An AI-Powered Approach for Medical Specialty Triage Using Natural Language Processing and Transformer Models

Abstract

Upon arrival at a hospital, patients require an initial assessment to determine the urgency of their condition and the appropriate medical specialty for their needs. This manual triage process, however, is often time-consuming and resource-intensive, leading to potential delays in care, patient dissatisfaction, and inefficient allocation of specialized medical staff. This study presents an AI-based solution to address this critical challenge. A model is introduced that automatically suggests a suitable medical specialty based on a textual description of a patient’s symptoms, with the aim of improving the efficiency of the hospital’s initial patient triage process. The proposed methodology involves pre-processing a large dataset of over 100,000 patient inquiries from online health forums and conducting a comparative analysis of multiple BERT-based models. Experimental results demonstrate that a domain-specific model, BiomedNLP-PubMedBERT, is par-ticularly effective. To further enhance performance and address the inherent class imbalance in the dataset, a data augmentation strategy using synonym replacement and a weighted loss function was implemented. This combined approach achieved a final weighted F1-score of 92.91%, significantly outperforming the non-augmented baseline models. This work provides a practical path toward building effective automated triage tools that can streamline initial patient assessment and improve operational efficiency in hospital environments. The final model is publicly available for verification and further application.

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