Journal of Shandong University (Health Sciences) ›› 2025, Vol. 63 ›› Issue (8): 41-50.doi: 10.6040/j.issn.1671-7554.0.2025.0511

• Clinical Research • Previous Articles    

Blood glucose concentration prediction method for type 1 diabetes mellitus based on multi-modal cross-attention mechanism fusion

WANG Mengxing1, XUE Fuzhong1,2,3, YANG Fan1,2,3   

  1. 1. Department of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. National Institute of Health and Medical Big Data, Jinan 250003, Shandong, China;
    3. Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
  • Published:2025-08-25

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Abstract: Objective To develop a blood glucose concentration prediction model for patients with type 1 diabetes mellitus(T1DM)by integrating multimodal information from flash glucose monitoring(FGM)data and structured electronic health records(EHR), so as to address the limitations of traditional unimodal models in capturing complex glucose fluctuation patterns and provide data support for personalized glycemic control strategies and early risk warning in clinical practice. Methods Based on the T1DiabetesGranada dataset, the study integrated multimodal features including FGM data, biochemical test indicators, demographic information, and diagnostic codes to construct a multimodal temporal prediction model, XCLA-Net. The model employed a one-dimensional convolutional neural network(1D-CNN)to extract short-term dynamic features of the glucose sequence, combined a long short-term memory(LSTM)network to capture long-term temporal dependencies, incorporated a cross-attention mechanism for multimodal semantic alignment, and introduced a self-normalizing neural network(SNN)to enhance the stability of the fused representations. Results XCLA-Net significantly outperformed multiple baseline models in terms of error metrics such as root mean square error(RMSE), mean absolute error(MAE), and mean absolute percentage error(MAPE). The MAPE values for 1-hour and 3-hour prediction tasks were 19.64% and 37.81%, respectively, indicating strong predictive accuracy across different temporal scales. Clarke error grid analysis showed that the majority of prediction points fell within Zone A, reflecting good clinical consistency. Ablation experiments confirm the critical roles of the cross-attention mechanism, 1D-CNN, and LSTM in enhancing model performance. Conclusion The proposed XCLA-Net model effectively improves the accuracy and stability of blood glucose prediction through multimodal data fusion and temporal modeling. It demonstrates favorable clinical interpretability and practical value, providing reliable support for personalized glycemic management and early risk prediction in patients with T1DM.

Key words: Type 1 diabetes mellitus, Multimodal data fusion, Cross-attention mechanism, Blood glucose prediction, Time series modeling

CLC Number: 

  • R587.1
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