GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909801895690240 |
|---|---|
| author | Farahmand, Ebrahim Azghan, Reza Rahimi Chatrudi, Nooshin Taheri Ansu-Baidoo, Velarie Yaa Kim, Eric Gudur, Gautham Krishna Malu, Mohit Krueger, Owen Thomaz, Edison Pedrielli, Giulia Turaga, Pavan Ghasemzadeh, Hassan |
| author_facet | Farahmand, Ebrahim Azghan, Reza Rahimi Chatrudi, Nooshin Taheri Ansu-Baidoo, Velarie Yaa Kim, Eric Gudur, Gautham Krishna Malu, Mohit Krueger, Owen Thomaz, Edison Pedrielli, Giulia Turaga, Pavan Ghasemzadeh, Hassan |
| contents | This paper proposes GluMind, a transformer-based multimodal framework designed for continual and long-term blood glucose forecasting. GluMind devises two attention mechanisms, including cross-attention and multi-scale attention, which operate in parallel and deliver accurate predictive performance. Cross-attention effectively integrates blood glucose data with other physiological and behavioral signals such as activity, stress, and heart rate, addressing challenges associated with varying sampling rates and their adverse impacts on robust prediction. Moreover, the multi-scale attention mechanism captures long-range temporal dependencies. To mitigate catastrophic forgetting, GluMind incorporates a knowledge retention technique into the transformer-based forecasting model. The knowledge retention module not only enhances the model's ability to retain prior knowledge but also boosts its overall forecasting performance. We evaluate GluMind on the recently released AIREADI dataset, which contains behavioral and physiological data collected from healthy people, individuals with prediabetes, and those with type 2 diabetes. We examine the performance stability and adaptability of GluMind in learning continuously as new patient cohorts are introduced. Experimental results show that GluMind consistently outperforms other state-of-the-art forecasting models, achieving approximately 15% and 9% improvements in root mean squared error (RMSE) and mean absolute error (MAE), respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting Farahmand, Ebrahim Azghan, Reza Rahimi Chatrudi, Nooshin Taheri Ansu-Baidoo, Velarie Yaa Kim, Eric Gudur, Gautham Krishna Malu, Mohit Krueger, Owen Thomaz, Edison Pedrielli, Giulia Turaga, Pavan Ghasemzadeh, Hassan Machine Learning This paper proposes GluMind, a transformer-based multimodal framework designed for continual and long-term blood glucose forecasting. GluMind devises two attention mechanisms, including cross-attention and multi-scale attention, which operate in parallel and deliver accurate predictive performance. Cross-attention effectively integrates blood glucose data with other physiological and behavioral signals such as activity, stress, and heart rate, addressing challenges associated with varying sampling rates and their adverse impacts on robust prediction. Moreover, the multi-scale attention mechanism captures long-range temporal dependencies. To mitigate catastrophic forgetting, GluMind incorporates a knowledge retention technique into the transformer-based forecasting model. The knowledge retention module not only enhances the model's ability to retain prior knowledge but also boosts its overall forecasting performance. We evaluate GluMind on the recently released AIREADI dataset, which contains behavioral and physiological data collected from healthy people, individuals with prediabetes, and those with type 2 diabetes. We examine the performance stability and adaptability of GluMind in learning continuously as new patient cohorts are introduced. Experimental results show that GluMind consistently outperforms other state-of-the-art forecasting models, achieving approximately 15% and 9% improvements in root mean squared error (RMSE) and mean absolute error (MAE), respectively. |
| title | GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.18457 |