Forecasting daily patient visits in an emergency department by ga-ann hybrid approach


Pekel E., Gul M., Celik E.

14th International Symposium on Operational Research, SOR 2017, Bled, Slovenia, 27 - 29 September 2017, vol.2017-September, pp.473-478 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2017-September
  • City: Bled
  • Country: Slovenia
  • Page Numbers: pp.473-478
  • Keywords: Artificial neural network, Emergency department, Forecasting, Genetic algorithm, Patient visit
  • Istanbul University Affiliated: No

Abstract

© 2017 Slovenian Society Informatika. All Rights Reserved.An Emergency Department (ED) plays a crucial role in the health system by providing acute care for patients who attend hospital without prior appointment. An accurate forecasting of patient visits in EDs contributes to health care decision makers to better allocate ED human resources and medical equipment. In this paper, a hybrid genetic algorithm–artificial neural network (GA–ANN) approach is developed. The forecasting performance of the hybrid approach is obtained using the real-world data set collected from a public hospital in Istanbul, Turkey. The hybrid GA–ANN model is shown to perform well in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying GA-ANN to model ED patient arrivals, and the results can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations.