Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1369
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dc.contributor.authorSwift, S-
dc.contributor.authorLiu, X-
dc.coverage.spatial20en
dc.date.accessioned2007-12-03T20:24:46Z-
dc.date.available2007-12-03T20:24:46Z-
dc.date.issued2002-
dc.identifier.citationArtificial Intelligence in Medicine, Volume 24, Issue 1, Pages 5-24en
dc.identifier.urihttp://linkinghub.elsevier.com/retrieve/pii/S0933365701000951en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/1369-
dc.description.abstractIn 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 data set 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.en
dc.format.extent278798 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherElsevieren
dc.subjectVisual field deteriorationen
dc.subjectGlaucomaen
dc.subjectGenetic algorithmsen
dc.subjectMultivariate time seriesen
dc.titlePredicting glaucomatous visual field deterioration through short multivariate time series modellingen
dc.typeResearch Paperen
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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