Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools.

ABSTRACT:

Background

Major incidents (MIs) are an important cause of death and disability. Triage tools are crucial to identifying priority 1 (P1) patients-those needing time-critical, life-saving interventions. Existing expert opinion-derived tools have limited evidence supporting their use. This study employs machine learning (ML) to develop and validate models for novel primary and secondary triage tools.

Methods

Adults (16+ years) from the UK Trauma Audit and Research Network (TARN) registry (January 2008-December 2017) served as surrogates for MI victims, with P1 patients identified using predefined criteria. The TARN database was split chronologically into model training and testing (70:30) datasets. Input variables included physiological parameters, age, mechanism and anatomica

SUBMITTER: Xu Y 

PROVIDER: S-EPMC10894820 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature