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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes
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Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

机译：人工神经网络用于计算机控制和特设1型糖尿病的闭环控制

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摘要

The closed loop control of blood glucose levels might help to reduce many short- and long-term complications of type 1 diabetes. Continuous glucose monitoring and insulin pump systems have facilitated the development of the artificial pancreas. In this paper, artificial neural networks are used for both the identification of patient dynamics and the glycaemic regulation. A subcutaneous glucose measuring system together with a Lispro insulin subcutaneous pump were used to gather clinical data for each patient undergoing treatment, and a corresponding in silico and ad hoc neural network model was derived for each patient to represent their particular glucose-insulin relationship. Based on this nonlinear neural network model, an ad hoc neural network controller was designed to close the feedback loop for glycaemic regulation of the in silico patient. Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.
机译：血糖水平的闭环控制可能有助于减少1型糖尿病的许多短期和长期并发症。连续的葡萄糖监测和胰岛素泵系统促进了人工胰腺的发展。在本文中，人工神经网络可用于识别患者动态和血糖调节。皮下葡萄糖测量系统与Lispro胰岛素皮下泵一起用于收集每位接受治疗的患者的临床数据，并为每位患者获得相应的计算机和自组织神经网络模型，以代表其特定的葡萄糖-胰岛素关系。基于此非线性神经网络模型，设计了一个ad hoc神经网络控制器来关闭反馈回路，以对计算机病患者进行血糖调节。在模拟中为每位患者测试了神经网络模型和控制器，并且获得的结果显示出在食物摄入和可变运动条件下的良好表现。

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