The authors propose a Hypoglycemic Expert Query Parametric Estimation (H-EQPE) model and a Linear Checkpoint (L-Checkpoint) algorithm to detect hypoglycemia of diabetes patients. The proposed approach combines the strengths of both domain-knowledge-based and machine-learning-based approaches to learn the optimal decision parameter over time series for monitoring the symptoms, in which the objective function (i.e., the maximal number of detections of hypoglycemia) is dependent on the optimal time point from which the parameter is learned. To evaluate the approach, the authors conducted an experiment on a dataset from the Diabetes Research in Children Network group. The L-Checkpoint algorithm learned the optimal monitoring decision parameter, 99 mg/dL, and achieved the maximal number of detections of hypoglycemic symptoms. The experiment shows that the proposed approach produces the results that are superior to those of the domain-knowledge-based and the machine-learning-based approaches, resulting in a 99.2% accuracy, 100% sensitivity, and 98.8% specificity.
Optimal time-point of response assessment for predicting survival is associated with tumor burden in hepatocellular carcinoma receiving repeated transarterial chemoembolization
