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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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4647 |
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| author | Antonio J. Rodriguez-Almeida Carmelo Betancort Ana M. Wägner Gustavo M. Callico Himar Fabelo on behalf of the WARIFA Consortium |
| author_facet | Antonio J. Rodriguez-Almeida Carmelo Betancort Ana M. Wägner Gustavo M. Callico Himar Fabelo on behalf of the WARIFA Consortium |
| author_sort | Antonio J. Rodriguez-Almeida |
| collection | DOAJ |
| description | 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 regard, automatic glucose level estimation implements Continuous Glucose Monitoring (CGM) devices, showing positive therapeutic outcomes. AI-based glucose prediction has commonly followed a deterministic approach, usually with a lack of interpretability. Therefore, these AI-based methods do not provide enough information in critical decision-making scenarios, like in the medical field. This work intends to provide accurate, interpretable, and personalized glucose prediction using the Temporal Fusion Transformer (TFT), and also includes an uncertainty estimation. The TFT was trained using two databases, an in-house-collected dataset and the OhioT1DM dataset, commonly used for glucose forecasting benchmarking. For both datasets, the set of input features to train the model was varied to assess their impact on model interpretability and prediction performance. Models were evaluated using common prediction metrics, diabetes-specific metrics, uncertainty estimation, and interpretability of the model, including feature importance and attention. The obtained results showed that TFT outperforms existing methods in terms of RMSE by at least 13% for both datasets. |
| format | Article |
| id | doaj-art-68e3c8eee4fb4c7783d3a586b6b03126 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-68e3c8eee4fb4c7783d3a586b6b031262025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-07-012515464710.3390/s25154647Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion TransformerAntonio J. Rodriguez-Almeida0Carmelo Betancort1Ana M. Wägner2Gustavo M. Callico3Himar Fabelo4on behalf of the WARIFA ConsortiumInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas de Gran Canaria, SpainEndocrinology and Nutrition Department, Complejo Hospitalario Universitario Insular Materno-Infantil, CHUIMI, 35016 Las Palmas de Gran Canaria, SpainEndocrinology and Nutrition Department, Complejo Hospitalario Universitario Insular Materno-Infantil, CHUIMI, 35016 Las Palmas de Gran Canaria, SpainInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas de Gran Canaria, SpainInstitute for Applied Microelectronics, University of Las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas de Gran Canaria, SpainMore 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 regard, automatic glucose level estimation implements Continuous Glucose Monitoring (CGM) devices, showing positive therapeutic outcomes. AI-based glucose prediction has commonly followed a deterministic approach, usually with a lack of interpretability. Therefore, these AI-based methods do not provide enough information in critical decision-making scenarios, like in the medical field. This work intends to provide accurate, interpretable, and personalized glucose prediction using the Temporal Fusion Transformer (TFT), and also includes an uncertainty estimation. The TFT was trained using two databases, an in-house-collected dataset and the OhioT1DM dataset, commonly used for glucose forecasting benchmarking. For both datasets, the set of input features to train the model was varied to assess their impact on model interpretability and prediction performance. Models were evaluated using common prediction metrics, diabetes-specific metrics, uncertainty estimation, and interpretability of the model, including feature importance and attention. The obtained results showed that TFT outperforms existing methods in terms of RMSE by at least 13% for both datasets.https://www.mdpi.com/1424-8220/25/15/4647glucose predictiontransformersartificial intelligenceexplainable AIdeep learningpersonalized medicine |
| spellingShingle | Antonio J. Rodriguez-Almeida Carmelo Betancort Ana M. Wägner Gustavo M. Callico Himar Fabelo on behalf of the WARIFA Consortium Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer Sensors glucose prediction transformers artificial intelligence explainable AI deep learning personalized medicine |
| title | Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer |
| title_full | Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer |
| title_fullStr | Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer |
| title_full_unstemmed | Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer |
| title_short | Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer |
| title_sort | incorporating uncertainty estimation and interpretability in personalized glucose prediction using the temporal fusion transformer |
| topic | glucose prediction transformers artificial intelligence explainable AI deep learning personalized medicine |
| url | https://www.mdpi.com/1424-8220/25/15/4647 |
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