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Updated: Apr 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Secondary triage classification using an ensemble random forest technique.

Dhifaf Azeez1, K B Gan2, M A Mohd Ali2

  • 1Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|March 21, 2015
PubMed
Summary
This summary is machine-generated.

An intelligent triage system using random forest and resampling significantly reduced errors in emergency department patient assessment. This approach improves accuracy and efficiency in critical care settings.

Keywords:
Decision support systememergency departmentrandom forestrandomized resampling

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Last Updated: Apr 16, 2026

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Area of Science:

  • Emergency Medicine
  • Artificial Intelligence
  • Data Science

Background:

  • Emergency department triage is complex, involving uncertainty and ambiguity.
  • Rapid triage (2-5 minutes) is crucial to prevent fatalities and reduce wait times.
  • Human error is a significant concern in emergency triage processes.

Purpose of the Study:

  • To develop an intelligent triage system to minimize human error in emergency departments.
  • To create a secondary triage model using advanced machine learning techniques.

Main Methods:

  • The study employed an ensemble random forest technique for secondary triage modeling.
  • A randomized resampling method was utilized to address data imbalance before model development.
  • The system was based on the objective primary triage scale (OPTS).

Main Results:

  • The random forest model with 300% resampling achieved a low out-of-bag error of 0.02, compared to 0.37 without pre-processing.
  • The developed model demonstrated high performance with a sensitivity of 0.98 and specificity of 0.89 on unseen data.
  • Resampling effectively balanced the dataset, improving model robustness.

Conclusions:

  • The combination of random forest and randomized resampling effectively reduces variance and bias, respectively.
  • This intelligent system shows promise for enhancing the accuracy and efficiency of emergency department triage.
  • The findings suggest a significant improvement in handling complex triage scenarios.