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dc.contributor.authorGarg, Lalit-
dc.contributor.authorMcClean, Sally-
dc.contributor.authorBarton, Maria-
dc.contributor.authorMeenan, Brian-
dc.contributor.authorFullerton, Ken-
dc.date.accessioned2023-05-15T12:29:11Z-
dc.date.available2023-05-15T12:29:11Z-
dc.date.issued2013-
dc.identifier.citationGarg, L., McClean, S., Barton, M., Meenan, B., & Fullerton, K. (2013). An extended mixture distribution survival tree for patient pathway prognostication. Communications in Statistics-Theory and Methods, 42(16), 2912-2934.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/109417-
dc.description.abstractMixture distribution survival trees are constructed by approximating different nodes in the tree by distinct types of mixture distributions to improve within node homogeneity. Previously, we proposed a mixture distribution survival tree-based method for determining clinically meaningful patient groups from a given dataset of patients’ length of stay. This article extends this approach to examine the interrelationship between length of stay in hospital, outcome measures, and other covariates. We describe an application of this approach to patient pathway and examine the relationship between length of stay in hospital and/or treatment outcome using five-years’ retrospective data of stroke patients.en_GB
dc.language.isoenen_GB
dc.publisherTaylor & Francisen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectStochastic processes -- Mathematical modelsen_GB
dc.subjectHospital utilization -- Length of stayen_GB
dc.subjectCerebrovascular disease -- Patients -- Hospital careen_GB
dc.subjectComputer science -- Mathematicsen_GB
dc.subjectGaussian processesen_GB
dc.titleAn extended mixture distribution survival tree for patient pathway prognosticationen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1080/03610926.2012.725262-
dc.publication.titleCommunications in Statistics-Theory and Methodsen_GB
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