@article{ref1,
title="Machine learning improves the accuracy of trauma team activation level assignments in pediatric patients",
journal="Journal of pediatric surgery",
year="2023",
author="Liu, Catherine W. and Chacon, Miranda and Crawford, Loralai and Polydore, Hadassah and Ting, Tiffany and Wilson, Nicole A.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: The assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries. <br><br>METHODS: Following IRB approval, we retrospectively collected data from the institutional trauma registry and electronic medical record at our Pediatric Trauma Center for all patients (age <18 y) who triggered a trauma team activation (1/2014-12/2021), including: demographics, mechanisms of injury, comorbidities, pre-hospital interventions, numeric variables, and the six &quot;Need for Trauma Intervention (NFTI)&quot; criteria. Three machine learning models (Logistic Regression, Random Forest, Support Vector Machine) were tested 1000 times in separate trials using the union of the Cribari and NFTI metrics as ground-truth (Injury Severity Score >15 or positive for any of 6 NFTI criteria = full activation). Model performance was quantified and compared to emergency department (ED) staff. <br><br>RESULTS: ED staff had 75% accuracy, an area under the curve (AUC) of 0.73 ± 0.04, and an F1 score of 0.49. The best performing of all machine learning models, the support vector machine, had 80% accuracy, AUC 0.81 ± 4.1e(-5), F1 Score 0.80, with less variance compared to other models and ED staff. <br><br>CONCLUSIONS: All machine learning models outperformed ED staff in all performance metrics. These results suggest that data-driven methods can optimize trauma team activations in the ED, with potential improvements in both patient safety and hospital resource utilization. TYPE OF STUDY: Economic/Decision Analysis or Modeling Studies. LEVEL OF EVIDENCE: II.<p /> <p>Language: en</p>",
language="en",
issn="0022-3468",
doi="10.1016/j.jpedsurg.2023.09.014",
url="http://dx.doi.org/10.1016/j.jpedsurg.2023.09.014"
}