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9/4/2018 3:36:51 AM | Browse: 622 | Download: 591
Publication Name World Journal of Clinical Cases
Manuscript ID 38945
Country/Territory Taiwan
Received
2018-03-28 03:42
Peer-Review Started
2018-03-28 09:07
To Make the First Decision
2018-05-18 01:05
Return for Revision
2018-05-18 08:28
Revised
2018-06-07 13:58
Second Decision
2018-06-25 09:54
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2018-06-27 05:13
Articles in Press
2018-06-27 05:13
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2018-07-30 06:12
Publish the Manuscript Online
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
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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
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
Funding Agency and Grant Number
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
Full Article (PDF) WJCC-6-200.pdf
Full Article (Word) WJCC-6-200.doc
Manuscript File 38945-Review.docx
Answering Reviewers 38945-Answering reviewers.pdf
Audio Core Tip 38945-Audio core tip.m4a
Biostatistics Review Certificate 38945-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 38945-Conflict-of-interest statement.pdf
Copyright License Agreement 38945-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate 38945-Language certificate.pdf
Supplementary Material 38945-Supplementary material.docx
Peer-review Report 38945-Peer-review(s).pdf
Scientific Misconduct Check 38945-Scientific misconduct check.pdf
Scientific Editor Work List 38945-Scientific editor work list.pdf