International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 1578 - 1589

Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism

Authors
Xiaojia Wang1, Wenqing Gong1, Keyu Zhu1, *, Lushi Yao1, Shanshan Zhang2, 4, Weiqun Xu3, 4, Yuxiang Guan3, 4
1Department of Information Management, School of Management, Hefei University of Technology, Hefei, Anhui, 230009, China
2Department of Clinical Teaching, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, China
3Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, China
4The National Chinese Medicine Clinical Research Base—Key Disease of Diabetes Mellitus Study, Hefei, Anhui, 230031, China
*Corresponding author. Email: zhukeyu@hfut.edu.cn
Corresponding Author
Keyu Zhu
Received 6 March 2020, Accepted 7 September 2020, Available Online 22 September 2020.
DOI
10.2991/ijcis.d.200915.001How to use a DOI?
Keywords
Network; Machine learning; T2DM; Glycosylated hemoglobin; Self-attention mechanism
Abstract

Type 2 diabetes mellitus (T2DM) has been identified as one of the most challenging chronic diseases to manage. In recent years, the incidence of T2DM has increased, which has seriously endangered people’s health and life quality. Glycosylated hemoglobin (HbA1c) is the gold standard clinical indicator of the progression of T2DM. An accurate prediction of HbA1c levels not only helps medical workers improve the accuracy of clinical decision-making but also helps patients to better understand the clinical progression of T2DM and conduct self-management to achieve the goal of controlling the progression of T2DM. Therefore, we introduced the long short-term memory (LSTM) neural network to predict patients’ HbA1c levels using time sequential data from electronic medical records (EMRs). We added the self-attention mechanism based on the traditional LSTM to capture the long-term interdependence of feature elements and which ensure that the memory was more profound and effective, and used the gradient search technology to minimize the mean square error of the predicted value of the network and the real value. LSTM with the self-attention mechanism performed better than the traditional deep learning sequence prediction method. Our research provides a good reference for the application of deep learning in the field of medical health management.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
1578 - 1589
Publication Date
2020/09/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200915.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Xiaojia Wang
AU  - Wenqing Gong
AU  - Keyu Zhu
AU  - Lushi Yao
AU  - Shanshan Zhang
AU  - Weiqun Xu
AU  - Yuxiang Guan
PY  - 2020
DA  - 2020/09/22
TI  - Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism
JO  - International Journal of Computational Intelligence Systems
SP  - 1578
EP  - 1589
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200915.001
DO  - 10.2991/ijcis.d.200915.001
ID  - Wang2020
ER  -