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Data mining medical information: should artificial neural networks be used to analyse trauma audit data?

Chesney, Thomas, Penny, Kay I, Oakley, Peter, Davies, Simon, Chesney, David, Maffulli, Nicola and Templeton, John (2006) Data mining medical information: should artificial neural networks be used to analyse trauma audit data? International Journal of Healthcare Information Systems and Informatics, 1 (2). pp. 51-64. ISSN 1555-3396

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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: Article
Print ISSN: 1555-3396
Electronic ISSN: 1555-340X
Uncontrolled Keywords: trauma audit; logistic regression analysis; patient outcomes; artificial neural network
University Divisions/Research Centres: Faculty of Health, Life & Social Sciences > School of Nursing, Midwifery and Social Care
Dewey Decimal Subjects: 600 Technology > 610 Medicine & health > 610 Medicine & health
000 Computer science, information & general works > 000 Computer science, knowledge & systems > 005 Computer programming, programs & data
Library of Congress Subjects: R Medicine > R Medicine (General)
Q Science > QA Mathematics > QA76 Computer software
Item ID: 1575
Depositing User: RAE Import
Date Deposited: 04 Apr 2008 14:13
Last Modified: 21 Mar 2013 16:16
URI: http://researchrepository.napier.ac.uk/id/eprint/1575

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