The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed-loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data-based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.

中文翻译：

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从治疗历史数据估计 TIDM 患者膳食葡萄糖-胰岛素模型的参数。

膳食对血糖浓度的影响是糖尿病的一个关键问题，因为它的估计在治疗决策中可能非常有用。在 1 型糖尿病 (T1DM) 的情况下，基于自动胰岛素输送的治疗需要一个闭环控制系统来维持血糖正常，即使在餐后状态下也是如此。因此，葡萄糖代谢的数学模型与预测患者的代谢状态有关。此外，饮食习惯是每个人的特点，因此有趣的是，膳食摄入的数学模型允许使用治疗历史数据来个性化患者的血糖状态，即每天测量葡萄糖和记录碳水化合物摄入量和胰岛素供应。因此，在这里，分析了包括进餐影响的葡萄糖代谢模型，以建立基于数据的个性化标准。分析包括参数的敏感性和可识别性，通过粒子群优化和evonorm两种算法解决参数估计问题。结果表明，数学模型可以成为估计患者血糖状态并根据她/他的历史数据对其进行个性化的有用工具。