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dc.contributor.authorElbattah, Mahmoud
dc.contributor.authorMolloy, Owen
dc.contributor.authorZeigler, Bernard P.
dc.date.accessioned2019-04-26T18:57:17Z
dc.date.available2019-04-26T18:57:17Z
dc.date.issued2018
dc.identifier.citationElbattah, M., Molloy, O., & Zeigler, B. P. (2018, December). Designing care pathways using simulation modeling and machine learning. In 2018 Winter Simulation Conference (WSC) (pp. 1452-1463). IEEE.en_US
dc.identifier.issn0891-7736
dc.identifier.doi10.1109/WSC.2018.8632360
dc.identifier.urihttp://hdl.handle.net/10150/632118
dc.description.abstractThe development of care pathways is increasingly becoming an instrumental artefact towards improving the quality of care and cutting costs. This paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study goes through a use case in relation to elderly healthcare in Ireland, with a particular focus on the hip-fracture care scheme. Initially, unsupervised ML is utilized to extract knowledge from the Irish Hip Fracture Database. Data clustering is specifically applied to learn potential insights pertaining to patient characteristics, care-related factors, and outcomes. Subsequently, the data-driven knowledge is utilized within the process of simulation model development. Generally, the framework is conceived to provide a systematic approach for developing healthcare policies that help optimize the quality and cost of care.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2018 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleDESIGNING CARE PATHWAYS USING SIMULATION MODELING AND MACHINE LEARNINGen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.identifier.journal2018 WINTER SIMULATION CONFERENCE (WSC)en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
refterms.dateFOA2019-04-26T18:57:18Z


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