STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series

Weixiong Rao, Guangda Yang*, Qinpei Zhao, Yuzhi Liu, Hongming Zhu, Ming Li, Xuefeng Li, Yinjia Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings
EditorsXiaochun Yang, Bin Wang, Heru Suhartanto, Guoren Wang, Jing Jiang, Bing Li, Huaijie Zhu, Ningning Cui
PublisherSpringer
Pages437-450
Number of pages14
ISBN (Electronic)978-3-031-46677-9
ISBN (Print)978-3-031-46676-2
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventInternational Conference on Advanced Data Mining and Applications - Shenyang, China
Duration: 21 Aug 202323 Aug 2023
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14180 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Advanced Data Mining and Applications
Abbreviated titleADMA
Country/TerritoryChina
CityShenyang
Period21/08/202323/08/2023

Keywords

  • blood glucose prediction
  • continuous glucose monitoring
  • convolution
  • seasonality and trend
  • time series

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