Paper Info
Title
Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events.
Abstract
Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6944708
EMBC
Keywords
Field
DocType
rnn,dst,early warning system,diseases,data fusion techniques,type 1 diabetes,fusion scheme,hyperglycemic events,genetic programming,regression analysis,model fusion methods,sensor augmented pump therapy,medical signal processing,hypoglycemic events,biochemistry,blood glucose levels,dempster-shafer evidential theory,genetic algorithms,sap therapy,output correction,recurrent neural network,multimodel data fusion,autoregressive model,carx,neural nets,adaptive data driven models,patient treatment,sensor fusion
Warning system,Autoregressive model,Computer science,Recurrent neural network,Genetic programming,Sensor fusion,Correlation,Artificial intelligence,Early warning system,Genetic algorithm,Machine learning
Conference
Volume
ISSN
Citations 
2014
1557-170X
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ransford Henry Botwey100.34
Elena Daskalaki200.34
Peter Diem3655.19
Stavroula G Mougiakakou434228.61