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dc.contributor.authorShahabikargar, Zahra
dc.contributor.authorKhanna, Sankalp
dc.contributor.authorSattar, Adbul
dc.contributor.authorLind, James
dc.contributor.editorRyan, A
dc.contributor.editorSchaper, LK
dc.contributor.editorWhetton, S
dc.date.accessioned2018-10-12T01:33:07Z
dc.date.available2018-10-12T01:33:07Z
dc.date.issued2017
dc.identifier.isbn978-1-61499-782-5
dc.identifier.doi10.3233/978-1-61499-783-2-133
dc.identifier.urihttp://hdl.handle.net/10072/346408
dc.description.abstractAccurate surgery duration estimation is essential for efficient use of hospital operating theatres and the scheduling of elective patients. This study focuses on analysing the performance of previously developed surgery duration prediction algorithms at a specialty level to gain further insight on their performance. We also evaluate algorithm performance after applying filtering to exclude unreliable data from modelling, and develop and validate new ensemble approaches for prediction. These are shown to significantly improve the prediction accuracy of the algorithms. Employing filtered data delivers a reduction in overall prediction error of 44% (Mean Absolute Percentage Error from 0.68 to 0.38) employing the Random Forests algorithm, while using the newly developed ensemble approach delivers a Mean Absolute Percentage Error of 0.31, a reduction of 55% when compared to the original error, and a reduction of 18% when compared to the Random Forests performance on filtered data.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIOS Press
dc.publisher.placeUnited States
dc.relation.ispartofbooktitleIntegrating and Connecting Care
dc.relation.ispartofchapter20
dc.relation.ispartofpagefrom133
dc.relation.ispartofpageto138
dc.subject.fieldofresearchInformation Systems not elsewhere classified
dc.subject.fieldofresearchLibrary and Information Studies
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchcode080699
dc.subject.fieldofresearchcode0807
dc.subject.fieldofresearchcode1117
dc.titleImproved Prediction of Procedure Duration for Elective Surgery
dc.typeBook chapter
dc.type.descriptionB2 - Chapters (Other)
dc.type.codeB - Book Chapters
dcterms.licensehttp://creativecommons.org/licenses/by-nc/4.0/deed.en_US
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2017 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)., which permits unrestricted, non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorKhanna, Sankalp
gro.griffith.authorShahabi Kargar, Zahra


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