Chen MJ, Yang PH, Hsieh MT, Yeh CH, Huang CH, Yang CM, Lin GM. Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis. World J Clin Cases 2018; 6(8): 200-206 [PMID: 30148148 DOI: 10.12998/wjcc.v6.i8.200]
Corresponding Author of This Article
Gen-Min Lin, MD, PhD, Assistant Professor, Chief Doctor, Department of Electrical Engineering, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd. Shoufeng, Hualien 974, Taiwan. farmer507@yahoo.com.tw
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Retrospective Study
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Chen MJ, Yang PH, Hsieh MT, Yeh CH, Huang CH, Yang CM, Lin GM. Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis. World J Clin Cases 2018; 6(8): 200-206 [PMID: 30148148 DOI: 10.12998/wjcc.v6.i8.200]
Mei-Juan Chen, Pei-Hsuan Yang, Mi-Tren Hsieh, Chieh-Ming Yang, Gen-Min Lin, Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan
Chia-Hung Yeh, Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan
Chia-Hung Yeh, Chih-Hsiang Huang, Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
Gen-Min Lin, Department of Medicine, Hualien Armed Forces General Hospital, Hualien 971, Taiwan
Gen-Min Lin, Departments of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Author contributions: Chen MJ and Yeh CH contributed to the conception and design of the study, as well as the acquisition and interpretation of the data; Yang PH, Hsieh MT and Huang CH analyzed the data; Yang CM collected the data; Lin GM wrote the article; all authors made critical revisions related to the important intellectual content of the article and approved the final version of the article to be published.
Supported byHualien Armed Forces General Hospital, No. 805-C107-14; and Ministry of Science and Technology, Taiwan, R.O.C., No. MOST 107-2221-E-899-002-MY3.
Informed consent statement: Participants were not required to give informed consent to this retrospective study since the analysis of baseline characteristics used anonymized clinical data.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Correspondence to: Gen-Min Lin, MD, PhD, Assistant Professor, Chief Doctor, Department of Electrical Engineering, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd. Shoufeng, Hualien 974, Taiwan. farmer507@yahoo.com.tw
Telephone: +886-3-8634086 Fax: +886-3-8634060
Received: March 28, 2018 Peer-review started: March 28, 2018 First decision: May 16, 2018 Revised: June 7, 2018 Accepted: June 26, 2018 Article in press: June 27, 2018 Published online: August 16, 2018 Processing time: 141 Days and 20.7 Hours
Core Tip
Core tip: Particulate matter (PM) 2.5 and PM10 air pollutants can trigger inflammation and predispose the respiratory tract to infections. This study used the multilayer perceptron (MLP) machine learning architecture to relate the daily PM2.5 and PM10 concentrations over 30 consecutive days to the subsequent one-week outpatient visits for upper respiratory tract infections (URIs) in Taiwan between 2008 and 2016. In the nationwide data analysis, PM2.5 and PM10 concentrations can precisely predict the volumes of URI for the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively). Our findings suggested that machine learning could accurately relate PM2.5 and PM10 concentrations to the outpatient visits for URI, especially for the elderly population.
