent) diabetes mellitus: errors of metabolism or of methods? , 1993, Diabetologia.

[2]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[3]  Carolyn M. Machan.  Type 2 diabetes mellitus and the prevalence of age-related cataract in a clinic population. , 2012 .

[4]  Biao Huang,et al.  Bayesian method for identification of constrained nonlinear processes with missing output data , 2011, Proceedings of the 2011 American Control Conference.

[5]  A. El-Jabali.  Neural network modeling and control of type 1 diabetes mellitus , 2005, Bioprocess and biosystems engineering.

[6]  Bhushan Gopaluni,et al.  A Feedback Glucose Control Strategy for Type II Diabetes Mellitus , 2011, 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP).

[7]  Sten Madsbad,et al.  Reduced Incretin Effect in Type 2 Diabetes , 2007, Diabetes.

[8]  Detection of organ dysfunction in type II diabetic patients , 2011, Proceedings of the 2011 American Control Conference.

[9]  Claudio Cobelli,et al.  A System Model of Oral Glucose Absorption: Validation on Gold Standard Data , 2006, IEEE Transactions on Biomedical Engineering.

[10]  I A Gardner,et al.  The utility of Bayes' theorem and Bayesian inference in veterinary clinical practice and research. , 2002, Australian veterinary journal.

[11]  R. B. Gopaluni,et al.  Developing a physiological model for type II diabetes mellitus , 2011 .

[12]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[13]  John Thomas Sorensen,et al.  A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes , 1985 .

[14]  Volker Tresp,et al.  A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction , 1997, NIPS.

[15]  O. Vahidi,et al.  Dynamic modeling of glucose metabolism for the assessment of type II diabetes mellitus , 2013 .

[16]  Giovanni Sparacino,et al.  Minimal model S(I)=0 problem in NIDDM subjects: nonzero Bayesian estimates with credible confidence intervals. , 2002, American journal of physiology. Endocrinology and metabolism.

[17]  Giovanni Sparacino,et al.  Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the c-peptide impulse response case study , 2000, IEEE Transactions on Biomedical Engineering.

[18]  E. Irving,et al.  Modeling the Prevalence of Age-Related Cataract: Waterloo Eye Study , 2012, Optometry and vision science : official publication of the American Academy of Optometry.

[19]  Kanak Saxena,et al.  Achieving Realistic and Interactive Clinical Simulation Using Case Based TheraSim’s Therapy Engine Dynamically , 2010 .

[20]  R. DeFronzo.  Lilly lecture 1987. The triumvirate: beta-cell, muscle, liver. A collusion responsible for NIDDM. , 1988, Diabetes.

[21]  Christopher G. Chute,et al.  The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects , 2013, Int. J. Medical Informatics.

[22]  Biao Huang,et al.  On simultaneous on-line state and parameter estimation in non-linear state-space models , 2013 .

[23]  C Cobelli,et al.  Minimal model SG overestimation and SI underestimation: improved accuracy by a Bayesian two-compartment model. , 1999, The American journal of physiology.

[24]  Biao Huang,et al.  Dealing with Irregular Data in Soft Sensors: Bayesian Method and Comparative Study , 2008 .

[25]  M. Jensen,et al.  Effects of type 2 diabetes on the ability of insulin and glucose to regulate splanchnic and muscle glucose metabolism: evidence for a defect in hepatic glucokinase activity. , 2000, Diabetes.

[26]  Volker Tresp,et al.  Neural-network models for the blood glucose metabolism of a diabetic , 1999, IEEE Trans. Neural Networks.

[27]  R. B. Gopaluni.  A particle filter approach to identification of nonlinear processes under missing observations , 2008 .

[28]  J. Holst,et al.  Inappropriate suppression of glucagon during OGTT but not during isoglycaemic i.v. glucose infusion contributes to the reduced incretin effect in type 2 diabetes mellitus , 2007, Diabetologia.

[29]  R. DeFronzo.  The Triumvirate: β-Cell, Muscle, Liver: A Collusion Responsible for NIDDM , 1988, Diabetes.

[30]  S.G. Mougiakakou,et al.  Neural Network based Glucose - Insulin Metabolism Models for Children with Type 1 Diabetes , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Development of a physiological model forpatients with type 2 diabetes mellitus , 2010, Proceedings of the 2010 American Control Conference.