Document Detail

Multivariate Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes Patients Based on Support Vector Regression.
MedLine Citation:
PMID:  23008265     Owner:  NLM     Status:  Publisher    
Data-driven techniques have recently drawn significant interest in the predictive modeling of subcutaneous (s.c.) glucose concentration in type 1 diabetes. In this study, the s.c. glucose prediction is treated as a multivariate regression problem, which is addressed using support vector regression (SVR). The proposed method is based on variables concerning: (i) the s.c. glucose profile, (ii) the plasma insulin concentration, (iii) the appearance of meal-derived glucose in the systemic circulation, and (iv) the energy expenditure during physical activities. Six cases corresponding to different combinations of the aforementioned variables are used to investigate the influence of the input on the daily glucose prediction. The proposed method is evaluated using a dataset of 27 patients in free-living conditions. 10-fold cross validation is applied to each dataset individually to both optimize and test the SVR model. In the case where all the input variables are considered, the average prediction errors are 5.21, 6.03, 7.14 and 7.62 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively. The results clearly indicate that the availability of multivariable data and their effective combination can significantly increase the accuracy of both short-term and long-term predictions.
E Georga; V C Protopappas; D Ardigo; M Marina; I Zavaroni; D Polyzos; D Fotiadis
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-19
Journal Detail:
Title:  IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society     Volume:  -     ISSN:  1558-0032     ISO Abbreviation:  IEEE Trans Inf Technol Biomed     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-9-25     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9712259     Medline TA:  IEEE Trans Inf Technol Biomed     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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