The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study.

ABSTRACT:

Background

Accurate prehospital trauma triage is crucial for identifying critically injured patients and determining the level of care. In the prehospital setting, time and data are often scarce, limiting the complexity of triage models. The aim of this study was to assess whether, compared with logistic regression, the advanced machine learner XGBoost (eXtreme Gradient Boosting) is associated with reduced prehospital trauma mistriage.

Methods

We conducted a simulation study based on data from the US National Trauma Data Bank (NTDB) and the Swedish Trauma Registry (SweTrau). We used categorized systolic blood pressure, respiratory rate, Glasgow Coma Scale and age as our predictors. The outcome was the difference in under- and overtriage rates between the models for different

SUBMITTER: Larsson A 

PROVIDER: S-EPMC8215793 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature