Background and Aims
Bolus decision is a difficult task since patients need to estimate the number of carbohydrates they are going to ingest, take into account the past and future circumstances, know the past values of glucose, evaluate if the effect of previously injected insulin has already finished and any other relevant information. In this work, we present and compare a set of methodologies to automate the decision of the insulin bolus, which reduces the number of dangerous predictions.
We combine two different data enrichment techniques based on Markov chains with grammatical evolution engines to generate models of blood glucose, and univariate marginal distribution algorithms and bagging techniques to select the set of models to assemble. Modeling is solved as four symbolic regression problems by Random-Grammatical Evolution.
We report the Clarke’s Error Grid Analysis.The best results are obtained with an ensemble model developed by bagging of 100 models. For a 30 minutes forecasting horizon, all the strategies have similar performance with almost no points in the D and E zones. For the 60, 90 and 120 minutes, the best models are produced by Random-GE and Bagging with both data augmentation techniques, obtaining a 95% of safe predictions,on average.
Results show how the methods improve results from pre-vious works. The proposal uses a simplified preprocessing process of the raw data, we use symbolic regression standard grammars and execution times are low. In future works, we should move a step forward in the forecasting horizon.
Thanks: RTI2018-095180-B-I00 and Fundación Eugenio Rodriguez Pascual