Predicting glaucomatous visual field deterioration through short multivariate time series modelling.
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.
Swift
Stephen
S
Department of Information Systems and Computing, Brunel University, UB8 3PH, Middlesex, Uxbridge, UK.
Liu
Xiaohui
X
D000465:Algorithms; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005901:Glaucoma / Q000235:genetics* / Q000503:physiopathology; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D014794:Visual Fields / Q000502:physiology*