@article {4974,
	title = {Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece},
	journal = {International Journal of Environmental Health Research},
	volume = {22},
	year = {2012},
	note = {Cited By :3Export Date: 2 November 2015CODEN: IJERECorrespondence Address: Douros, K.; 3rd Pediatric Department, Medical School, University of Athens, Athens, Greece; email: costasdouros@gmail.comChemicals/CAS: carbon monoxide, 630-08-0; nitrogen dioxide, 10102-44-0; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; Air Pollutants; Carbon Monoxide, 630-08-0; Nitrogen Dioxide, 10102-44-0; Ozone, 10028-15-6; Particulate Matter; Sulfur Dioxide, 7446-09-5References: Badjate, S.L., Dudul, S.V., Multi step ahead prediction of North and South hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model (2009) WSEAS Trans Infor Sci Appl., 6, pp. 684-693;Barnett, A.G., Williams, G.M., Schwartz, J., Neller, A.H., Best, T.L., Petroeschevsky, A.L., Simpson, R.W., Air pollution and child respiratory health. A case-crossover study in Australia and New Zealand (2005) Am J Resp Crit Care., 171, pp. 1272-1278; 
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},
	month = {2012},
	pages = {93 - 104},
	abstract = {Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001-2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children{\textquoteright}s Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0-4, 5-14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA. {\textcopyright} 2012 Taylor \& Francis.},
	keywords = {Adolescent, Air Pollutants, Air pollution, air temperature, ambient air, article, artificial neural network, Artificial neural networks, asthma, Athens, Athens [Attica], atmospheric pollution, Attica, bioclimatology, carbon monoxide, Child, child health, child hospitalization, Child, Preschool, Childhood asthma, childhood disease, cloudiness, disease registry, Female, Forecasting, forecasting method, global radiation, Greece, hospital admission, hospital sector, Hospitalization, Hospitals, Pediatric, human, Humans, humidity, Infant, Infant, Newborn, Male, meteorological phenomena, Meteorology, Neural Networks (Computer), nitrogen dioxide, Ozone, Particulate matter, pollution monitoring, population structure, Sulfur dioxide, sunshine, Weather, Wind speed},
	isbn = {09603123 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84859169079\&partnerID=40\&md5=d4da7c0f3eaef1a182da62f9b6796cc2},
	author = {Moustris, K. P. and Douros, K. and Nastos, P. T. and Larissi, I. K. and Anthracopoulos, M. B. and Paliatsos, A. G. and Priftis, K. N.}
}
