Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis

doi:10.12998/wjcc.v6.i8.200

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Aug 16, 2018 (publication date) through May 19, 2024

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World Journal of Clinical Cases

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2307-8960

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Clin Cases. Aug 16, 2018; 6(8): 200-206
Published online Aug 16, 2018. doi: 10.12998/wjcc.v6.i8.200

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Figure 1 Multilayer perceptron model for the proposed algorithm.

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Figure 2 The average daily concentrations and weekly numbers of outpatient visits for upper respiratory tract infections in each month. A and B: PM2.5 and PM10, repectively (December 2008 - December 2016); C and D: The overall and the elderly patients, repsectively (January 2009 - December 2016). PM: Particulate matter.

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: https://www.wjgnet.com/2307-8960/full/v6/i8/200.htm
DOI: https://dx.doi.org/10.12998/wjcc.v6.i8.200