Cappon Giacomo, Vettoretti Martina, Marturano Francesca, Facchinetti Andrea, Sparacino Giovanni
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8512250.
Type 1 diabetes (TID) therapy is based on multiple daily injections of exogenous insulin. The so-called insulin bolus calculators facilitate insulin dose calculation to the patients by implementing a standard formula SF which, besides some patient-related parameters, also considers the current value of blood glucose concentration (BG), normally measured by the patient through a fingerprick device. The recent approval by the U.S. Food and Drug Administration to use the measurements collected by wearable continuous glucose monitoring (CGM) sensors for insulin dosing of fers new perspectives. Indeed, CGM sensors provide real-time information on both glucose concentration and rate of change, currently not considered in the SF. The purpose of this work is to preliminary investigate the possibility of using neural networks (NN)s for the calculation of meal insulin bolus dose exploiting CGM-based information. Using the UVa/Padova TID Simulator, we generated data of 100 subjects in 9-h, single-meal, noise-free scenarios. In particular, for each subject we analyzed different meal conditions in terms of carbohydrate intakes, preprandial BG and glucose rate-of -change. Then, a fully-connected feedforward NN was trained, with the aim of estimating the insulin bolus needed to obtain the best glycemic outcomes according to the blood glucose risk index (BGRI). Preliminary results show that by using the NN to calculate insulin doses lower BGRI values are obtained, on average, compared to the SF. These results encourage further development of the approach and its assessment in more challenging scenarios.
1型糖尿病（TID）的治疗基于每日多次注射外源性胰岛素。所谓的胰岛素大剂量计算器通过实施标准公式SF来帮助患者计算胰岛素剂量，该公式除了一些与患者相关的参数外，还考虑血糖浓度（BG）的当前值，通常由患者通过指尖采血设备进行测量。美国食品药品监督管理局最近批准将可穿戴式连续血糖监测（CGM）传感器收集的测量数据用于胰岛素给药，这提供了新的视角。实际上，CGM传感器可提供有关葡萄糖浓度和变化率的实时信息，而标准公式SF目前并未考虑这些信息。这项工作的目的是初步研究利用基于CGM的信息，使用神经网络（NN）计算餐时胰岛素大剂量的可能性。我们使用弗吉尼亚大学/帕多瓦TID模拟器，在9小时、单餐、无噪声的场景中生成了100名受试者的数据。特别是，对于每个受试者，我们根据碳水化合物摄入量、餐前BG和葡萄糖变化率分析了不同的用餐条件。然后，训练了一个全连接前馈神经网络，目的是根据血糖风险指数（BGRI）估计获得最佳血糖结果所需的胰岛素大剂量。初步结果表明，与标准公式相比，使用神经网络计算胰岛素剂量平均可获得更低的BGRI值。这些结果鼓励进一步开发该方法，并在更具挑战性的场景中对其进行评估。
