Affiliations: [a] Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq | [b] Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Malaysia | [c] Department of Emergency Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
Correspondence:
[*]
Corresponding author: K.B. Gan, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Malaysia. Tel.: +603 8921 7149; Fax: +603 89118359; E-mail:gankokbeng@ukm.edu.my
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.
Keywords: Decision support system, emergency department, random forest, randomized resampling
