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Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children

机译：1型糖尿病儿童短期个性化葡萄糖预测模型的研究

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Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions.
机译：糖尿病的研究，特别是在建立数据驱动的模型以预测未来的血糖值方面，受到数据敏感性的阻碍。由于研究人员在两次研究之间不会共享相同的数据，因此很难评估进展。本文旨在比较该领域最有前途的算法，即前馈神经网络（FFNN），长短期记忆（LSTM）递归神经网络，极限学习机（ELM），支持向量回归（SVR）和高斯过程（ GP）。他们是个性化的，并接受了来自1型糖尿病代谢模拟器软件的10个虚拟儿童的训练，以在30分钟的预测范围内预测未来的血糖值。使用均方根误差（RMSE）和连续葡萄糖误差网格分析（CG-EGA）评估模型的性能。尽管大多数模型最终都具有较低的RMSE，但是具有点积核（GP-DP）的GP模型（在葡萄糖预测中的一种新颖用法）具有最低的模型。尽管具有良好的RMSE值，但我们显示，通过CG-EGA测量，模型不一定表现出良好的临床可接受性。只有LSTM，SVR和GP-DP模型具有总体可接受的结果，它们中的每一个在血糖区域之一中表现最佳。

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