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Stochastic workflow modeling in a surgical ward : towards simulating and predicting patient flow
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dc.contributor.author | Olling Back, Christoffer | |
dc.contributor.author | Manataki, Areti | |
dc.contributor.author | Papanastasiou, Angelos | |
dc.contributor.author | Harrison, Ewen | |
dc.contributor.editor | Ye, Xuesong | |
dc.contributor.editor | Soares, Filipe | |
dc.contributor.editor | De Maria, Elisabetta | |
dc.contributor.editor | Gómez Vilda, Pedro | |
dc.contributor.editor | Cabitza, Federico | |
dc.contributor.editor | Fred, Ana | |
dc.contributor.editor | Gamboa, Hugo | |
dc.date.accessioned | 2021-04-09T09:30:21Z | |
dc.date.available | 2021-04-09T09:30:21Z | |
dc.date.issued | 2021 | |
dc.identifier | 272601916 | |
dc.identifier | 0cbd1590-b873-403d-9ff5-08d1620b96f8 | |
dc.identifier | 85107282158 | |
dc.identifier.citation | Olling 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_28 | en |
dc.identifier.isbn | 9783030723781 | |
dc.identifier.isbn | 9783030723798 | |
dc.identifier.other | ORCID: /0000-0003-3698-8535/work/92020387 | |
dc.identifier.uri | https://hdl.handle.net/10023/22995 | |
dc.description.abstract | Intelligent 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.format.extent | 1400034 | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartof | Biomedical Engineering Systems and Technologies | en |
dc.relation.ispartofseries | Communications in Computer and Information Science | en |
dc.subject | Surgery | en |
dc.subject | Surgical workflow | en |
dc.subject | Bayesian network | en |
dc.subject | Petri Nets | en |
dc.subject | Simulation | en |
dc.subject | Data mining | en |
dc.subject | Patient flow | en |
dc.subject | Process mining | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | RD Surgery | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | RD | en |
dc.title | Stochastic workflow modeling in a surgical ward : towards simulating and predicting patient flow | en |
dc.type | Book item | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.identifier.doi | https://doi.org/10.1007/978-3-030-72379-8_28 |
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