Abstract
Blood glucose prediction is important for managing diabetes, preventing hypoglycemia, optimizing insulin therapy, and improving the quality of life for people with diabetes. Because of the continuous glucose monitoring technique, the prediction models can be trained on the patient’s historical blood glucose data in time series. In order to learn the seasonality and trend of the blood glucose data, we introduce a seasonal trend integrated predictor (STIP). Especially for the seasonality, the local and global patterns are captured by embedding and convolutions. The experimental results on different prediction methods indicate the performance of the introduced method.
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Acknowledgement
This work is partially supported by National Key R &D Program of China (No. 2022YFE0208000, 2021YFE204500, 2021YFC3340601), National Natural Science Foundation of China (No. 61972286), the Shanghai Science and Technology Development Funds (No. 22410713200, 20ZR1460500), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100), and Shanghai Key Lab of Vehicle Aerodynamics and Vehicle Thermal Management Systems, and the Fundamental Research Funds for the Central Universities.
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Rao, W. et al. (2023). STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_30
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DOI: https://doi.org/10.1007/978-3-031-46677-9_30
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