Abstract
Glucose dysregulation in non-diabetic individuals can precede metabolic disease onset by years, yet most glucose prediction models target diabetic populations with structured insulin records. This article presents Patch Time-Aware Cross-Attention (Patch-TACA), a multimodal transformer-based framework for long-term blood glucose forecasting in healthy individuals. The approach combines patch-based self-supervised pretraining with a time-aware cross-attention mechanism to integrate continuous glucose monitoring (CGM) data with physiological and behavioral measures, such as accelerometry, heart rate, electrodermal activity, and meal macronutrients, without requiring explicit resampling of irregularly sampled signals. Patch-TACA is assessed using data from twelve healthy participants across three laboratory sessions. It achieves an RMSE of 14.26 ± 3.48$ mg/dL at the 90-minute prediction horizon, outperforming GlySim and Gluformer baselines. Moreover, employing a self-supervised method for pre-training reduced RMSE by 7.9% compared to training with random weight initialization, and hyperglycemia prediction accuracy reached 93.9%. These results demonstrate that multimodal sensor fusion with self-supervised learning enables accurate long-horizon glucose forecasting in non-diabetic populations, supporting proactive metabolic health monitoring before clinical disease onset.
Shovito Barua Soumma
Graduate Research Associate
I am a Ph.D. candidate at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.
Asiful Arefeen
Graduate Research Assistant
I am a PhD student at Arizona State University (ASU). I am working under the supervision of Professor Hassan Ghasemzadeh at the Embedded Machine Intelligence Lab (EMIL). I am interested in explainable AI, using AI to generate interventions in digital health, machine learning, passive sensing and mobile health. I received a BS in Electrical & Electronic Engineering from Bangladesh University of Engineering & Technology (BUET) in 2019 and an MS in Biomedical Informatics from Arizona State University in 2023.
Hassan Ghasemzadeh
Director
Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).