@inproceedings{fec105326920430ca4129a7001f6733f,
title = "Generalized additive models of hospital admissions with respiratory disease and meteorology",
abstract = "Clinicians are very interested in researching what are important determinants of hospitalization for respiratory disease. In this paper, a general model to explain the relationship between the risk of respiratory disease and several meteorological variables will be presented by the framework of generalized additive models (GAMs) and its predictive effects will be evaluated. By using 9655 medical records with respiratory disease in a county in central China and daily meteorological data, a reasonably good fit was obtained. The result shows that the general method which was presented by this paper to discover the relationship between the meteorological factors and the hospitalization rate for respiratory disease is can explain most of the variation in the daily counts of hospital admissions.",
keywords = "generalized additive models, hospital admissions, respiratory disease",
author = "Lei An and Hongyu Kang and Yi Xin and Xiaoming Hu and Qin Li and Yin Ling and Heng Gu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 3rd IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2014 ; Conference date: 27-11-2014 Through 29-11-2014",
year = "2014",
doi = "10.1109/CCIS.2014.7175750",
language = "English",
series = "CCIS 2014 - Proceedings of 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "315--318",
editor = "Huadong Ma and Weining Wang and Yong Zhang",
booktitle = "CCIS 2014 - Proceedings of 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems",
address = "United States",
}