Prediction of Hospital Admissions from Air Pollutant Data
Resumo
Diversos trabalho na literatura indicam que poluentes atmosféricos são nocivos à saúde. Sendo assim, neste trabalho buscou-se verificar a possibilidade de prever, a partir de dados de concentração de poluentes atmosféricos e com 24 h de antecedência, o número de internações hospitalares associadas a doenças respiratórias. Para isso, foram utilizados Extreme Learning Machines (ELMs) como preditores e experimentos foram realizados com dados referentes à cidade de Campinas (SP). Os resultados mostraram que, apesar do uso de dados de alguns poluentes específicos levarem a menores erros de previsão, os melhores resultados ainda foram obtidos utilizando-se apenas a série histórica de internações hospitalares como entrada para as ELMs.
Palavras-chave:
Extreme Learning Machines, Poluição Atmosférica, Internações Hospitalares
Referências
Anderson, J. O., Thundiyil, J. G., and Stolbach, A. (2012). Clearing the air: A review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology, 8(2):166–175.
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.
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.
Publicado
15/10/2019
Como Citar
LAENDLE JUNIOR, Marcelo; POZZA, Simone; COELHO, Guilherme.
Prediction of Hospital Admissions from Air Pollutant Data. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (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.
