The need for skilled medical support is growing in the era of digital healthcare. This research presents an innovative strategy, utilising the RuBERT model, for categorising user inquiries in the field of medical consultation with a focus on expert specialisation. 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 generalisation 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.

本研究通过使用RuBERT模型提出了一种创新策略，将医学咨询领域中的用户查询按专业分类。通过在多样化的数据集上对预训练的RuBERT模型进行微调，实现了查询与特定医学专业之间的精确对应。通过全面的数据集，我们展示了我们方法卓越的性能，F1得分超过92%，通过交叉验证和传统的测试和训练数据集的划分进行计算。我们的方法在心脏病学、神经学和皮肤病学等医学领域表现出优秀的泛化能力。该策略将用户引导至合适的专家以获得及时和有针对性的医疗建议，有助于提高医疗系统效率、减轻从业者负担，并提升患者护理质量。总之，我们提出的策略有助于在数字医疗领域中获取特定的医学知识，提供及时准确的建议。

基于Transformer的医学咨询用户查询分类