Dhifaf Azeez1, K B Gan2, M A Mohd Ali2, M S Ismail3. 1. Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq. 2. Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Malaysia. 3. Department of Emergency Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia.
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.
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.
Entities:
Keywords:
Decision support system; emergency department; random forest; randomized resampling
Authors: Marta Fernandes; Rúben Mendes; Susana M Vieira; Francisca Leite; Carlos Palos; Alistair Johnson; Stan Finkelstein; Steven Horng; Leo Anthony Celi Journal: PLoS One Date: 2020-03-03 Impact factor: 3.240
Authors: Marta Fernandes; Rúben Mendes; Susana M Vieira; Francisca Leite; Carlos Palos; Alistair Johnson; Stan Finkelstein; Steven Horng; Leo Anthony Celi Journal: PLoS One Date: 2020-04-02 Impact factor: 3.240