Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models

Abstract The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. There is a need to establish an automated tool to identify the severity of trauma injuries across various body regions. We gather trauma registry data from a Level I Trauma Center at the University of Wisconsin-Madison (UW Health) between 2015 and 2019. Our study utilizes clinical documents and structured electronic health records (EHR) variables linked with the trauma registry data to create two machine learning models with different approaches to representing text. The first one fuses concept unique identifiers (CUIs) extracted from free text with structured EHR variables, while the second one integrates free text with structured EHR variables. Both models demonstrate impressive performance in categorizing leg injuries, achieving high accuracy with macro-F1 scores of around 0.8. Additionally, they show considerable accuracy, with macro- F1 scores exceeding 0.6, in assessing injuries in the areas of the chest and head. Temporal validation is conducted to ensure the models’ temporal generalizability. We show in our variable importance analysis that the most important features in the model have strong face validity in determining clinically relevant trauma injuries.