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Bayesian Combination of Multiple Plasma Glucose Predictors

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Author

Editor

  • Michael Khoo

Summary, in English

This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined prediction is better, or in par, with the best predictor for each evaluated data set. The results suggest that the outlined method could be a suitable way to improve prediction performance when using multiple predictors, or as a means to reduce the risk associated with definite a priori model selection.

Publishing year

2012

Language

English

Pages

2839-2844

Publication/Series

Proceedings of the 34th Annual Conference of the IEEE EMBS

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Control Engineering

Conference name

34th Annual Conference of the IEEE EMBS

Conference date

2012-08-28

Conference place

San Diego, CA, United States

Status

Published

Project

  • DIAdvisor

Research group

  • LCCC