Model comparison in Emergency Severity Index level prediction

  • Authors:

  • Seifu J. Chonde;Omar M. Ashour;David A. Nembhard;Gül E. Okudan Kremer

  • Affiliations:

  • The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA;The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA;The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA;The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA and School of Engineering Design, The Pennsylva ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

Emergency Department (ED) triage is a process of determining illness severity and accordingly assigning patient priority. The Emergency Severity Index (ESI) is a 5-level acuity categorization system that aides in triage. This paper compared the capabilities of predicting ESI level using ordinal logistic regression (OLR), artificial neural networks (NNs), and naive Bayesian networks (NBNs). Data were obtained from Susquehanna Williamsport Hospital for 947 patients over a one month period in 2008. It contained the assigned ESI level, chief complaint, systolic blood pressure, pulse, respiration rate, temperature, oxygen saturation level (SaO"2), age, gender, and pain level. An OLR model was fit using a subset of these covariates. NBNs and NNs were modeled to relax the inherent assumptions of linearity and covariate independence in logistic regression. These three techniques were compared using incremental training dataset sizes between 50% and 100% of given data. All models were 60% accurate using the entire dataset for training. It was found that NBNs and NNs were robust to data size changes and all models had evaluation speeds of less than 0.5s. At this time the use of NBNs is recommended considering speed, accuracy, data utilization, model flexibility, and interpretability of the model.