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Updated: Sep 11, 2025

Homogeneous Time-resolved Förster Resonance Energy Transfer-based Assay for Detection of Insulin Secretion

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

Antonio J Rodriguez-Almeida¹, Carmelo Betancort², Ana M Wägner^2,3

¹Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas de Gran Canaria, Spain.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces an interpretable AI model for accurate glucose prediction in type 1 diabetes management. The Temporal Fusion Transformer (TFT) improves continuous glucose monitoring (CGM) by providing reliable, personalized forecasts.

Keywords:

artificial intelligence deep learning explainable AI glucose prediction mHealth personalized medicine transformers

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A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Endocrinology

Background:

Diabetes mellitus affects over 14% of the global population, with type 1 diabetes posing significant treatment challenges due to insulin deficiency.
Continuous Glucose Monitoring (CGM) devices offer therapeutic benefits for automatic glucose level estimation.
Current AI-based glucose prediction models often lack interpretability, hindering critical medical decision-making.

Purpose of the Study:

To develop an accurate, interpretable, and personalized glucose prediction model using the Temporal Fusion Transformer (TFT).
To incorporate uncertainty estimation into AI-based glucose forecasting.
To evaluate the impact of feature selection on model performance and interpretability.

Main Methods:

Trained the Temporal Fusion Transformer (TFT) model on two datasets: an in-house dataset and the OhioT1DM dataset.
Varied input features during training to assess their influence on interpretability and prediction accuracy.
Evaluated model performance using standard prediction metrics, diabetes-specific metrics, and interpretability techniques (feature importance, attention).

Main Results:

The TFT model demonstrated superior performance, outperforming existing methods by at least 13% in Root Mean Square Error (RMSE) on both datasets.
The study successfully provided accurate and interpretable glucose predictions with uncertainty estimation.
Feature engineering significantly impacted model interpretability and predictive performance.

Conclusions:

The developed TFT model offers a significant advancement in interpretable and accurate AI-driven glucose prediction for type 1 diabetes.
This approach enhances the utility of CGM data for personalized diabetes management and clinical decision support.
The findings highlight the potential of interpretable AI in improving therapeutic outcomes for metabolic conditions.