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.

翻译：随着数字医疗时代的到来，对专业医疗支持的需求日益增长。本研究提出了一种创新策略，利用RuBERT模型对医学咨询中的用户问题进行分类，重点关注专家专业领域。通过利用Transformer的强大能力，我们在多样化数据集上对预训练的RuBERT模型进行了微调，实现了问题与特定医学专科之间的精准匹配。基于综合数据集，我们的方法通过交叉验证和传统训练/测试集分割均取得了超过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均表现出优秀的泛化能力。该技术通过将用户引导至相应专家以获取及时且有针对性的医疗建议，具有实用性价值，同时能够提升医疗系统效率、减轻医生负担并改善患者护理质量。总之，我们提出的策略有助于获取特定医学知识，在数字医疗领域提供及时精准的咨询服务。