Predicting Human Subcutaneous Glucose Concentration in Real Time: A Universal Data-Driven Approach
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2011
- Accession Number
- ADA571031
Entities
People
- Jaques Reifman
- Robert A. Vigersky
- Srinivasan Rajaraman
- W. K. Ward
- Yinghui Lu
Organizations
- United States Army Medical Research and Development Command