Predicting glaucomatous visual field deterioration through short multivariate time series modelling.

Stephen Swift; Xiaohui Liu

doi:10.1016/s0933-3657(01)00095-1

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Predicting glaucomatous visual field deterioration through short multivariate time series modelling.

Stephen Swift·Xiaohui Liu

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Abstract

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.

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Algorithms Computer Simulation Genetic Predisposition to Disease Glaucoma Humans Models, Genetic Models, Statistical Multivariate Analysis Predictive Value of Tests Visual Fields Journal Article Research Support, Non-U.S. Gov't

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Stephen Swift, Xiaohui Liu. 2002. Predicting glaucomatous visual field deterioration through short multivariate time series modelling.. https://doi.org/10.1016/s0933-3657(01)00095-1

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