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Mining patient flow patterns in a surgical ward
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dc.contributor.author | Olling Back, Christoffer | |
dc.contributor.author | Manataki, Areti | |
dc.contributor.author | Harrison, Ewen | |
dc.date.accessioned | 2021-02-08T15:30:08Z | |
dc.date.available | 2021-02-08T15:30:08Z | |
dc.date.issued | 2020-03-18 | |
dc.identifier.citation | Olling 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/10.5220/0009181302730283 | en |
dc.identifier.citation | conference | en |
dc.identifier.isbn | 9789897583988 | |
dc.identifier.other | PURE: 272601481 | |
dc.identifier.other | PURE UUID: 27f38bf2-f403-43f9-847d-77041791e5ad | |
dc.identifier.other | Scopus: 85083715322 | |
dc.identifier.other | ORCID: /0000-0003-3698-8535/work/87846159 | |
dc.identifier.uri | https://hdl.handle.net/10023/21387 | |
dc.description.abstract | Surgery 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.extent | 11 | |
dc.language.iso | eng | |
dc.publisher | SciTePress | |
dc.relation.ispartof | Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies | en |
dc.rights | Copyright © 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.subject | Bayesian network | en |
dc.subject | Data mining | en |
dc.subject | Patient flows | en |
dc.subject | Process mining | en |
dc.subject | Surgery | en |
dc.subject | Surgical workflow | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | T Technology | en |
dc.subject | R Medicine | en |
dc.subject | 3rd-DAS | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | T | en |
dc.subject.lcc | R | en |
dc.title | Mining patient flow patterns in a surgical ward | en |
dc.type | Conference item | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.identifier.doi | https://doi.org/10.5220/0009181302730283 |
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