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Articles Published Processes
8/16/2018 11:54:24 AM | Browse: 792 | Download: 1525
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Received |
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2018-03-28 03:42 |
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Peer-Review Started |
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2018-03-28 09:07 |
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To Make the First Decision |
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2018-05-18 01:05 |
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Return for Revision |
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2018-05-18 08:28 |
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Revised |
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2018-06-07 13:58 |
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Second Decision |
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2018-06-25 09:54 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2018-06-27 05:13 |
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Articles in Press |
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2018-06-27 05:13 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2018-07-30 06:12 |
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Publish the Manuscript Online |
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2018-08-16 11:54 |
| ISSN |
2307-8960 (online) |
| Open Access |
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Medicine, Research & Experimental |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Mei-Juan Chen, Pei-Hsuan Yang, Mi-Tren Hsieh, Chia-Hung Yeh, Chih-Hsiang Huang, Chieh- Ming Yang and Gen-Min Lin |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Gen-Min Lin, MD, MPhil, 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 |
| Key Words |
Particulate matter 2.5; Particulate matter 10; Upper respiratory infections; Machine learning; Air pollution |
| 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. |
| Publish Date |
2018-08-16 11:54 |
| Citation |
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 |
| URL |
http://www.wjgnet.com/2307-8960/full/v6/i8/200.htm |
| DOI |
http://dx.doi.org/10.12998/wjcc.v6.i8.200 |
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