Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17403
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dc.contributor.authorLiu, W-
dc.contributor.authorWang, Z-
dc.contributor.authorLiu, X-
dc.contributor.authorZeng, N-
dc.contributor.authorBell, D-
dc.date.accessioned2019-01-22T12:22:30Z-
dc.date.available2018-10-29-
dc.date.available2019-01-22T12:22:30Z-
dc.date.issued2018-09-
dc.identifier.citationLiu, W., Wang, Z., Liu, X., Zeng, N. and Bell, D. (2019) 'A Novel Particle Swarm Optimization Approach for Patient Clustering From Emergency Departments,' IEEE Transactions on Evolutionary Computation, 23(4), pp. 632-644. doi: 10.1109/TEVC.2018.2878536.en_US
dc.identifier.issn1089-778X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/17403-
dc.description.abstractIn this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of traditional clustering approaches with applications in analyzing real-time patient attendance data from an accident & emergency (A&E) department in a local UK hospital. In the proposed randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one mode to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, randomly occurring time-delays that reflect the history of previous personal best and global best particles are introduced in the velocity updating model in a distributed manner. Eight well-known benchmark functions are employed to evaluate the proposed RODDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the RODDPSO algorithm is successfully exploited in the patient clustering problem for data analysis with respect to a local A&E department in West London. Experiment results demonstrate that the RODDPSO-based clustering method is superior over two other well-known clustering algorithms.en_US
dc.description.sponsorshipEuropean Union’s Horizon 2020 Research and Innovation Programme (INTEGRADDE); 10.13039/501100000266-Engineering and Physical Sciences Research Council; 10.13039/501100000288-Royal Society; Alexander von Humboldt Foundation of Germany;-
dc.format.extent632 - 644-
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectaccident and emergencyen_US
dc.subjectclusteringen_US
dc.subjectdistributed time-delayen_US
dc.subjectevolutionary computationen_US
dc.titleA Novel Particle Swarm Optimization Approach for Patient Clustering from Emergency Departmentsen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TEVC.2018.2878536-
dc.relation.isPartOfIEEE Transactions on Evolutionary Computation-
pubs.issue4-
pubs.publication-statusPublished-
pubs.volume23-
dc.identifier.eissn1941-0026-
Appears in Collections:Dept of Computer Science Research Papers

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