Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible.  Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications.  An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. The new model outperforms diabetes experts at predicting blood glucose levels and could be used to anticipate almost a quarter of hypoglycemic events 30 minutes in advance.  Although the corresponding precision is currently just 42%, most false alarms are in near-hypoglycemic regions and therefore patients responding to these hypoglycemia alerts would not be harmed by intervention.
['Kevin Plis', 'Razvan C. Bunescu', 'Cindy Marling', 'Jay H. Shubrook', 'Frank Schwartz']
A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management