An Oriented Attention Model for Infectious Disease Cases Prediction

Peisong Zhang; Zhijin Wang; Guoqing Chao; Yaohui Huang; Jingwen Yan

doi:10.1007/978-3-031-08530-7_11

An Oriented Attention Model for Infectious Disease Cases Prediction

Peisong Zhang
, Zhijin Wang^*
, Guoqing Chao
, Yaohui Huang
, Jingwen Yan

^*Corresponding author for this work

Jimei University
School of Computer Science and Technology, Harbin Institute of Technology
Guangxi University for Nationalities
Shantou University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a single attention mechanism can only capture local information, i.e. part of the temporal dynamics from time series. In this paper, we take for the hypothesis that using multiple attention from different aspects could improve prediction accuracy. An oriented attention model (OAM) is proposed to draw temporal dynamics in several aspects, via oriented attention units and their aggregation. Firstly, time series are represented as oriented transformations. And then those representations are consolidated to connect with outputs. Intensive experiments on two real infectious disease datasets show OAM’s effectiveness.

Original language	English
Title of host publication	Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings
Editors	Hamido Fujita, Philippe Fournier-Viger, Moonis Ali, Yinglin Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	124-136
Number of pages	13
ISBN (Print)	9783031085291
DOIs	https://doi.org/10.1007/978-3-031-08530-7_11
State	Published - 2022
Externally published	Yes
Event	35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022 - Kitakyushu, Japan Duration: 19 Jul 2022 → 22 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13343 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022
Country/Territory	Japan
City	Kitakyushu
Period	19/07/22 → 22/07/22

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Aggregation
Infectious disease
Oriented attention
Prediction
Time series

Access to Document

10.1007/978-3-031-08530-7_11

Cite this

Zhang, P., Wang, Z., Chao, G., Huang, Y., & Yan, J. (2022). An Oriented Attention Model for Infectious Disease Cases Prediction. In H. Fujita, P. Fournier-Viger, M. Ali, & Y. Wang (Eds.), Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings (pp. 124-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13343 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-08530-7_11

Zhang, Peisong ; Wang, Zhijin ; Chao, Guoqing et al. / An Oriented Attention Model for Infectious Disease Cases Prediction. Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings. editor / Hamido Fujita ; Philippe Fournier-Viger ; Moonis Ali ; Yinglin Wang. Springer Science and Business Media Deutschland GmbH, 2022. pp. 124-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a single attention mechanism can only capture local information, i.e. part of the temporal dynamics from time series. In this paper, we take for the hypothesis that using multiple attention from different aspects could improve prediction accuracy. An oriented attention model (OAM) is proposed to draw temporal dynamics in several aspects, via oriented attention units and their aggregation. Firstly, time series are represented as oriented transformations. And then those representations are consolidated to connect with outputs. Intensive experiments on two real infectious disease datasets show OAM{\textquoteright}s effectiveness.",

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Zhang, P, Wang, Z, Chao, G, Huang, Y & Yan, J 2022, An Oriented Attention Model for Infectious Disease Cases Prediction. in H Fujita, P Fournier-Viger, M Ali & Y Wang (eds), Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13343 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 124-136, 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, 19/07/22. https://doi.org/10.1007/978-3-031-08530-7_11

An Oriented Attention Model for Infectious Disease Cases Prediction. / Zhang, Peisong; Wang, Zhijin; Chao, Guoqing et al.
Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings. ed. / Hamido Fujita; Philippe Fournier-Viger; Moonis Ali; Yinglin Wang. Springer Science and Business Media Deutschland GmbH, 2022. p. 124-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13343 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yan, Jingwen

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KW - Aggregation

KW - Infectious disease

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KW - Prediction

KW - Time series

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A2 - Wang, Yinglin

PB - Springer Science and Business Media Deutschland GmbH

T2 - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022

Y2 - 19 July 2022 through 22 July 2022

ER -

Zhang P, Wang Z, Chao G, Huang Y, Yan J. An Oriented Attention Model for Infectious Disease Cases Prediction. In Fujita H, Fournier-Viger P, Ali M, Wang Y, editors, Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 124-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-08530-7_11