S. De Carli

Meta-Learning in Biologically Informed Recurrent Neural Networks for Personalized System Identification of Glucose--Insulin Dynamics

This work embeds prior physiological knowledge about the glucose–insulin relation in the training procedure of RNNs, following the framework of knowledge-guided learning (KGL), and employs model-agnostic meta-learning (MAML) to deal with interpatient variability.