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 th...
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| Format: | Article |
| Language: | English |
| Published: |
Information Technology Publishing House
2025-02-01
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| Series: | Problems of Information Society |
| Online Access: | https://jpis.az/uploads/article/en/2025_1/APPLICATION_OF_THE_WORD2VEC_ALGORITHM_FOR_CLINICAL_DIAGNOSIS_DETERMINATION.pdf |
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| Summary: | 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. |
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| ISSN: | 2077-964X 2309-7566 |