A multivariate time series approach to forecasting daily attendances at hospital emergency department
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-02-07
Print Publication Date2017-11
Permanent link to this recordhttp://hdl.handle.net/10754/627438
MetadataShow full item record
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.
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.
SponsorsThis 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.