Predicting Human Subcutaneous Glucose Concentration in Real Time: A Universal Data-Driven Approach
ARMY MEDICAL RESEARCH AND MATERIEL COMMAND FORT DETRICK MD TELEMEDICINE AND ADVANCED TECH RESEARCH CENTER
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Continuous glucose monitoring CGM devices measure and record a patients 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 individuals data can be used to predict the glucose levels of any other individual in real time.