A meta-learning approach to personalized blood glucose prediction in type 1 diabetes

Author:

Langarica SaúlORCID,Rodriguez-Fernandez Maria,Núñez Felipe,Doyle Francis J.ORCID

Funder

ANID

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference53 articles.

1. Deep personalized glucose level forecasting using attention-based recurrent neural networks;Armandpour,2021

2. Learn2learn: A library for meta-learning research;Arnold,2020

3. Embedding adaptation is still needed for few-shot learning;Arnold,2021

4. Berend, D., Xie, X., Ma, L., Zhou, L., Liu, Y., Xu, C., et al. (2020). Cats Are Not Fish: Deep Learning Testing Calls for Out-Of-Distribution Awareness. In 2020 35th IEEE/ACM international conference on automated software engineering (pp. 1041–1052).

5. Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes;Brown;New England Journal of Medicine,2019

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