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dc.contributor.authorOlling Back, Christoffer
dc.contributor.authorManataki, Areti
dc.contributor.authorHarrison, Ewen
dc.date.accessioned2021-02-08T15:30:08Z
dc.date.available2021-02-08T15:30:08Z
dc.date.issued2020-03-18
dc.identifier.citationOlling Back , C , Manataki , A & Harrison , E 2020 , Mining patient flow patterns in a surgical ward . in Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies . vol. 5 , SciTePress , pp. 273-283 , 13th International Conference on Health Informatics, part of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies , Valetta , Malta , 24/02/20 . https://doi.org/10.5220/0009181302730283en
dc.identifier.citationconferenceen
dc.identifier.isbn9789897583988
dc.identifier.otherPURE: 272601481
dc.identifier.otherPURE UUID: 27f38bf2-f403-43f9-847d-77041791e5ad
dc.identifier.otherScopus: 85083715322
dc.identifier.otherORCID: /0000-0003-3698-8535/work/87846159
dc.identifier.urihttps://hdl.handle.net/10023/21387
dc.description.abstractSurgery is a highly critical and costly procedure, and there is an imperative need to improve the efficiency in surgical wards. Analyzing surgical patient flow and predicting cycle times of different peri-operative phases can help improve the scheduling and management of surgeries. In this paper, we propose a novel approach to mining temporal patterns of surgical patient flow with the use of Bayesian belief networks. We present and compare three classes of probabilistic models and we evaluate them with respect to predicting cycle times of individual phases of patient flow. The results of this study support previous work that surgical times are log-normally distributed. We also show that the inclusion of a clustering pre-processing step improves the performance of our models considerably.
dc.format.extent11
dc.language.isoeng
dc.publisherSciTePress
dc.relation.ispartofProceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologiesen
dc.rightsCopyright © 2020 by SCITEPRESS - Science and Technology Publications Lda. Open Access Article. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence.en
dc.subjectBayesian networken
dc.subjectData miningen
dc.subjectPatient flowsen
dc.subjectProcess miningen
dc.subjectSurgeryen
dc.subjectSurgical workflowen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.subjectR Medicineen
dc.subject3rd-DASen
dc.subject.lccQA75en
dc.subject.lccTen
dc.subject.lccRen
dc.titleMining patient flow patterns in a surgical warden
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.5220/0009181302730283


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