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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 h 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. 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 especı́ficos 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.

Prediction of Hospital Admissions from Air Pollutant Data