A Glucose Model Based on Support Vector Regression for the Prediction of Hypoglycemic Events Under Free-Living Conditions
A Glucose Model Based on Support Vector Regression for the Prediction of Hypoglycemic Events Under Free-Living Conditions
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DOI：
10.1089/dia.2012.0285
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发表时间：
2013-08-01
影响因子：
5.4
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.
背景：低血糖事件的预防在胰岛素治疗的糖尿病的日常管理中至关重要。使用皮下（s.c.）葡萄糖浓度可能对该方向有显著贡献。文献表明，尽管最近的血糖曲线是低血糖症的重要预测因素，但患者的整体背景极大地影响了其准确估计。本研究的目的是评估支持向量回归机（SVR）的性能。材料和方法：我们扩展了我们的SVR模型，以分别预测睡眠期间的夜间事件和非夜间事件（即，日间的）超过30分钟和60分钟的视野，使用关于最近的葡萄糖曲线、膳食、胰岛素摄入和身体活动的信息，用于70 mg/dL的低血糖阈值。我们还在此介绍了其他变量解释由于先前的低血糖、运动和睡眠引起的复发性夜间低血糖。SVR预测进行了比较，从其他两个机器学习techniques.Results：该方法进行评估的数据集上的15例1型糖尿病患者的自由生活条件。预测夜间低血糖事件的灵敏度为94%，时间滞后分别为5.43分钟和4.57分钟。至于昼夜事件，当不考虑身体活动，灵敏度分别为92%和96%，为30分钟和60分钟的地平线，两个时间滞后小于5分钟。然而，当这样的信息，昼夜灵敏度分别下降了8%和3%。夜间和昼夜预测显示出高（>90%）precision.Conclusions：结果表明，低血糖预测使用SVR可以准确，并在大多数昼夜和夜间的情况下，与其他技术相比，表现更好。建议根据输入变量和结果解释，对夜间和白天的低血糖预测问题进行不同的处理。
Background: The prevention of hypoglycemic events is of paramount importance in the daily management of insulin-treated diabetes. The use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly toward this direction. The literature suggests that, although the recent glucose profile is a prominent predictor of hypoglycemia, the overall patient's context greatly impacts its accurate estimation. The objective of this study is to evaluate the performance of a support vector for regression (SVR) s.c. glucose method on hypoglycemia prediction.Materials and Methods: We extend our SVR model to predict separately the nocturnal events during sleep and the non-nocturnal (i.e., diurnal) ones over 30-min and 60-min horizons using information on recent glucose profile, meals, insulin intake, and physical activities for a hypoglycemic threshold of 70 mg/dL. We also introduce herein additional variables accounting for recurrent nocturnal hypoglycemia due to antecedent hypoglycemia, exercise, and sleep. SVR predictions are compared with those from two other machine learning techniques.Results: The method is assessed on a dataset of 15 patients with type 1 diabetes under free-living conditions. Nocturnal hypoglycemic events are predicted with 94% sensitivity for both horizons and with time lags of 5.43 min and 4.57 min, respectively. As concerns the diurnal events, when physical activities are not considered, the sensitivity is 92% and 96% for a 30-min and 60-min horizon, respectively, with both time lags being less than 5 min. However, when such information is introduced, the diurnal sensitivity decreases by 8% and 3%, respectively. Both nocturnal and diurnal predictions show a high (>90%) precision.Conclusions: Results suggest that hypoglycemia prediction using SVR can be accurate and performs better in most diurnal and nocturnal cases compared with other techniques. It is advised that the problem of hypoglycemia prediction should be handled differently for nocturnal and diurnal periods as regards input variables and interpretation of results.