Muhamadiyeva, D. T., Primova, X.A., & Nabiyeva, S.S.
Overview of early diagnosis of «diabetes» based on artificial intelligence. |
 |
Full Article: PDF
Scientific Object Identifier: http://s-o-i.org/1.1/TAS-10-102-34
DOI: https://dx.doi.org/10.15863/TAS.2021.10.102.34
Language: English
Citation: Muhamadiyeva, D. T., Primova, X.A., & Nabiyeva, S.S. (2021). Overview of early diagnosis of «diabetes» based on artificial intelligence. ISJ Theoretical & Applied Science, 10 (102), 443-446. Soi: http://s-o-i.org/1.1/TAS-10-102-34 Doi: https://dx.doi.org/10.15863/TAS.2021.10.102.34 |
Pages: 443-446
Published: 30.10.2021
Abstract: The review presents the possibilities of using artificial intelligence to study the mechanisms of development of diabetes mellitus (DM) and create new technologies for its prevention, monitoring and treatment. In recent years, a huge array of molecular data has been accumulated that reveal the pathogenetic mechanisms of the development of diabetes mellitus and its complications. Intellectual analysis of data and texts of scientific publications (data mining and text mining) opens up new possibilities for processing this information. Analysis of molecular genetic networks makes it possible to identify molecular interactions that are important for the development of diabetes mellitus and its complications, as well as to identify new targeted molecules. Based on the analysis of big data and machine learning, new platforms have been created for the prognosis and screening of diabetes mellitus, diabetic retinopathy, chronic kidney disease, and cardiovascular complications. Machine learning algorithms are used for personalized glucose prediction, closed-loop insulin delivery systems, and decision support systems for lifestyle modification and diabetes treatment.
Key words: diabetes mellitus, artificial intelligence, machine learning, data mining, text mining, gene networks, decision support systems.
|
