Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods
Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods
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DOI：
10.1007/s10916-017-0788-2
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发表时间：
2017-09-01
影响因子：
5.3
通讯作者：
Lanchares, Juan
中科院分区：
文献类型：
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作者：
Hidalgo, J. Ignacio;Colmenar, J. Manuel;Lanchares, Juan
根据胰岛素和食物摄入量预测血糖值是糖尿病患者每天都需要做的一项艰巨的任务。这是必要的，因为重要的是将血糖水平保持在适当的值，以避免疾病的短期和长期并发症。总的来说，人工智能，特别是机器学习技术，已经在建模和预测血糖浓度方面取得了令人振奋的结果。在这项工作中，几种机器学习技术被用于建模和预测血糖浓度，使用连续监测血糖系统测量的值作为输入，以及先前和估计的未来碳水化合物摄入量和胰岛素注射。特别是，我们使用了以下四种技术：遗传编程、随机森林、k近邻和语法进化。我们提出了两种新的用于血糖预测的改进建模算法，即(I)使用优化语法的语法进化的变体，和(Ii)使用碳水化合物和胰岛素动力学的三室模型的基于树的遗传编程的变体。这些预测者是使用来自西班牙一家公立医院的10名患者的数据进行培训和测试的。我们使用Clarke误差网格度量分析我们的实验结果，发现90%的预测是正确的(即Clarke误差类别A和B)，但即使是最好的方法也会产生5%到10%的严重错误(D类)和大约0.5%的非常严重的错误(E类)。我们还提出了一种改进的遗传规划算法，该算法将三室模型结合到符号回归模型中，以创建原始碳水化合物和胰岛素时间序列的平滑时间序列。
Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.