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Development of prediction models for the incidence of pediatric acute otitis media using Poisson regression analysis and XGBoost

Authors
Mun, S.-K.Chang, M.
Issue Date
Mar-2022
Publisher
Springer Science and Business Media Deutschland GmbH
Keywords
Acute otitis media; Air pollution; Climate; Poisson regression analysis; Prediction; XGBoost
Citation
Environmental Science and Pollution Research, v.29, no.13, pp 18629 - 18640
Pages
12
Journal Title
Environmental Science and Pollution Research
Volume
29
Number
13
Start Page
18629
End Page
18640
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62752
DOI
10.1007/s11356-021-17135-9
ISSN
0944-1344
1614-7499
Abstract
Otitis media has profound health and economic impact, and its occurrence is known to be influenced by air pollution and climate. The purpose of this study was to develop prediction models using climate and air pollution indicators for the occurrence of acute otitis media (AOM). The study was conducted from January 1, 2014, to December 31, 2019, and included pediatric patients (age < 12 years) diagnosed on their emergency room visit in our tertiary medical institution. We obtained data on the weekly number of AOM patients and the weekly average values of air pollution and climate indicators. Poisson regression analysis and eXtreme Gradient Boosting (XGBoost) were used to develop prediction models for the overall pediatric patients and for the patients classified according to sex (male and female) and age (< 2 years and ≥ 2 years). For the overall population, the correlation coefficients between the original and estimated data in the testing set were 0.441 (p < 0.001) and 0.844 (p < 0.001) for the models developed using Poisson regression analysis and XGBoost, respectively. The root-mean-square errors in the testing set were 3.094 and 1.856, respectively. For patients classified according to sex and age, the prediction models developed using XGBoost showed better performance than the models developed using Poisson regression analysis. In conclusion, this study successfully developed prediction models with air pollution and climate indicators for the incidence of pediatric AOM, using XGBoost. This model can be further developed to prevent pediatric AOM. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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의과대학 (의학부(임상-서울))
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