CGM-Based Blood Glucose Prediction Model With LSTM Encoder-Decoder Architecture

Accurate prediction of blood glucose levels is crucial for automated treatment in diabetic patients. This study proposes a blood glucose prediction model based on an improved attention mechanism within a long short-term memory (LSTM) encoder-decoder (Att-E-D) architecture to enhance blood glucose pr...

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Bibliographic Details
Published in:	IEEE sensors journal Vol. 25; no. 3; pp. 5824 - 5839
Main Authors:	Xu, He, Zhang, Yi, Liu, Sixing, Ji, Yimu, Lv, Ming, Li, Peng
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Attention mechanism Attention mechanisms Blood blood glucose prediction continuous glucose monitoring (CGM) sensor Data models Datasets Decoding Diabetes encoder-decoder (E-D) Encoders-Decoders Glucose Information retrieval Long short term memory Prediction models Predictive models Regression models Search algorithms Sensors similar sequence Support vector machines Time series analysis
ISSN:	1530-437X, 1558-1748
Online Access:	Get full text
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