Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. For technical support, please contact us at We apologize for any inconvenience.
By Topic

A Glucose-Specific Metric to Assess Predictors and Identify Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Del Favero, S. ; Dept. of Inf. Eng., Univ. of Padova, Padova, Italy ; Facchinetti, A. ; Cobelli, C.

In diabetes, the mean square error (MSE) metric is extensively used for assessing glucose prediction methods and identifying glucose models. One limitation of this metric is that, by equally treating errors in hypo-, eu-, and hyperglycemia, it is not able to weight the different clinical impact of errors in these three situations. In this paper, we propose a new cost function, which overcomes this limitation and can be used in place of MSE for several scopes, in particular for assessing the quality of glucose predictors and identifying glucose models. The new metric called glucose-specific MSE (gMSE) modifies MSE with a Clark error grid inspired penalty function, which penalizes overestimation in hypoglycemia and underestimation in hyperglycemia, i.e., the most harmful conditions on a clinical perspective. From a mathematical point of view, gMSE retains sensitivity of MSE and inherits some of its important mathematical features, in particular it has no local minima, simplifying the optimization. This makes it suitable for model identification purposes also. First, the goodness of it is demonstrated by means of three experiments, designed ad hoc to evidence its sensitivity to accuracy, precision, and distortion in glucose predictions. Second, a prediction assessment problem is presented, in which two real prediction profiles are compared. Results show that the MSE chooses the worst clinical situation, while gMSE correctly selects the situation with less clinical risk. Finally, we also demonstrate that models identified minimizing gMSE are more accurate in potentially harmful situations (hypo- and hyperglycemia) than those obtained by MSE.

Published in:

Biomedical Engineering, IEEE Transactions on  (Volume:59 ,  Issue: 5 )