GlucoNet applies feature decomposition and knowledge distillation to a transformer model to forecast blood glucose levels from irregular multimodal data, reporting accuracy gains and model compression on data from 12 T1D participants.
A novel approach for the prediction of glucose concen- tration in type 1 diabetes ahead in time through arima and differential evolution
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Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
GlucoNet applies feature decomposition and knowledge distillation to a transformer model to forecast blood glucose levels from irregular multimodal data, reporting accuracy gains and model compression on data from 12 T1D participants.