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- W4387251310 abstract "Accurately predicting blood glucose(BG) levels is crucial for effective management of type 1 diabetes. This paper proposes a hybrid neural network model for blood glucose prediction algorithm, which combines convolutional neural network (CNN) and Transformer. The algorithm takes into account various parameters, including patients’ historical blood glucose data, carbohydrate intake, and insulin injection amount, and integrates CNN and Transformer networks for comprehensive analysis. Specifically, the CNN is employed to extract local features from the input data, while the Transformer network captures global dependencies and contextual information in the time series data. The experiments were conducted using data generated by the UVA/Padova simulator, simulating blood glucose data for 10 adult individuals. Evaluation results demonstrate that the algorithm achieves average MAPE values of 2.41%, 2.89%, and 3.19% for the 15minute, 30-minute, and 60-minute predictions, respectively, with average RMSE values of 6.75, 10.51, and 15.98, indicating low prediction errors and good accuracy." @default.
- W4387251310 created "2023-10-03" @default.
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- W4387251310 date "2023-08-12" @default.
- W4387251310 modified "2023-10-06" @default.
- W4387251310 title "Multi-parameter Blood Glucose Prediction Algorithm for Type 1 Diabetes Based on Hybrid Neural Network Deep Learning Technique" @default.
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- W4387251310 doi "https://doi.org/10.1109/ccis59572.2023.10263038" @default.
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