کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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718542 | 892261 | 2010 | 6 صفحه PDF | دانلود رایگان |
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).
Journal: IFAC Proceedings Volumes - Volume 43, Issue 5, 2010, Pages 242-247