论文标题
研究1型糖尿病儿童的短期个性化葡萄糖预测模型
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
论文作者
论文摘要
糖尿病的研究，尤其是在构建数据驱动模型以预测未来葡萄糖值时，数据受到数据的敏感性的阻碍。由于研究人员之间的研究之间没有相同的数据，因此很难评估进度。本文旨在比较该领域中最有希望的算法，即馈电神经网络（FFNN），长期短期记忆（LSTM）复发性神经网络，极端学习机器（ELM），支持矢量回归（SVR）和高斯流程（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个虚拟儿童人群的培训，以预测30分钟的预测范围。使用均方根误差（RMSE）和连续的葡萄糖 - 错误网格分析（CG-EGA）评估模型的性能。尽管大多数模型最终都具有低RMSE，但具有点产生核（GP-DP）的GP模型（在葡萄糖预测的背景下是一种新颖的用法）最低。尽管具有良好的RMSE值，但我们表明这些模型不一定表现出通过CG-EGA衡量的良好临床可接受性。只有LSTM，SVR和GP-DP模型具有总体上可接受的结果，每个结果在一个血糖区域之一中表现最佳。
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.
