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.

翻译：数字医疗时代对专业医疗支持的需求日益增长。本研究提出一种创新策略，利用RuBERT模型对医疗咨询领域的用户咨询进行分类，重点关注专家专科领域。通过发挥Transformer架构的能力，我们在多样化数据集上对预训练的RuBERT模型进行微调，实现了咨询与特定医学专科之间的精确映射。基于综合数据集的实验表明，该方法通过交叉验证和传统训练-测试集划分两种评估方式，均取得了超过92%的F1分数，展现出卓越性能。该方法在心脏病学、神经病学和皮肤病学等医学领域表现出优异的泛化能力。该技术路径通过引导用户快速匹配适当专科医生获取针对性医疗建议，具有显著实践价值；同时能够提升医疗系统效率、减轻执业医师负担、改善患者护理质量。综上所述，我们提出的策略促进了精准医学知识的获取，为数字医疗领域提供即时准确的诊疗建议。