Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Intelligent Data Analysis in Medicine and Pharmacology
Intelligent Data Analysis in Medicine and Pharmacology
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Practical Statistics for Medical Research
Practical Statistics for Medical Research
Efficient mining of statistical dependencies
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data
IEEE Transactions on Information Technology in Biomedicine
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy
Artificial Intelligence in Medicine
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
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In bio-medical domains there are many applications involving the modelling of multivariate time series (MTS) data. One area that has been largely overlooked so far is the particular type of time series where the dataset consists of a large number of variables but with a small number of observations. In this paper, we describe the development of a novel computational method based on genetic algorithms that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the order and associated parameters as a whole step. We apply this method to the prediction and modelling of glaucomatous visual field deterioration.