Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

ABSTRACT:

Background

Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy remains under debate. This study evaluates the effectiveness of machine learning (ML) models in predicting triage decisions and compares their performance to the KTS.

Methods

Data from 4,109 trauma patients at Soroti Regional Referral Hospital, a rural hospital in Uganda, were used to train and evaluate four ML models: Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The models were assessed in regard to accuracy, precision, recall, F1-s

SUBMITTER: Nsubuga M 

PROVIDER: S-EPMC11755936 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature