title: Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.
abstract: BACKGROUND
  Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose.
  
  
  METHODS
  A feed-forward NNM was designed for real-time prediction of glucose in patients with diabetes implementing a prediction horizon of 75 min. Inputs to the NNM included CGM values, insulin dosages, metered glucose values, nutritional intake, lifestyle, and emotional factors. Performance of the NNM was assessed in 10 patients not included in the model training set.
  
  
  RESULTS
  The NNM had a root mean squared error of 43.9 mg/dL and a mean absolute difference percentage of 22.1. The NNM routinely overestimates hypoglycemic extremes, which can be attributed to the limited number of hypoglycemic reactions in the model training set. The model predicts 88.6% of normal glucose concentrations (> 70 and < 180 mg/dL), 72.6% of hyperglycemia (≥ 180 mg/dL), and 2.1% of hypoglycemia (≤ 70 mg/dL). Clarke Error Grid Analysis of model predictions indicated that 92.3% of predictions could be regarded as clinically acceptable and not leading to adverse therapeutic direction. Of these predicted values, 62.3% and 30.0% were located within Zones A and B, respectively, of the error grid.
  
  
  CONCLUSIONS
  Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.
authors.affiliation: Department of Bioengineering, University of Toledo, Toledo, Ohio 43606-3390, USA.
authors.forename: Scott M
authors.initials: SM
authors.lastname: Pappada
authors.forename: Brent D
authors.initials: BD
authors.lastname: Cameron
authors.forename: Paul M
authors.initials: PM
authors.lastname: Rosman
authors.forename: Raymond E
authors.initials: RE
authors.lastname: Bourey
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Papadimos
authors.forename: William
authors.initials: W
authors.lastname: Olorunto
authors.forename: Marilyn J
authors.initials: MJ
authors.lastname: Borst
mesh_terms: D001185:Artificial Intelligence; D001786:Blood Glucose / Q000032:analysis*; D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy / Q000523:psychology; D004032:Diet; D006801:Humans; D006943:Hyperglycemia / Q000517:prevention & control; D007003:Hypoglycemia / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use; D008019:Life Style; D017551:Microdialysis; D008954:Models, Biological; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D013315:Stress, Psychological; D013673:Technology Assessment, Biomedical; D013997:Time Factors
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin