|Title:||A dynamic model with structured recurrent neural network to predict glucose-insulin regulation of type 1 diabetes mellitus||Authors:||Huang, H.-P.
|Keywords:||Neural network; Type 1 diabetes||Issue Date:||2010||Journal Volume:||9||Journal Issue:||PART 1||Start page/Pages:||242-247||Source:||IFAC Proceedings Volumes (IFAC-PapersOnline)||Abstract:||
An artificial neural network (ANN) model for the prediction of glucose concentration in a glucose-insulin regulation system for type 1 diabetes mellitus is developed and validated by using the Continuous Glucose Monitoring System (CGMS) data. This network consists of structured framework according to the compartmental structure of the Hovorka-Wilinska model (HWM), and an additional update scheme is also included, which can improve the prediction accuracy whenever new measurements are available. The model is tested on a real case, as well as long term prediction has been carried over an extended time horizon from 30 minutes to 4 hours, and the quality of prediction is assessed by examining the values of the four indexes. For instant, the overall Clarke error grid (CEG) Zone A value is up to 100% for the 30-min-ahead prediction horizon with update. Therefore, for practical purpose, our results indicate that the promising prediction performance can be achieved by our proposed structured recurrent neural network model (SRNNM). ? 2009 IFAC.
|DOI:||10.3182/20100705-3-BE-2011.0084||SDG/Keyword:||Artificial neural network models; Continuous glucose monitoring; Glucose concentration; Long-term prediction; Prediction accuracy; Prediction horizon; Prediction performance; Quality of predictions; Structured recurrent neural networks; Time horizons; Type 1 diabetes; Type 1 diabetes mellitus; Forecasting; Insulin; Process control; Recurrent neural networks; Glucose
|Appears in Collections:||化學工程學系|
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