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.

journal_name

Diabetes Technol Ther

authors

Pappada SM,Cameron BD,Rosman PM,Bourey RE,Papadimos TJ,Olorunto W,Borst MJ

doi

10.1089/dia.2010.0104

subject

Has Abstract

pub_date

2011-02-01 00:00:00

pages

135-41

issue

2

eissn

1520-9156

issn

1557-8593

journal_volume

13

pub_type

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