A Comparison of Multivariate SARIMA and SVM Models for Emergency Department Admission Prediction

Alexander Zlotnik, Juan Manuel Montero Martínez, Ascención Gallardo-Antolín

2013

Abstract

A comparison of multivariate SARIMA model with a multivariate regression-based time series based on a Support Vector Machine model was performed for emergency department admissions prediction. The same input variables were used in both models. Both models were trained with consecutive daily samples of data corresponding to the January 2009 – August 2012 period (n=1339). Performance was evaluated on the September 2012 test dataset (n=30). The results obtained with the Support Vector Machine were found to be more accurate with a 46,53% RMSE improvement and a 48,89% MAE improvement on the train set. The experiment was repeated six times with varying time periods. The SVM approach produced better results in all cases. Error measurements on the test set were compared with a paired T test. The differences between all comparisons were found to be statistically significant in all cases with a 95% CI.

References

  1. Monte, E., Roca, J. and Vilardell, L. On the self-similar distribution of the emergency ward arrivals time series. Fractals-an Interdisciplinary Journal on the Complex Geometry 10, 413-428 (2002).
  2. Wargon, M., Guidet, B., Hoang, T. D. and Hejblum, G. A systematic review of models for forecasting the number of emergency department visits. Emerg Med J 26, 395-399, doi:26/6/395 [pii] (2009).
  3. Batal, H., Tench, J., McMillan, S., Adams, J. and Mehler, P. S. Predicting patient visits to an urgent care clinic using calendar variables. Academic Emergency Medicine 8, 48-53 (2001).
  4. Jones, S. A., Joy, M. P. and Pearson, J. Forecasting demand of emergency care. Health Care Management Science 5, 297-305 (2002).
  5. Schweigler, L. M. et al. Forecasting models of emergency department crowding. Acad Emerg Med 16, 301-308, doi:ACEM356 [pii] (2009).
  6. Metzger, K. B. et al. Ambient air pollution and cardiovascular emergency department visits. Epidemiology 15, 46 (2004).
  7. Stieb, D. M., Szyszkowicz, M., Rowe, B. H. and Leech, J. A. Air pollution and emergency department visits for cardiac and respiratory conditions: a multi-city timeseries analysis. Environ Health 8, 25 (2009).
  8. Schaffer, A., Muscatello, D., Broome, R., Corbett, S. and Smith, W. Emergency department visits, ambulance calls, and mortality associated with an exceptional heat wave in Sydney, Australia, 2011: a time-series analysis. Environmental Health 11, 3 (2012).
  9. Sun, Y., Heng, B. H., Seow, Y. T. and Seow, E.: Forecasting daily attendances at an emergency department to aid resource planning. BMC Emergency Medicine 9, 1 (2009).
  10. Joy, M. P. and Jones, S.: in ESANN'2005 proceedings - European Symposium on Artificial Neural Networks 13th.
  11. Palanca-Sánchez, I., Elola-Somozam, J. and MejíaEstebaranz, F.: Unidad de urgencias hospitalarias: Estándares y recomendaciones. Informes, estudios e investigación. Madrid: Ministerio de Sanidad y Política Social (2010).
  12. McCarthy, M. L. et al. The challenge of predicting demand for emergency department services. Academic Emergency Medicine 15, 337-346 (2008).
  13. Abraham, G., Byrnes, G. B. & Bain, C. A. Short-term forecasting of emergency inpatient flow. IEEE Transactions on Information Technology in Biomedicine 13 (2009).
  14. Darlington, R. B. Regression and linear models. (McGraw-Hill New York, 1990).
  15. Mukherjee, S., Osuna, E. and Girosi, F. in Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop, 511-520 (IEEE).
  16. Smola, A. J. and Schölkopf, B.: A tutorial on support vector regression. Statistics and computing 14, 199- 222 (2004).
  17. Shevade, S. K., Keerthi, S., Bhattacharyya, C. and Murthy, K. R. K. Improvements to the SMO algorithm for SVM regression. Neural Networks, IEEE Transactions on 11, 1188-1193 (2000).
Download


Paper Citation


in Harvard Style

Zlotnik A., Montero Martínez J. and Gallardo-Antolín A. (2013). A Comparison of Multivariate SARIMA and SVM Models for Emergency Department Admission Prediction . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013) ISBN 978-989-8565-37-2, pages 245-249. DOI: 10.5220/0004326102450249


in Bibtex Style

@conference{healthinf13,
author={Alexander Zlotnik and Juan Manuel Montero Martínez and Ascención Gallardo-Antolín},
title={A Comparison of Multivariate SARIMA and SVM Models for Emergency Department Admission Prediction},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)},
year={2013},
pages={245-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004326102450249},
isbn={978-989-8565-37-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)
TI - A Comparison of Multivariate SARIMA and SVM Models for Emergency Department Admission Prediction
SN - 978-989-8565-37-2
AU - Zlotnik A.
AU - Montero Martínez J.
AU - Gallardo-Antolín A.
PY - 2013
SP - 245
EP - 249
DO - 10.5220/0004326102450249