Bayesian combination of multiple plasma glucose predictors.
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.
Dept. Automatic Control, Lund University, PO Box 118, SE22100 Lund Sweden. Fredrik.Stahl@control.lth.se
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Stahl
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Johansson
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Renard
D000465:Algorithms; D001499:Bayes Theorem; D001786:Blood Glucose / Q000032:analysis*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood; D006801:Humans
D001786:Blood Glucose