Figure 1. Overview of Model Development.

The first column represents the data preparation. Black and White patients with a penetrating mechanism of injury were selected from the 2010-2016 American College of Surgeons Trauma Quality Improvement Program database. Data variables were reviewed, and relevant ones were included in the model. Exploratory data analysis (B) was performed by identifying cross-correlation between different features (heat map) and determining feature importance (Shapely plot). The second column represents the machine learning. Fairness adjustment (C) was achieved by computing a weighted severity metric based on relative feature importance, followed by application of demographic parity across all features. Optimal classification trees (OCTs) were developed to predict discharge disposition using fairness-adjusted and unadjusted data. The third column represents model evaluation. Model performance (E) was compared between fairness-adjusted and unadjusted OCTs using the area under the receiver operating characteristic curve (AUC).