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

doi:10.1007/978-3-031-46677-9_30

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings
Editors	Xiaochun Yang, Bin Wang, Heru Suhartanto, Guoren Wang, Jing Jiang, Bing Li, Huaijie Zhu, Ningning Cui
Publisher	Springer
Pages	437-450
Number of pages	14
ISBN (Electronic)	978-3-031-46677-9
ISBN (Print)	978-3-031-46676-2
DOIs	https://doi.org/10.1007/978-3-031-46677-9_30
Publication status	Published - 2023
MoE publication type	A4 Conference publication
Event	International Conference on Advanced Data Mining and Applications - Shenyang, China Duration: 21 Aug 2023 → 23 Aug 2023 Conference number: 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14180 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Advanced Data Mining and Applications
Abbreviated title	ADMA
Country/Territory	China
City	Shenyang
Period	21/08/2023 → 23/08/2023

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-46677-9_30

Cite this

Rao, W., Yang, G., Zhao, Q., Liu, Y., Zhu, H., Li, M., Li, X., & Zhang, Y. (2023). STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. In X. Yang, B. Wang, H. Suhartanto, G. Wang, J. Jiang, B. Li, H. Zhu, & N. Cui (Eds.), Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings (pp. 437-450). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14180 LNAI). Springer. https://doi.org/10.1007/978-3-031-46677-9_30

Rao, Weixiong ; Yang, Guangda ; Zhao, Qinpei et al. / STIP : A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. editor / Xiaochun Yang ; Bin Wang ; Heru Suhartanto ; Guoren Wang ; Jing Jiang ; Bing Li ; Huaijie Zhu ; Ningning Cui. Springer, 2023. pp. 437-450 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6dbc448e09e4444aa14df8d2039742f9,

title = "STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series",

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{\textquoteright}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.",

keywords = "blood glucose prediction, continuous glucose monitoring, convolution, seasonality and trend, time series",

author = "Weixiong Rao and Guangda Yang and Qinpei Zhao and Yuzhi Liu and Hongming Zhu and Ming Li and Xuefeng Li and Yinjia Zhang",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; International Conference on Advanced Data Mining and Applications, ADMA ; Conference date: 21-08-2023 Through 23-08-2023",

year = "2023",

doi = "10.1007/978-3-031-46677-9_30",

language = "English",

isbn = "978-3-031-46676-2",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "437--450",

editor = "Xiaochun Yang and Bin Wang and Heru Suhartanto and Guoren Wang and Jing Jiang and Bing Li and Huaijie Zhu and Ningning Cui",

booktitle = "Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings",

address = "Germany",

}

Rao, W, Yang, G, Zhao, Q, Liu, Y, Zhu, H, Li, M, Li, X & Zhang, Y 2023, STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. in X Yang, B Wang, H Suhartanto, G Wang, J Jiang, B Li, H Zhu & N Cui (eds), Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14180 LNAI, Springer, pp. 437-450, International Conference on Advanced Data Mining and Applications, Shenyang, China, 21/08/2023. https://doi.org/10.1007/978-3-031-46677-9_30

STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. / Rao, Weixiong; Yang, Guangda; Zhao, Qinpei et al.
Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. ed. / Xiaochun Yang; Bin Wang; Heru Suhartanto; Guoren Wang; Jing Jiang; Bing Li; Huaijie Zhu; Ningning Cui. Springer, 2023. p. 437-450 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14180 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - STIP

T2 - International Conference on Advanced Data Mining and Applications

AU - Rao, Weixiong

AU - Yang, Guangda

AU - Zhao, Qinpei

AU - Liu, Yuzhi

AU - Zhu, Hongming

AU - Li, Ming

AU - Li, Xuefeng

AU - Zhang, Yinjia

N1 - Conference code: 19

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - blood glucose prediction

KW - continuous glucose monitoring

KW - convolution

KW - seasonality and trend

KW - time series

UR - http://www.scopus.com/inward/record.url?scp=85177445388&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-46677-9_30

DO - 10.1007/978-3-031-46677-9_30

M3 - Conference article in proceedings

AN - SCOPUS:85177445388

SN - 978-3-031-46676-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 437

EP - 450

BT - Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings

A2 - Yang, Xiaochun

A2 - Wang, Bin

A2 - Suhartanto, Heru

A2 - Wang, Guoren

A2 - Jiang, Jing

A2 - Li, Bing

A2 - Zhu, Huaijie

A2 - Cui, Ningning

PB - Springer

Y2 - 21 August 2023 through 23 August 2023

ER -

Rao W, Yang G, Zhao Q, Liu Y, Zhu H, Li M et al. STIP: A Seasonal Trend Integrated Predictor for Blood Glucose Level in Time Series. In Yang X, Wang B, Suhartanto H, Wang G, Jiang J, Li B, Zhu H, Cui N, editors, Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. Springer. 2023. p. 437-450. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-46677-9_30