TY - JOUR T1 - The trauma severity model: An ensemble machine learning approach to risk prediction JF - bioRxiv DO - 10.1101/210575 SP - 210575 AU - Michael T. Gorczyca AU - Nicole C. Toscano AU - Julius D. Cheng Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/10/29/210575.abstract N2 - 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 TSM’s 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.Highlights:We propose an ensemble machine learning model for trauma risk prediction.A hyper-parameter search scheme is proposed for model development.We compare our model to established models for trauma risk prediction.Our model improves over established models for each performance metric considered. ER -