Forecasting of sick leave usage among nurses via artificial neural networks

dc.contributor.advisorHasenbein, John J.en
dc.contributor.committeeMemberPopova, Elmiraen
dc.creatorTondukulam Seeth, Srikanthen
dc.date.accessioned2011-02-21T21:12:34Zen
dc.date.available2011-02-21T21:12:34Zen
dc.date.available2011-02-21T21:12:52Zen
dc.date.issued2010-12en
dc.date.submittedDecember 2010en
dc.date.updated2011-02-21T21:12:53Zen
dc.descriptiontexten
dc.description.abstractThis report examines the trends in sick leave usage among nurses in a hospital and aims at creating a forecasting model to predict sick leave usage on a weekly basis using the concept of artificial neural networks (ANN). The data used for the research includes the absenteeism (sick leave) reports for 3 years at a hospital. The analysis shows that there are certain factors that lead to a rise or fall in the weekly sick leave usage. The ANN model tries to capture the effect of these factors and forecasts the sick leave usage for a 1 year horizon based on what it has learned from the behavior of the historical data from the previous 2 years. The various parameters of the model are determined and the model is constructed and tested for its forecasting ability.en
dc.description.departmentOperations Research and Industrial Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-12-2259en
dc.language.isoengen
dc.subjectArtificial neural networksen
dc.subjectForecastingen
dc.subjectNursingen
dc.subjectNursesen
dc.subjectAbsenteeismen
dc.titleForecasting of sick leave usage among nurses via artificial neural networksen
dc.type.genrethesisen
thesis.degree.departmentOperations Research and Industrial Engineeringen
thesis.degree.disciplineOperations Research and Industrial Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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