Innovative data mining approaches for outcome prediction of trauma patients

Abstract

Trauma is the most common cause of death to young people and many of these deaths are preventable [1]. The prediction of trauma patients outcome was a difficult problem to investigate till present times. In this study, prediction models are built and their capabilities to accurately predict the mortality are assessed. The analysis includes a comparison of data mining techniques using classification, clustering and association algorithms. Data were collected by Hellenic Trauma and Emergency Surgery Society from 30 Greek hospitals. Dataset contains records of 8544 patients suffering from severe injuries collected from the year 2005 to 2006. Factors include patients' demographic elements and several other variables registered from the time and place of accident until the hospital treatment and final outcome. Using this analysis the obtained results are compared in terms of sensitivity, specificity, positive predictive value and negative predictive value and the ROC curve depicts these methods performance.

Share and Cite:

Theodoraki, 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, 3, 791-798. doi: 10.4236/jbise.2010.38105.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] The trauma audit and research network. http://www. tarn.ac.uk/introduction/firstDecade.pdf
[2] Meyer, A. (1998) Death and disability from injury: A global challenge. Journal of Trauma, 44(1), 1-12.
[3] World Health Organization. http://www.who.int/en/
[4] The trauma audit and research network. http://www. tarn.ac.uk/content/downloads/36/firstdecade.pdf
[5] 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.
[6] 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.
[7] Teasdale, G. and Jennett, B. (1974) Assessment of coma and impaired consciousness. A practical scale. Lancet, 2(7872), 81-84.
[8] 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.
[9] 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.
[10] Cox, D.R. and Hinkley, D.V. (1974) Theoretical statistics. Chapman and Hall, London.
[11] Cramer, H. (1946) Mathematical methods of statistics. Princeton University Press, Princeton.
[12] Dobson, A. (2002) An introduction to generalized linear models. 2nd Edition, Chapman and Hall/CRC, London.
[13] Pearson, R.L. (1983) Karl Pearson and the chi-squared test. International Statistical Review, 51, 59-72.
[14] 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.
[15] 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.
[16] 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.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.