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Use of a Psychophysiological Script-driven Imagery Experiment to Study Trauma-related Dissociation in Borderline Personality Disorder

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Assessing and Addressing Model Trustworthiness Trade-offs in Trauma Triage.

Douglas A Talbert¹, Katherine L Phillips¹, Katherine E Brown²

¹Computer Science, Tennessee Tech University, 1 William L. Jones Dr., Cookeville, TN 38505, USA.

International Journal of Artificial Intelligence Tools : Architectures, Languages, Algorithms

|January 2, 2026

Summary

This study introduces a new methodology for trauma triage decision-making, balancing model accuracy and complexity. It also incorporates fairness and multi-model approaches for better trauma care insights.

Keywords:

Machine learning trauma triage trustworthy AI

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Area of Science:

Medical Informatics
Machine Learning
Public Health

Background:

Trauma triage decisions are made in challenging environments.
Existing studies present a dichotomy between simple, less accurate models and complex, highly accurate ones.
A need exists for methods to navigate the complexity-accuracy trade-off in trauma triage.

Purpose of the Study:

To examine the trade-off between model complexity and accuracy in trauma triage.
To incorporate group-based fairness into the analysis of machine learning models for trauma triage.
To propose and analyze a multi-model approach for trauma triage decision-making.

Main Methods:

Utilized a registry of 50,644 cases from a Level I Trauma Center.
Developed and applied a methodology to analyze the complexity/accuracy trade-off.
Integrated group-based fairness evaluation and a multi-model strategy.

Main Results:

The methodology provides insights into balancing understandability and accuracy for practitioners.
Group-based fairness analysis offers an additional dimension for model selection.
The multi-model approach helps mitigate trust-related trade-offs.

Conclusions:

Machine learning models in trauma triage require careful consideration of complexity, accuracy, and fairness.
The proposed trade-off analysis is valuable for informed model selection.
This work supports practitioners in optimizing trauma triage systems.