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

Working papers by Cornell University. Series math "arxiv.org". 2023. P. 1–16.

Lyutkin D., Soloviev A., Zhukov D., Pozdnyakov D., Malik M. S., Ignatov D. I.

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.

Keywords: Transformers, Query Matching, Medical Texts, Many-class Learning

Publication based on the results of: Models and method for analysis of unstructured data, data mining and recommender systems (2023)