KUTS: A Foundational Triage System for Improving Accuracy in Mid-Level Emergency Classifications

Abstract Triage is of great importance in the procedure of the emergency diagnosis, which needs artificial intelligence to assist due to the scarcity of medical resources. However, AI models for aiding triage have difficulty in identifying levels that are difficult or ambiguous for human clinicians to distinguish. Here, to address this challenge and improve the performance of triage models, we propose KUTS, a foundational classification model for emergency triage, which leverages a knowledge prompt-tuning encoder and an uncertainty-based classifier. KUTS takes tabular data and chief complaints as input, and then generates the recommended triage level for human clinicians. On the most difficult level for human to distinguish (Level 3), our KUTS significantly outperformed the previous shallow single-modal methods, deep single-modal methods and deep multi-modal methods by an average of 14.85%, 28.38% and 11.14%, respectively. Besides, on all the triage levels, our KUTS also outperformed the previous shallow single-modal methods, deep single-modal methods and deep multi-modal methods by an average of 9.02%, 9.45% and 4.18%, respectively. KUTS provides a foundational framework and paradigm for the study of emergency triage, and can be extended to scenarios that require pre-hospital emergency treatment such as disasters and accidents.