The Trauma Severity Model: An Ensemble Learning Approach to Risk Prediction

Abstract

Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant in the context of mortality risk prediction for trauma patients, as researchers have focused exclusively on the use of generalized linear models for risk prediction, and generalized linear models may be too inflexible to capture the potentially complex relationships in trauma data. Due to this, we propose a machine learning model, the Trauma Severity Model (TSM), for risk prediction. In order to validate TSMs performance, this study compares TSM to three established risk prediction models: the Bayesian Logistic Injury Severity Score, the Harborview Assessment for Risk of Mortality, and the Trauma Mortality Prediction Model. Our results indicate that TSM has superior performance, and thereby provides improved risk prediction.\n\nHighlightsO_LIWe propose an ensemble machine learning model for trauma risk prediction.\nC_LIO_LIA hyper-parameter search scheme is proposed for model development.\nC_LIO_LIWe compare our model to established models for trauma risk prediction.\nC_LIO_LIOur model improves over established models for each performance metric considered.\nC_LI