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dc.contributor.authorOlling Back, Christoffer
dc.contributor.authorManataki, Areti
dc.contributor.authorPapanastasiou, Angelos
dc.contributor.authorHarrison, Ewen
dc.contributor.editorYe, Xuesong
dc.contributor.editorSoares, Filipe
dc.contributor.editorDe Maria, Elisabetta
dc.contributor.editorGómez Vilda, Pedro
dc.contributor.editorCabitza, Federico
dc.contributor.editorFred, Ana
dc.contributor.editorGamboa, Hugo
dc.date.accessioned2021-04-09T09:30:21Z
dc.date.available2021-04-09T09:30:21Z
dc.date.issued2021
dc.identifier.citationOlling Back , C , Manataki , A , Papanastasiou , A & Harrison , E 2021 , Stochastic workflow modeling in a surgical ward : towards simulating and predicting patient flow . in X Ye , F Soares , E De Maria , P Gómez Vilda , F Cabitza , A Fred & H Gamboa (eds) , Biomedical Engineering Systems and Technologies : 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers . Communications in Computer and Information Science , vol. 1400 , Springer , Cham , pp. 565-591 . https://doi.org/10.1007/978-3-030-72379-8_28en
dc.identifier.isbn9783030723781
dc.identifier.isbn9783030723798
dc.identifier.otherPURE: 272601916
dc.identifier.otherPURE UUID: 0cbd1590-b873-403d-9ff5-08d1620b96f8
dc.identifier.otherORCID: /0000-0003-3698-8535/work/92020387
dc.identifier.urihttp://hdl.handle.net/10023/22995
dc.description.abstractIntelligent systems play an increasingly central role in healthcare systems worldwide. Nonetheless, operational friction represents an obstacle to full utilization of scarce resources and improvement of service standards. In this paper we address the challenge of developing data-driven models of complex workflow systems - a prerequisite for harnessing intelligent technologies for workflow improvement. We present a proof-of-concept model parametrized using real-world data and constructed based on domain knowledge from the Royal Infirmary of Edinburgh, demonstrating how off-the-shelf process mining, machine learning and stochastic process modeling tools can be combined to build predictive models that capture complex control flow, constraints, policies and guidelines.
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofBiomedical Engineering Systems and Technologiesen
dc.relation.ispartofseriesCommunications in Computer and Information Scienceen
dc.rightsCopyright © Springer Nature Switzerland AG 2021. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-030-72379-8_28.en
dc.subjectSurgeryen
dc.subjectSurgical workflowen
dc.subjectBayesian networken
dc.subjectPetri Netsen
dc.subjectSimulationen
dc.subjectData miningen
dc.subjectPatient flowen
dc.subjectProcess miningen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRD Surgeryen
dc.subject.lccQA75en
dc.subject.lccRDen
dc.titleStochastic workflow modeling in a surgical ward : towards simulating and predicting patient flowen
dc.typeBook itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1007/978-3-030-72379-8_28


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