GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting

• URL: http://arxiv.org/abs/2509.18457v1
• Date: Mon, 22 Sep 2025 22:27:58 GMT
• Title: GluMind: Multimodal Parallel Attention and Knowledge Retention for Robust Cross-Population Blood Glucose Forecasting
• Authors: Ebrahim Farahmand, Reza Rahimi Azghan, Nooshin Taheri Chatrudi, Velarie Yaa Ansu-Baidoo, Eric Kim, Gautham Krishna Gudur, Mohit Malu, Owen Krueger, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh,
• Abstract summary: GluMind is a transformer-based multimodal framework for blood glucose forecasting.<n>Cross-attention and multi-scale attention operate in parallel and deliver accurate predictive performance.
• Score: 3.7514590842486295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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