PREDICTING AND PREVENTING HYPOGLYCEMIA IN PATIENTS HAVING TYPE 1 DIABETES DURING PERIODS OF INCOGNIZANCE USING BIG DATA ANALYTICS AND DECISION THEORETIC ANALYSIS

摘要

Disclosed are techniques for supporting glucoregulatory management decisions using a decision support recommender to predict an occurrence of a hypoglycemic event in an incognizant living subject. Glucose management data is obtained from a living subject. It is processed as prescribed by a feature extractor to generate a set of glucoregulatory feature values. The glucoregulatory feature values are applied as prescribed by the feature extractor to a hypoglycemia prediction model. The hypoglycemia prediction model is formulated to predict the occurrence of a hypoglycemic event during a period of incognizance of the living subject. A glucoregulatory management recommendation is generated as prescribed by the decision support recommender if the hypoglycemia prediction model predicts the occurrence of a hypoglycemic event during the period of incognizance.