Prediction of Hospital Admissions from Air Pollutant Data
Abstract
Several papers in the literature indicate that air pollutants are harmful to health. Thus, in this work we sought to verify the possibility of predicting, based on atmospheric pollutants concentration and 24 hours in advance, the number of hospital admissions associated with respiratory diseases. For this, Extreme Learning Machines (ELMs) were used as predictors and experiments were made with data from the city of Campinas (SP). The results showed that, although the use of some specific pollutants leads to smaller prediction errors, the best results were still obtained using only the historical series of hospitalizations as input to the ELMs.
Keywords:
Extreme Learning Machines, Air Pollution, Hospital Admissions
References
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Barajas, J. A. B. (2018). Dynamic ensemble mechanisms to improve particulate matter forecasting. Master’s thesis, Faculdade de Tecnologia (FT), Universidade Estadual de Campinas (UNICAMP).
Bisht, M. and Seeja, K. R. (2018). Air pollution prediction using Extreme Learning Machine: A case study on Delhi (India). In Proceedings of the First International Conference on Smart System, Innovations and Computing, pages 181–189.
Braga, B., Hespanhol, I., Conejo, J. G. L., Mierzwa, J. C., de Barros, M. T. L., Spencer, M., Porto, M., Nucci, N., Juliano, N., and Eiger, S. (2005). Introdução à engenharia ambiental – O desafio do desenvolvimento sustentável. Prentice-Hall Brasil.
Burnett, R. T., Brook, J., Dann, T., Delocla, C., Philips, O., Cakmak, S., Vincent, R., Goldberg, M. S., and Krewski, D. (2000). Association between particulate- and gasphase components of urban air pollution and daily mortality in eight Canadian cities. Inhalation Toxicology, 12(sup4):15–39.
CETESB (2019). Qualidade do ar – Poluentes. Disponı́vel em: https://cetesb. sp.gov.br/ar/poluentes/. Acessado em 12-Jul-2019.
DataSUS (2019). Informações de saúde (TABNET). Disponı́vel em: http:// datasus.saude.gov.br/informacoes-de-saude/tabnet. Acessado em 12-Jul-2019.
Enders, C. K. (2013). Applied Missing Data Analysis. Guilford Press.
Gardner, M. W. and Dorling, S. R. (1998). Artificial neural networks (the Multilayer Perceptron) — a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14–15):2627––2636.
Godish, T. (2004). Air Quality. CRC Press, 4th. edition.
Han, J., Kamber, M., and Pei, J. (2012). Data Mining Concepts and Techniques. Morgan Kaufmann Publishers.
Haykin, S. (2008). Neural Networks and Learning Machines. Pearson, 3rd. edition.
Huang, G.-B., Wang, D. H., and Lan, Y. (2011). Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2(2):107–122.
Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3):489–501.
Loomis, D., Castillejos, M., Gold, D. R., McDonnell, W., and Borja-Aburto, V. H. (1999). Air pollution and infant mortality in Mexico City. Epidemiology, 10(2):118–123.
Morettin, P. A. and Toloi, C. M. C. (2006). Análise de Séries Temporais. Edgard Blucher.
Ostro, B., Broadwin, R., Feng, S. G. W. Y., and Lipsett, M. F. (2006). Particulate air pollution and mortality in nine California counties: Results from CALFINE. Environmental Health Perspectives, 144(1):29–33.
QUALAR (2019). Qualidade do ar. Disponı́vel em: https://cetesb.sp.gov. br/ar/qualar/. Acessado em 12-Jul-2019.
Seinfeld, J. H. and Pandis, S. N. (2016). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley.
Tadano, Y. S., Alves, T. A., Silva, N. S. S., and Valadares, H. S. (2017). Impacto da poluição atmosférica e das alterações climáticas na saúde populacional utilizando redes neurais artificiais. Mecânica Experimental – Revista da Associação Portuguesa de Análise Experimental de Tensões, 29:35–42.
WHO (2016a). Ambient air pollution: A global assessment of exposure and burden of disease. Technical report, World Health Organization. Disponı́vel em: https://www.who.int/phe/publications/ air-pollution-global-assessment/en/. Acessado em: 12-jul-2019.
WHO (2016b). International statistical classification of diseases and related health problems 10th revision. Disponı́vel em: https://icd.who.int/browse10/ 2016/en. Acessado em 12-Jul-2019.
Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., and Zhang, M. (2017). Spatiotemporal prediction of continuous daily PM2.5 concentrations across china using a spatially explicit machine learning algorithm. Atmospheric Environment, 55:129–139.
Published
2019-10-15
How to Cite
LAENDLE JUNIOR, Marcelo; POZZA, Simone; COELHO, Guilherme.
Prediction of Hospital Admissions from Air Pollutant Data. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 332-343.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9295.
