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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1221

Title: A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration
Authors: Tucker, A
Vinciotti, V
Liu, X
Garway-Heath, D
Keywords: Classification
Multivariate Time Series
Bayesian Networks
Visual Field
Glaucoma
Publication Date: 2005
Publisher: Elsevier
Citation: (2005) Tucker, A., Vinciotti, V., Liu, X. and Garway-Heath, D., A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration, Artificial Intelligence in Medicine 34 (2) : 163-177
Abstract: Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data.
URI: http://bura.brunel.ac.uk/handle/2438/1221
Appears in Collections:Mathematical Science
Computer Science

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