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Abstract：
The paper \"Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions\" (Sensors2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model\'s root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors\' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones.
摘要：
论文“使用胰岛素和碳水化合物的吸收模型和深度倾斜以提高血糖水平预测”（Sensors2021，21，5273）提出了一种新的方法来预测1型糖尿病（T1DM）患者的血糖水平。通过从原始碳水化合物和胰岛素数据建立指数模型来模拟体内的吸收，作者报道,在预测未来一小时的血糖水平时,模型的均方根误差(RMSE)从15.5mg/dL(原始)降低至9.2mg/dL(指数).在这篇评论中，我们证明了那篇论文中使用的实验技术是有缺陷的，使其结果和结论无效。具体来说,在审查了作者的代码之后，我们发现模型验证方案是错误的，即,来自相同时间间隔的训练和测试数据是混合的.这意味着参考论文中报告的RMSE数字没有准确衡量所提出方法的预测能力。我们通过适当隔离训练和测试数据来修复测量技术，我们发现他们的模型实际上比论文中报道的要糟糕得多。事实上,那篇论文中提出的模型似乎没有比预测未来血糖水平与当前水平相同的幼稚模型表现更好。
