TriagE-NLU: A Natural Language Understanding System for Clinical Triage and Intervention in Multilingual Emergency Dialogues

Béatrix-May Balaban, Ioan Sacală () and Alina-Claudia Petrescu-Niţă
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Béatrix-May Balaban: Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania
Ioan Sacală: Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania
Alina-Claudia Petrescu-Niţă: Faculty of Applied Sciences, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania

Future Internet, 2025, vol. 17, issue 7, 1-27

Abstract: Telemedicine in emergency contexts presents unique challenges, particularly in multilingual and low-resource settings where accurate, clinical understanding and triage decision support are critical. This paper introduces TriagE-NLU, a novel multilingual natural language understanding system designed to perform both semantic parsing and clinical intervention classification from emergency dialogues. The system is built on a federated learning architecture to ensure data privacy and adaptability across regions and is trained using TriageX, a synthetic, clinically grounded dataset covering five languages (English, Spanish, Romanian, Arabic, and Mandarin). TriagE-NLU integrates fine-tuned multilingual transformers with a hybrid rules-and-policy decision engine, enabling it to parse structured medical information (symptoms, risk factors, temporal markers) and recommend appropriate interventions based on recognized patterns. Evaluation against strong multilingual baselines, including mT5, mBART, and XLM-RoBERTa, demonstrates superior performance by TriagE-NLU, achieving F1 scores of 0.91 for semantic parsing and 0.89 for intervention classification, along with 0.92 accuracy and a BLEU score of 0.87. These results validate the system’s robustness in multilingual emergency telehealth and its ability to generalize across diverse input scenarios. This paper establishes a new direction for privacy-preserving, AI-assisted triage systems.

Keywords: multilingual NLP; emergency telemedicine; semantic parsing; intervention classification; federated learning; low-resource languages; medical dialogue; privacy-preserving (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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