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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 >A data driven nonlinear stochastic model for blood glucose dynamics
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A data driven nonlinear stochastic model for blood glucose dynamics

机译：数据驱动的血糖动态非线性随机模型

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摘要

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. (C) 2015 The Authors. Published by Elsevier Ireland Ltd.
机译：适当的血糖动力学数学模型的开发可以改善糖尿病的早期诊断和控制。我们开发了一个随机的非线性二阶微分方程，使用连续葡萄糖监测（CGM）数据来描述血糖浓度对食物摄入的响应。应用变分贝叶斯学习方案通过迭代优化自由能来定义系统参数的数量和值。该模型具有参数的最小顺序和数量，可以成功描述患有和不患有DM的人的血糖动态。该模型说明了潜在的葡萄糖-胰岛素动态过程的非线性和随机性。由于是数据驱动的，它充分利用了可用的CGM数据，并且同时反映了葡萄糖-胰岛素系统的内在特征，而没有对生理机制的详细了解。我们已经表明，某些餐后血糖波动的动力学可以通过先前在文献中看到的简化（线性）模型来描述。全面的分析表明，确定性系统参数属于糖尿病和对照的不同范围。讨论了对临床实践的意义。这是第一项介绍连续数据驱动的非线性随机模型的研究，该模型能够描述DM和非DM轮廓。 （C）2015作者。由Elsevier Ireland Ltd.发布

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