Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models
Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models
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DOI：
10.1007/s11517-015-1263-1
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发表时间：
2015-12-01
影响因子：
3.2
通讯作者：
Fotiadis, Dimitrios I.
中科院分区：
文献类型：
--
作者：
Georga, Eleni I.;Protopappas, Vasilios C.;Fotiadis, Dimitrios I.
1型糖尿病患者的血糖浓度受生物和环境因素的影响，这些因素在患者间具有很大的可变性。本研究的目的是评估从医疗和生活方式自我监测数据中提取的一些特征，以及它们预测个人短期皮下（s.c.）葡萄糖浓度的能力。首先采用随机森林（RF）和RReliefF算法对候选特征集进行排序。然后，通过前向选择程序建立葡萄糖预测模型，其中按重要程度递减的顺序依次添加特征。使用支持向量回归或高斯过程进行预测。该方法在15型糖尿病患者的数据集上进行了验证。s.c.葡萄糖谱随着一天中的时间和血浆胰岛素浓度被系统地高度排列，而食物摄入和身体活动的影响在患者之间差异很大。平均预测误差在少于d/2次迭代（d为特征个数）内收敛。我们的研究结果表明，RF和RReliefF可以找到最具信息量的特征，并可以成功地用于定制葡萄糖模型的输入。
Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their ability to predict the short-term subcutaneous (s.c.) glucose concentration of an individual. Random forests (RF) and RReliefF algorithms are first employed to rank the candidate feature set. Then, a forward selection procedure follows to build a glucose predictive model, where features are sequentially added to it in decreasing order of importance. Predictions are performed using support vector regression or Gaussian processes. The proposed method is validated on a dataset of 15 type diabetics in real-life conditions. The s.c. glucose profile along with time of the day and plasma insulin concentration are systematically highly ranked, while the effect of food intake and physical activity varies considerably among patients. Moreover, the average prediction error converges in less than d/2 iterations (d is the number of features). Our results suggest that RF and RReliefF can find the most informative features and can be successfully used to customize the input of glucose models.