Research Output

Data mining medical Information.

  Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

  • Type:

    Book Chapter

  • Date:

    30 November 2007

  • Publication Status:


  • Publisher

    IGI Global

  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence


Chesney, T., Penny, K. I., Oakley, P., Davies, S., Chesney, D., Maffulli, N. & Templeton, J. (2007). Data mining medical Information. In Wang, J. (Ed.). Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, 2915-2927. IGI Global. doi:10.4018/978-1-59904-951-9.ch186. ISBN 9781599049519



Trauma audit; Artificial Neural Network (ANN); data mining; logistic regression model;

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