Application of the Weka Machine Learning Library to Hospital Ward Occupancy Problems

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dc.contributor.authorHarris, Ianeng
dc.contributor.authorDenzinger, Joergeng
dc.contributor.authorYergens, Deaneng
dc.date.accessioned2008-02-27T16:59:13Z
dc.date.available2008-02-27T16:59:13Z
dc.date.computerscience2008-01-04eng
dc.date.issued2008-01-04eng
dc.description.abstractWe explore the potential of applying machine learning techniques to the management of patient ow in hospitals. For this project, we have obtained the Weka machine learning library and three years of historical ward occupancy data from Rockyview Hospital. We use Weka's classifier algorithms and the Rockyview data to build a model of patient ow through each ward. Using Weka, we then attempt to predict ward occupancy problems on any given day using the model and the ward conditions from the previous day. This process is repeated for all eighteen wards. Finally, we obtain rules (sets of ward conditions that warn of an impending occupancy problem) for each ward and present the results.eng
dc.description.notesWe are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.caeng
dc.identifier.department2007-884-36eng
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/30573
dc.identifier.urihttp://hdl.handle.net/1880/45853
dc.language.isoEngeng
dc.publisher.corporateUniversity of Calgaryeng
dc.publisher.facultyScienceeng
dc.subjectComputer Scienceeng
dc.titleApplication of the Weka Machine Learning Library to Hospital Ward Occupancy Problemseng
dc.typeunknown
thesis.degree.disciplineComputer Scienceeng
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