Neural Network-Based Real-Time Prediction of Glucose in Patients with Insulin-Dependent Diabetes

Title

Neural Network-Based Real-Time Prediction of Glucose in Patients with Insulin-Dependent Diabetes

Creator

Pappada S M; Cameron B D; Rosman P M; Bourey R E; Papadimos T J; Olorunto W; Borst M J

Publisher

Diabetes Technology & Therapeutics

Date

2011
2011-02

Description

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 <180mg/dL), 72.6% of hyperglycemia (>= 180mg/dL), and 2.1% of hypoglycemia (<= 70mg/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.

Subject

blood-glucose; circadian rhythmicity; cortisol; depression; Endocrinology & Metabolism; glycemic control; mellitus; resistance; stress

Format

Journal Article

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Rights

Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).

Pages

135-141

Issue

2

Volume

13

Citation

Pappada S M; Cameron B D; Rosman P M; Bourey R E; Papadimos T J; Olorunto W; Borst M J, “Neural Network-Based Real-Time Prediction of Glucose in Patients with Insulin-Dependent Diabetes,” NEOMED Bibliography Database, accessed January 27, 2021, https://neomed.omeka.net/items/show/6712.

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