Secondary triage classification using an ensemble random forest technique

Dhifaf Azeez, Gan Kok Beng, M. A Mohd Ali, Ismail Mohd. Saiboon

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time. OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error. METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development. RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data. CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.

Original languageEnglish
Pages (from-to)419-428
Number of pages10
JournalTechnology and Health Care
Volume23
Issue number4
DOIs
Publication statusPublished - 21 Jul 2015

Fingerprint

Triage
Intelligent systems
Malaysia
Processing
Uncertainty
Hospital Emergency Service
Sensitivity and Specificity

Keywords

  • Decision support system
  • Emergency department
  • Random forest
  • Randomized resampling

ASJC Scopus subject areas

  • Biophysics
  • Biomaterials
  • Bioengineering
  • Biomedical Engineering
  • Information Systems
  • Health Informatics

Cite this

Secondary triage classification using an ensemble random forest technique. / Azeez, Dhifaf; Kok Beng, Gan; Ali, M. A Mohd; Mohd. Saiboon, Ismail.

In: Technology and Health Care, Vol. 23, No. 4, 21.07.2015, p. 419-428.

Research output: Contribution to journalArticle

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