Abstract
Trauma triage occurs in suboptimal environments for making consequential decisions. Published triage studies demonstrate the extremes of the complexity/accuracy trade-off, either studying simple models with poor accuracy or very complex models with accuracies nearing published goals. Using a Level I Trauma Center's registry cases ( n = 50644 ), this study describes, uses, and derives observations from a methodology to more thoroughly examine this trade-off. This or similar methods can provide the insight needed for practitioners to balance understandability with accuracy. Additionally, this study incorporates an evaluation of group-based fairness into this trade-off analysis to provide an additional dimension of insight into model selection. Lastly, this paper proposes and analyzes a multi-model approach to mitigating trust-related trade-offs. The experiments allow us to draw several conclusions regarding the machine learning models in the domain of trauma triage and demonstrate the value of our trade-off analysis to provide insight into choices regarding model complexity, model accuracy, and model fairness.