INSPIRING FUTURES

Data mining medical Information.

Chesney, Thomas, Penny, Kay I, Oakley, Peter, Davies, Simon, Chesney, David, Maffulli, Nicola and Templeton, John (2008) Data mining medical Information. In: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications. IGI Global, pp. 2915-2927. ISBN 9781599049519

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Abstract/Description

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.

Item Type: Book Section
ISBN: 9781599049519
Electronic ISBN: 9781599049526
Uncontrolled Keywords: Trauma audit; Artificial Neural Network (ANN); data mining; logistic regression model;
University Divisions/Research Centres: The Business School > School of Management
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 5381
Depositing User: Dr Kay Penny
Date Deposited: 15 Jun 2012 13:59
Last Modified: 15 Jun 2012 13:59
URI: http://researchrepository.napier.ac.uk/id/eprint/5381

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