Development and Analysis of Methods for Processing Medical Texts

Student: Alisa Kirdyashova

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2024

In recent years, significant progress has been made in the development of transformer-based models for practical applications. Notably, substantial advancements have been achieved in the field of Natural Language Processing (NLP), where modern models are now capable of processing and analyzing significantly larger volumes of data than before. This has led to the widespread use of language models not only in traditional machine learning areas but also in adjacent fields such as medicine. This study aims to explore and compare different models for processing medical texts, most often patient histories, and to interpret the resulting data.

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