Övervakad maskininlärning för identifiering av svårt skadade traumapatienter
Year: 2024
Type: dissertation
Abstract: Trauma is one of the leading causes of mortality, and is the leading cause of death among young people. The rapid activation of a trauma team for severely injured trauma patients is critical for improving patient outcomes. Severely injured patients must be rapidly identified upon arrival at the emergency department to activate a trauma team, making the need for triage tools that can rapidly and accurately assess the need for trauma team activation (TTA) important. This project explores the use of supervised machine learning models to predict the need for TTA using data from the Swedish Trauma Registry (SweTrau). The research focuses on the use of two machine learning models. A fine-tuned Large Language Model (LLM) for analyzing triage notes, and an AdaBoost ensemble model utilizing decision trees for structured data selected from the SweTrau by consulting an expert. The models were trained to predict the necessity of TTA, defined by a New Injury Severity Score (NISS) greater than 15. The results demonstrate that both models outperform the current triage practices in terms of sensitivity, ability to discern between patients severely injured patients and non-severely injured patients, with a combination of the models output offering the best performance. Although neither model is yet suitable for clinical use, the findings suggest that machine learning has the potential to enhance trauma triage protocols, particularly in identifying severe trauma cases that require TTA that could otherwise be overlooked.
Author: Jóhannsson, Salvar Andri
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7032809260