Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer

Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer

More than 14% of the world’s population suffered from diabetes mellitus in 2022. This metabolic condition is defined by increased blood glucose concentrations. Among the different types of diabetes, type 1 diabetes, caused by a lack of insulin secretion, is particularly challenging to treat. In this...

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Bibliographic Details
Main Authors: Antonio J. Rodriguez-Almeida, Carmelo Betancort, Ana M. Wägner, Gustavo M. Callico, Himar Fabelo, on behalf of the WARIFA Consortium
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Sensors
Subjects:
glucose prediction
transformers
artificial intelligence
explainable AI
deep learning
personalized medicine
Online Access:https://www.mdpi.com/1424-8220/25/15/4647
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