Short-Term Blood Glucose Prediction from Event-Driven Records Using Pharmacokinetic Feature Engineering in Insulin-Dependent Diabetes Mellitus (IDDM)

Background: Short-term blood glucose prediction usually relies on continuous glucose monitoring (CGM) data sampled every 5 min, which are often unavailable in outpatient care. This study developed a computational workflow that transforms event-driven records into physiologically informed features for 30 min glucose prediction in insulin-dependent diabetes mellitus (IDDM). Methods: Records of 300 virtual patients (30 days, 30 min resolution), simulated from real outpatient glucose measurements and including glucose readings, insulin doses, and carbohydrate intake, were converted into a seven-dimensional representation. Pharmacokinetic decay functions encoded active rapid- and long-acting insulin and carbohydrate absorption, plus the glucose rate of change and a circular time-of-day encoding. The representation was evaluated with artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting (XGBoost) regression on 345,360 training and 86,340 test samples. Results: All algorithms reached a root mean square error (RMSE) of 12.04–12.28 mg/dL and R2 of 0.9845–0.9851, and Clarke Error Grid Analysis placed 93.7–93.9% of predictions in Zone A. Conclusions: The convergence of the four algorithms suggests that predictive performance depends mainly on the feature representation. The workflow offers a reproducible and interpretable basis for glucose prediction without high-frequency CGM data, pending validation on real patient records.