AI Article Synopsis

  • Researchers developed and validated machine learning models to identify pediatric trauma patients at high risk of dying in warzones, aiming to improve care in limited-resource settings.
  • Using data from the Department of Defense Trauma Registry (2008-2016), models like random forest (RF) and support vector machine (SVM) were tested against other techniques to find the best predictor of mortality among these patients.
  • The study found that RF outperformed other models in predicting mortality risk, highlighting the potential of advanced machine learning to enhance medical decision-making for critically injured children.

Article Abstract

Background: Identification of pediatric trauma patients at the highest risk for death may promote optimization of care. This becomes increasingly important in austere settings with constrained medical capabilities. This study aimed to develop and validate predictive models using supervised machine learning (ML) techniques to identify pediatric warzone trauma patients at the highest risk for mortality.

Methods: Supervised learning approaches using logistic regression (LR), support vector machine (SVM), neural network (NN), and random forest (RF) models were generated from the Department of Defense Trauma Registry, 2008-2016. Models were tested and compared to determine the optimal algorithm for mortality.

Results: A total of 2,007 patients (79% male, median age range 7-12 years old, 62.5% sustaining penetrating injury) met the inclusion criteria. Severe injury (Injury Severity Score > 15) was noted in 32.4% of patients, while overall mortality was 7.13%. The RF and SVM models displayed recall values of .9507 and .9150, while LR and NN displayed values of .8912 and .8895, respectively. Random forest (RF) outperformed LR, SVM, and NN on receiver operating curve (ROC) analysis demonstrating an area under the ROC of .9752 versus .9252, .9383, and .8748, respectively.

Conclusion: Machine learning (ML) techniques may prove useful in identifying those at the highest risk for mortality within pediatric trauma patients from combat zones. Incorporation of advanced computational algorithms should be further explored to optimize and supplement the diagnostic and therapeutic decision-making process.

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Source
http://dx.doi.org/10.1093/milmed/usac171DOI Listing

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