Predicting human subcutaneous glucose concentration in real time: A universal data-driven approach

Continuous glucose monitoring (CGM) devices measure and record a patient's subcutaneous glucose concentration as frequently as every minute for up to several days. When coupled with data-driven mathematical models, CGM data can be used for short-term prediction of glucose concentrations in diabetic patients. In this study, we present a real-time implementation of a previously developed offline data-driven algorithm. The implementation consists of a Kalman filter for real-time filtering of CGM data and a data-driven autoregressive model for prediction. Results based on CGM data from 3 different studies involving 34 type 1 and 2 diabetic patients suggest that the proposed real-time approach can yield ~10-min-ahead predictions with clinically acceptable accuracy and, hence, could be useful as a tool for warning against impending glucose deregulation episodes. The results further support the feasibility of "universal" glucose prediction models, where an offline-developed model based on one individual's data can be used to predict the glucose levels of any other individual in real time.