APPLICATION OF THE WORD2VEC ALGORITHM FOR CLINICAL DIAGNOSIS DETERMINATION

The article examines the use of Natural Language Processing technologies in modern medicine for knowledge acquisition and the implementation of decisionmaking methods. The application of information technology in healthcare has become one of the main requirements of the modern era. Enhancing the quality and accessibility of medical services necessitates the utilization of modern technologies, mathematical methods, and the capabilities of artificial intelligence, alongside the development of comprehensive information systems. The paper proposes methods for analyzing and applying tools to assist physicians in diagnosing conditions and determining treatment plans with the help of artificial intelligence. It also focuses on evaluating the quality of diagnostic and treatment processes through applıcation of different methods and practical application. The analysis of large volumes of medical data using Natural Language Processing technology enables the extraction of valuable insights. A significant portion of medical data is stored and exchanged in text form compliant with the Health Level Seven standard, making semantic similarity methods that operate on textual data highly effective in this domain. By designing and implementing rules for applying and integrating different algorithms, it is possible to transform medical data into valuable knowledge, contributing significantly to advancements in the medical field. The article presents a Word2Vec algorithm-based approach for detecting diagnoses of cardiovascular diseases from collected patient histories, as well as refining existing diagnoses. The development of an algorithm capable of assigning new diagnoses based on historical patient records constitutes one of the key outcomes of this research.