Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent data analysis
Spatial operators for evolving dynamic Bayesian networks from spatio-temporal data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy
Artificial Intelligence in Medicine
Predicting glaucomatous visual field deterioration through short multivariate time series modelling
Artificial Intelligence in Medicine
Reforestation planning using Bayesian networks
Environmental Modelling & Software
Temporal Bayesian classifiers for modelling muscular dystrophy expression data
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Classification of Otoneurological Cases According to Bayesian Probabilistic Models
Journal of Medical Systems
Making time: pseudo time-series for the temporal analysis of cross section data
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data
IEEE Transactions on Information Technology in Biomedicine
The dynamic stage bayesian network: identifying and modelling key stages in a temporal process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
Bayesian network classifiers for time-series microarray data
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Continuous time Bayesian network classifiers
Journal of Biomedical Informatics
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
Improving predictive models of glaucoma severity by incorporating quality indicators
Artificial Intelligence in Medicine
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Objective: 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. Methodology: We carry out an analysis of our proposed spatio-temporal Bayesian classifier and compare it to a number of classifiers from the machine learning and statistical communities. These are all tested on two datasets of VF and clinical data. We investigate the receiver operating characteristics curves, the resulting network structures and also make use of existing anatomical knowledge of the eye in order to validate the discovered models. Results: 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. Conclusion: 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.