Cite this paper
nullTheodoraki, E. , Katsaragakis, S. , Koukouvinos, C. and Parpoula, C. (2010) Innovative data mining approaches for outcome prediction of trauma patients. Journal of Biomedical Science and Engineering
, 791-798. doi: 10.4236/jbise.2010.38105
 The trauma audit and research network. http://www. tarn.ac.uk/introduction/firstDecade.pdf
Meyer, A. (1998) Death and disability from injury: A global challenge. Journal of Trauma, 44(1), 1-12.
World Health Organization. http://www.who.int/en/
The trauma audit and research network. http://www. tarn.ac.uk/content/downloads/36/firstdecade.pdf
Baker, P., O’Neil, B., Haddon, W. and Long, B. (1974) The injury severity score: A method for describing patients with multiple injuries and evaluating emergency care. Journal of Trauma, 14(3), 187-196.
Copes, W.S., Sacco, W.J., Champion, H.R. and Bain, L.W. (1990) Progress in characterising anatomic injury. Proceedings of the 33rd Annual Meeting of the Asso- ciation for the Advancement of Automotive Medicine, Baltimore, 2-4 October 1989, 205-218.
Teasdale, G. and Jennett, B. (1974) Assessment of coma and impaired consciousness. A practical scale. Lancet, 2(7872), 81-84.
Penny, K. and Chesney, T. (2006) Imputation methods to deal with missing values when data mining trauma injury data. Proceedings of 28th International Conference on Information Technology Interfaces, Cavtat, 19-22 June 2006, 213-218.
Donders, A.R., Van der Heijden, G.J., Stijnen, T. and Moons, K.G. (2006) Review: A gentle introduction to imputation of missing values. Journal of Clinical Epi- demiology, 59(10), 1087-1091.
Cox, D.R. and Hinkley, D.V. (1974) Theoretical statistics. Chapman and Hall, London.
Cramer, H. (1946) Mathematical methods of statistics. Princeton University Press, Princeton.
Dobson, A. (2002) An introduction to generalized linear models. 2nd Edition, Chapman and Hall/CRC, London.
Pearson, R.L. (1983) Karl Pearson and the chi-squared test. International Statistical Review, 51, 59-72.
Agrawal, R. and Srikant, R. (1994) Fast algorithms for mining association rules. Proceedings of the 20th Inter- national Conference on Very Large Databases, Santiago de Chile, 12-15 September 1994, 479-499.
Craven, P. and Wahba, G. (1979) Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik, 31, 377-403.
Breault, J.L., Goodall, C.R. and Fos, P.J. (2002) Data mining a diabetic data warehouse. Artificial Intelligence in Medicine, 26(1-2), 37-54.