Retrospective Study
Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Aug 16, 2018; 6(8): 200-206
Published online Aug 16, 2018. doi: 10.12998/wjcc.v6.i8.200
Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis
Mei-Juan Chen, Pei-Hsuan Yang, Mi-Tren Hsieh, Chia-Hung Yeh, Chih-Hsiang Huang, Chieh-Ming Yang, Gen-Min Lin
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 by Hualien 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.
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/
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
ARTICLE HIGHLIGHTS
Research background

PM2.5 and PM10, also known as particle pollutions, can deposit in the respiratory tract and may trigger inflammatory reactions. Several studies have revealed that PM2.5 and PM10 concentrations may be associated with the occurrence of upper respiratory tract infections (URIs) and increase the mortality related to hospitalized pneumonia. Machine learning utilizes computational statistics to explore optimized algorithms that can learn from and make predictions based on data. Machine learning for potential hazardous exposures has been successfully applied to predict the occurrence of several clinical diseases, such as myocardial infarction, and the related risk of mortality. In addition, machine learning, such as artificial neural networks, can provide us an opportunity for big data training for the prediction of clinical diseases. For example, Carnegie Mellon’s Delphi group of the United States Centers of Disease Control has been working to create a machine learning model that accurately tracks the spread of the flu.

Research motivation

Since the severity of air pollution varies geographically, the hazardous effect on human health may also differ by region and ethnicity. It is reasonable to create a surveillance system to forecast the probability of disease occurrence related to regional air pollution. Accordingly, we attempted to establish a model of machine learning to relate PM2.5 and PM10 concentrations to the volume of outpatient visits for acute URIs in Taiwan.

Research objectives

To examine the accuracy of machine learning to relate PM2.5 and PM10 concentrations to URIs.

Research methods

Daily nationwide and regional outdoor PM2.5 and PM10 concentrations collected over 30 consecutive days from the Taiwan Environment Protection Administration were the inputs for the multilayer perceptron (MLP) machine learning to relate to the subsequent one-week outpatient visits for URIs. The URI data were obtained from the Centers for Disease Control datasets in Taiwan between 2009 and 2016. The testing used the middle month dataset of each season (January, April, July, and October), and the training used the other months’ datasets. The weekly URI cases were classified by tertile as high, moderate, and low volumes.

Research results

Both PM concentrations and URI cases peak in the winter and spring. In the nationwide data analysis, MLP machine learning can accurately relate PM2.5 and PM10 concentrations with the URI volumes of the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively). In the regional data analyses, PM2.5 has greater accuracy than PM10 for the elderly, particularly in the Central region (78.10% and 74.45%, respectively), whereas PM10 has greater accuracy than PM2.5 for the overall population, particularly in the Northern region (73.19% and 63.04%, respectively).

Research conclusions

Machine learning could accurately relate short-term PM2.5 and PM10 concentrations to subsequent URI occurrence. Our findings suggested that the effects of PM2.5 and PM10 on URI may differ by age, and the mechanism needs further evaluation.

Research perspectives

We used MLP machine learning to successfully relate PM concentrations data to the volume of URI cases. Data for more air pollutants and other meteorological parameters can be applied to the current MLP model in future work.