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dc.contributor.authorKadri, Farid
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.date.accessioned2018-04-15T07:13:34Z
dc.date.available2018-04-15T07:13:34Z
dc.date.issued2018-02-07
dc.identifier.citationKadri F, Harrou F, Sun Y (2017) A multivariate time series approach to forecasting daily attendances at hospital emergency department. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/ssci.2017.8280850.
dc.identifier.doi10.1109/ssci.2017.8280850
dc.identifier.urihttp://hdl.handle.net/10754/627438
dc.description.abstractEfficient management of patient demands in emergency departments (EDs) has recently received increasing attention by most healthcare administrations. Forecasting ED demands greatly helps ED's managers to make suitable decisions by optimally allocating the available limited resources to efficiently handle patient attendances. Furthermore, it permits pre-emptive action(s) to mitigate and/or prevent overcrowding situations and to enhance the quality of care. In this work, we present a statistical approach based on a vector autoregressive moving average (VARMA) model for a short term forecasting of daily attendances at an ED. The VARMA model has been validated using an experimental data from the paediatric emergency department (PED) at Lille regional hospital centre, France. The results obtained indicate the effectiveness of the proposed approach in forecasting patient demands.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR- 2015-CRG4-2582.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8280850/
dc.titleA multivariate time series approach to forecasting daily attendances at hospital emergency department
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journal2017 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.contributor.institutionExplosense, Big Data & Analytics services Institut d'Optique Graduate School 33400 Talence, France
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
dc.date.published-online2018-02-07
dc.date.published-print2017-11


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