Transformer-based classification of user queries for medical consultancy with respect to expert specialization

TL;DR

This paper introduces a transformer-based approach using RuBERT to accurately classify user queries by medical specialization, improving digital healthcare support and directing patients to appropriate specialists.

Contribution

The study presents a novel fine-tuning of RuBERT for medical query classification, achieving high accuracy and broad generalization across multiple medical domains.

Findings

F1-score over 92% on diverse datasets

Effective generalization across cardiology, neurology, dermatology

Enhanced healthcare efficiency and patient guidance

Abstract

The need for skilled medical support is growing in the era of digital healthcare. This research presents an innovative strategy, utilizing the RuBERT model, for categorizing user inquiries in the field of medical consultation with a focus on expert specialization. By harnessing the capabilities of transformers, we fine-tuned the pre-trained RuBERT model on a varied dataset, which facilitates precise correspondence between queries and particular medical specialisms. Using a comprehensive dataset, we have demonstrated our approach's superior performance with an F1-score of over 92%, calculated through both cross-validation and the traditional split of test and train datasets. Our approach has shown excellent generalization across medical domains such as cardiology, neurology and dermatology. This methodology provides practical benefits by directing users to appropriate specialists for prompt and targeted medical advice. It also enhances healthcare system efficiency, reduces practitioner burden, and improves patient care quality. In summary, our suggested strategy facilitates the attainment of specific medical knowledge, offering prompt and precise advice within the digital healthcare field.