Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543486
Title: Modelling outcome prediction for trauma patients : an artificial intelligence approach
Author: Ali, Nor Azizah
ISNI:       0000 0004 2714 4135
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2011
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Abstract:
Trauma, a term used in medicine to describe a physical injury, is believed to be one of the major causes of death and disability in modern societies. The development of the Trauma and Injury Severity Score (TRISS) method by Boyd et al. (1987) can be considered as a high-impact initiative in order to improve the trauma patient's care. This method was used to compare the expected and the observed outcomes in relation to mortality. Thus, the rate of unexpected deaths or survivals can be examined and any related problems such as improper trauma patient's care can be identified. In general, the model with this particular task can be referred to as a prediction or classification model. In our study, the Trauma Audit and Research Network (TARN) has applied the TRISS method to assist them in a comparative audit among the participating hospitals since 1989. Despite the fact that the TRISS model is simple and easy to use, there is some limitation in the logistic regression technique which the TRISS model is based upon. In fact, some preliminary results from other researchers have indicated that prediction accuracy may be improved by using alternative modelling approaches, such as the artificial intelligence (AI) based methods. Therefore, attempts are made in this study with the aim of developing new outcome prediction models using the AI methods namely; artificial neural networks, support vector machines, A > nearest neighbour and naive Bayesian, and then the results will be compared to the TRISS-FP model (for comparison purposes, we refer to the outcome prediction model based on the TRISS method developed by TARN as the TRISS-FP model throughout this thesis). The model's predictive performances are evaluated using performance measures, including sensitivity, specificity, area under the receiving operating characteristic curve (AUC) and geometric mean (Gmean). The data for this research is drawn from the TARN database. The empirical result has shown that the new Al-based models developed in this study obtained better predictive performances compared to the TRISS-FP model. The ANN model has emerged as the best Al-based model. Thus, this model will be recommended to the TARN for future consideration.
Supervisor: Not available Sponsor: Universiti Teknologi Malaysia ; Ministry of Higher Education, Malaysia
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.543486  DOI: Not available
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