title: Predicting subcutaneous glucose concentration using a latent-variable-based statistical method for type 1 diabetes mellitus.
abstract: BACKGROUND
  Accurate prediction of future glucose concentration for type 1 diabetes mellitus (T1DM) is needed to improve glycemic control and to facilitate proactive management before glucose concentrations reach undesirable concentrations. The availability of frequent glucose measurements, insulin infusion rates, and meal carbohydrate estimates can be used to good advantage to capture important information concerning glucose dynamics.
  
  
  METHODS
  This article evaluates the feasibility of using a latent variable (LV)-based statistical method to model glucose dynamics and to forecast future glucose concentrations for T1DM applications. The prediction models are developed using a proposed LV-based approach and are evaluated for retrospective clinical data from seven individuals with T1DM and for In silico simulations using the Food and Drug Administration-accepted University of Virginia/University of Padova metabolic simulator. This article provides comparisons of the prediction accuracy of the LV-based method with that of a standard modeling alternative. The influence of key design parameters on the performance of the LV-based method is also illustrated.
  
  
  RESULTS
  In general, the LV-based method provided improved prediction accuracy in comparison with conventional autoregressive (AR) models and autoregressive with exogenous input (ARX) models. For larger prediction horizons (≥30 min), the LV-based model with exogenous inputs achieved the best prediction performance based on a paired t-test (α = 0.05).
  
  
  CONCLUSIONS
  The LV-based method resulted in models whose glucose prediction accuracy was as least as good as the accuracies of standard AR/ARX models and a simple model-free approach. Furthermore, the new approach is less sensitive to changing conditions and the effect of key design parameters.
authors: Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California 93106-5080, USA.
authors: Chunhui
authors: C
authors: Zhao
authors: Eyal
authors: E
authors: Dassau
authors: Lois
authors: L
authors: Jovanovič
authors: Howard C
authors: HC
authors: Zisser
authors: Francis J
authors: FJ
authors: Doyle
authors: Dale E
authors: DE
authors: Seborg
mesh_terms: D000328:Adult; D001786:Blood Glucose / Q000187:drug effects / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000175:diagnosis / Q000188:drug therapy; D004040:Dietary Carbohydrates / Q000378:metabolism; D004334:Drug Administration Schedule; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D007328:Insulin / Q000008:administration & dosage; D007332:Insulin Infusion Systems; D016018:Least-Squares Analysis; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D019397:Pancreas, Artificial; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D040521:Subcutaneous Tissue / Q000187:drug effects / Q000378:metabolism*; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D001786:Blood Glucose; D004040:Dietary Carbohydrates; D007004:Hypoglycemic Agents; D007328:Insulin