Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning

Sing Ling Jhuo; Mi Tren Hsieh; Ting Chien Weng; Mei Juan Chen; Chieh Ming Yang; Chia Hung Yeh

doi:10.1109/ISPACS48206.2019.8986244

Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning

Sing Ling Jhuo
, Mi Tren Hsieh
, Ting Chien Weng
, Mei Juan Chen
, Chieh Ming Yang
, Chia Hung Yeh

研究成果: 書貢獻/報告類型 › 會議論文篇章

9 引文斯高帕斯（Scopus）

摘要

Trend prediction of influenza and the associated pneumonia can provide the information for taking preventive actions for public health. This paper uses meteorological and pollution parameters, and acute upper respiratory infection (AVRI) outpatient number as input to multilayer perceptron (MLP) to predict the patient number of influenza and the associated pneumonia in the following week. The meteorological parameters in use are temperature and relative humidity, air pollution parameters are Particulate Matter 2.5 (PM 2.5) and Carbon Monoxide (CO), and the patient prediction includes both outpatients and inpatients. Patients are classified by tertiles into three categories: high, moderate, and low volumes. In the nationwide data analysis, the proposed method using MLP machine learning can reach the accuracy of 81.16% for the elderly population and 77.54% for overall population in Taiwan. The regional data analyses with various age groups are also provided in this paper.

原文	英語
主出版物標題	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781728130385
DOIs	https://doi.org/10.1109/ISPACS48206.2019.8986244
出版狀態	已發佈 - 2019 12月
對外發佈	是
事件	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, 臺灣持續時間: 2019 12月 3 → 2019 12月 6

出版系列

名字	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

會議

會議	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
國家/地區	臺灣
城市	Taipei
期間	2019/12/03 → 2019/12/06

UN SDG

此研究成果有助於以下永續發展目標

SDG 3 健康與福祉

ASJC Scopus subject areas

人工智慧
電腦視覺和模式識別
訊號處理
電腦網路與通信

存取文件

10.1109/ISPACS48206.2019.8986244

其它文件與鏈接

引用此

Jhuo, S. L., Hsieh, M. T., Weng, T. C., Chen, M. J., Yang, C. M., & Yeh, C. H. (2019). Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. 於 Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 文章 8986244 (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPACS48206.2019.8986244

Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. / Jhuo, Sing Ling; Hsieh, Mi Tren; Weng, Ting Chien 等.
Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8986244 (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019).

研究成果: 書貢獻/報告類型 › 會議論文篇章

Jhuo, SL, Hsieh, MT, Weng, TC, Chen, MJ, Yang, CM & Yeh, CH 2019, Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. 於 Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019., 8986244, Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, Taipei, 臺灣, 2019/12/03. https://doi.org/10.1109/ISPACS48206.2019.8986244

Jhuo SL, Hsieh MT, Weng TC, Chen MJ, Yang CM, Yeh CH. Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. 於 Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8986244. (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019). doi: 10.1109/ISPACS48206.2019.8986244

Jhuo, Sing Ling ; Hsieh, Mi Tren ; Weng, Ting Chien 等. / Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019).

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title = "Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning",

abstract = "Trend prediction of influenza and the associated pneumonia can provide the information for taking preventive actions for public health. This paper uses meteorological and pollution parameters, and acute upper respiratory infection (AVRI) outpatient number as input to multilayer perceptron (MLP) to predict the patient number of influenza and the associated pneumonia in the following week. The meteorological parameters in use are temperature and relative humidity, air pollution parameters are Particulate Matter 2.5 (PM 2.5) and Carbon Monoxide (CO), and the patient prediction includes both outpatients and inpatients. Patients are classified by tertiles into three categories: high, moderate, and low volumes. In the nationwide data analysis, the proposed method using MLP machine learning can reach the accuracy of 81.16\% for the elderly population and 77.54\% for overall population in Taiwan. The regional data analyses with various age groups are also provided in this paper.",

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author = "Jhuo, \{Sing Ling\} and Hsieh, \{Mi Tren\} and Weng, \{Ting Chien\} and Chen, \{Mei Juan\} and Yang, \{Chieh Ming\} and Yeh, \{Chia Hung\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 ; Conference date: 03-12-2019 Through 06-12-2019",

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AU - Jhuo, Sing Ling

AU - Hsieh, Mi Tren

AU - Weng, Ting Chien

AU - Chen, Mei Juan

AU - Yang, Chieh Ming

AU - Yeh, Chia Hung

PY - 2019/12

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N2 - Trend prediction of influenza and the associated pneumonia can provide the information for taking preventive actions for public health. This paper uses meteorological and pollution parameters, and acute upper respiratory infection (AVRI) outpatient number as input to multilayer perceptron (MLP) to predict the patient number of influenza and the associated pneumonia in the following week. The meteorological parameters in use are temperature and relative humidity, air pollution parameters are Particulate Matter 2.5 (PM 2.5) and Carbon Monoxide (CO), and the patient prediction includes both outpatients and inpatients. Patients are classified by tertiles into three categories: high, moderate, and low volumes. In the nationwide data analysis, the proposed method using MLP machine learning can reach the accuracy of 81.16% for the elderly population and 77.54% for overall population in Taiwan. The regional data analyses with various age groups are also provided in this paper.

AB - Trend prediction of influenza and the associated pneumonia can provide the information for taking preventive actions for public health. This paper uses meteorological and pollution parameters, and acute upper respiratory infection (AVRI) outpatient number as input to multilayer perceptron (MLP) to predict the patient number of influenza and the associated pneumonia in the following week. The meteorological parameters in use are temperature and relative humidity, air pollution parameters are Particulate Matter 2.5 (PM 2.5) and Carbon Monoxide (CO), and the patient prediction includes both outpatients and inpatients. Patients are classified by tertiles into three categories: high, moderate, and low volumes. In the nationwide data analysis, the proposed method using MLP machine learning can reach the accuracy of 81.16% for the elderly population and 77.54% for overall population in Taiwan. The regional data analyses with various age groups are also provided in this paper.

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KW - influenza

KW - machine learning

KW - multilayer perceptron

KW - pneumonia

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BT - Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

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Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning

摘要

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會議

UN SDG

ASJC Scopus subject areas

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