Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique
Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique
复制标题
使用连续血糖监测数据和分类技术预测日间低血糖事件
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Sung
中科院分区：
文献类型：
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作者：
Miyeon Jung;You;Sang;Sung
应准确预测日间低血糖，以实现血糖正常并避免灾难性情况。低血糖症是一种血糖水平异常低的疾病，分为日间低血糖症和夜间低血糖症。许多低血糖预防研究涉及夜间低血糖。在本文中，我们提出了新的预测变量来预测白天低血糖症的连续血糖监测（CGM）数据。我们采用分类和回归树（CART）作为预测方法。我们的预测模型的自变量是决策点BG从峰值和绝对水平的下降率。评估结果表明，我们的模型能够提前15分钟检测到近80%的低血糖事件，这高于现有的方法具有类似的条件。该方法可以实现实时预测，也可以嵌入到血糖监测设备中。
Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, we propose new predictor variables to predict daytime hypoglycemia using continuous glucose monitoring (CGM) data. We apply classification and regression tree (CART) as a prediction method. The independent variables of our prediction model are the rate of decrease from a peak and absolute level of the BG at the decision point. The evaluation results showed that our model was able to detect almost 80% of hypoglycemic events 15 min in advance, which was higher than the existing methods with similar conditions. The proposed method might achieve a real-time prediction as well as can be embedded into BG monitoring device.