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Found 5 papers, 0 papers with code
Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction
Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons.
Modeling glycemia in humans by means of Grammatical Evolution
Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide.
Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning
People with diabetes have to manage their blood glucose level to keep it within an appropriate range.
Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems
In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series.
A Critical Review of the state-of-the-art on Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
This article compares ten recently proposed neural networks and proposes two ensemble neural network-based models for blood glucose prediction.
