Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique
This work addresses the prediction of daytime hypoglycemia for diabetes patients, representing an incremental improvement over prior methods focused on nocturnal hypoglycemia.
The paper tackled the problem of predicting daytime hypoglycemic events using continuous glucose monitoring data and a classification and regression tree method, achieving detection of almost 80% of events 15 minutes in advance, which outperformed existing methods.
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.