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首页> 外文会议>IEEE Engineering in Medicine and Biology Society >Neural Network based Glucose 驴 Insulin Metabolism Models for Children with Type 1 Diabetes
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Neural Network based Glucose 驴 Insulin Metabolism Models for Children with Type 1 Diabetes

机译：基于神经网络的葡萄糖α患儿糖尿病儿童胰岛素代谢模型

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In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN
机译：本文介绍了两种模拟糖尿病型糖尿病儿童葡萄糖 - 胰岛素代谢模拟模型。该模型基于组合使用隔间模型（CMS）和人工神经网络（NNS）。存储在数据库中的1型糖尿病的儿童的数据已被用作模型的输入。数据来自四种患有1型糖尿病的儿童，含有关于从连续葡萄糖监测系统，胰岛素摄入和食物摄入的葡萄糖水平的信息以及相应的时间。估计胰岛素对血浆胰岛素浓度对肠道血糖进入血液葡萄糖的影响，估计胰岛素对肠道血液的影响。 CMS的输出以及先前的葡萄糖测量馈送到NN，其提供葡萄糖值的短期预测。出于比较原因，已经测试了两种不同的NN架构：用具有自适应学习速率和动量的背传播算法培训的前馈NN（FFNN），以及用实时复发学习训练（RTRL）训练的反复NN（RNN）。（RTRL ） 算法。结果表明，通过使用RNN可以实现最佳预测性能

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