Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;2011:7945-8.
doi: 10.1109/IEMBS.2011.6091959.

Predicting Human Subcutaneous Glucose Concentration in Real Time: A Universal Data-Driven Approach

Affiliations

Predicting Human Subcutaneous Glucose Concentration in Real Time: A Universal Data-Driven Approach

Yinghui Lu et al. Conf Proc IEEE Eng Med Biol Soc. .

Abstract

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.

Similar articles

See all similar articles

Cited by 2 articles

Publication types

LinkOut - more resources

Feedback