Pattern recognition in physiological time-series data using Bayesian neural networks
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| Title: | Pattern recognition in physiological time-series data using Bayesian neural networks |
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| Authors: | Howells, Timothy Paul |
| Summary: | This thesis describes the application of Bayesian techniques to the analysis of a large database of physiological time series data collected during the management of patients following traumatic brain injury at the Western General Hospital in Edinburgh. The study can be divided into three main sections: • Model validation using simulated data: Techniques are developed that show that under certain conditions the distribution of network outputs generated by these Bayesian neural networks correctly models the desired conditional probability density functions for a wide range of simple problems for which exact solutions can be derived. This provides the basis for using these models in a scientific context. • Model validation using real data. Statistical prognostic modelling for head injured patients is well advanced using simple demographic and clinical features. The Bayesean techniques developed in the previous section are applied to this problem, and the results are compared to those obtained using standard statistical techniques. • Application of these models to physiological data. The models are now applied to the full database and used to interpret the data and provide new insight into the risk factors for head injured patients in intensive care. |
| URL: | http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.652639 |
| Database: | OpenDissertations |
| Abstract: | This thesis describes the application of Bayesian techniques to the analysis of a large database of physiological time series data collected during the management of patients following traumatic brain injury at the Western General Hospital in Edinburgh. The study can be divided into three main sections: • Model validation using simulated data: Techniques are developed that show that under certain conditions the distribution of network outputs generated by these Bayesian neural networks correctly models the desired conditional probability density functions for a wide range of simple problems for which exact solutions can be derived. This provides the basis for using these models in a scientific context. • Model validation using real data. Statistical prognostic modelling for head injured patients is well advanced using simple demographic and clinical features. The Bayesean techniques developed in the previous section are applied to this problem, and the results are compared to those obtained using standard statistical techniques. • Application of these models to physiological data. The models are now applied to the full database and used to interpret the data and provide new insight into the risk factors for head injured patients in intensive care. |
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