«Developing methods and algorithms of intelligent GIS for multi-criteria analysis of healthcare data» (IRN AP09259587)
The aim of the project:
Development of models, algorithms, and methods, an intelligent geoinformation system for multi-criteria decision support in health care based on the models of explainable machine learning, NLP, GIS using social, medical, and economic information.
The success of artificial intelligence in health care covers three major areas: diagnostics and treatment support, clinical decision support systems, and public health. The project aims to overcome the shortcomings of modern medical information systems and GIS in the field of public health by creating an intelligent geoinformation system for multi-criteria decision support based on the models of explainable machine learning, NLP, GIS. Developed methods will make it possible to produce recommendations for improving the work of healthcare organizations using medical, economic, and social information.
The project will result in
- Published at least 3 (three) articles and/or reviews in peer-reviewed scientific journals indexed in the Science Citation Index Expanded of the Web of Science database and/or having a CiteScore percentile in the Scopus database at least 35 (thirty-five);
- Published as well as at least 1 (one) article or review in a peer-reviewed foreign or domestic publication recommended by CCSES;);
- Certificates of intellectual property registration for software will be obtained;
- The scientific and technical documentation for the software will be developed;
- Certificates of intellectual property registration for software will be obtained.
Scientific results can be applied or commercialized as part of the end product in health management tasks. The scientific and social effects can be multiplicative, as technologies developed can be applied not only in healthcare but also in other large economic complexes.
Tasks 1.1, 1.2, 1.3, 1.4 of the project was completed.
- Kirill Yakunin, Ravil I. Mukhamediev, Elena Zaitseva , Vitaly Levashenko, Marina Yelis, Adilkhan Symagulov 1,2, Yan Kuchin, Elena Muhamedijeva, Margulan Aubakirov and Viktors Gopejenko. Mass Media as a Mirror of the COVID-19 Pandemic //Computation. – 2021. – Т. 9. – №. 12. – С. 140. https://doi.org/10.3390/computation9120140 (CiteScore highest quartile = Q2, CiteScore =2.9, CiteScore highest percentile=69%)
AbstractThe media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English. View Full-Text
- Сымагулов А. и др. МЕТОДЫ ИНТЕРПРЕТАЦИИ ЧЕРНЫХ ЯЩИКОВ МАШИННОГО ОБУЧЕНИЯ И ИХ ПРИМЕНЕНИЕ ДЛЯ СОЗДАНИЯ СИСТЕМ ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ //Известия НАН РК. Серия физико-математических наук. – 2021. – №. 5. – С. 91-99. http://22.214.171.124/physics-mathematics/article/view/2576
- K. O. Yakunin, S. B. Murzakhmetov, R. R. Musabayev and R. I. Mukhamediyev, “News Popularity Prediction Using Topic Modelling,” 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), 2021, pp. 1-4, doi: 10.1109/SIST50301.2021.9465884.
- Yakunin K. et al. Reflection of the COVID-19 pandemic in mass media //2021 International Conference on Information and Digital Technologies (IDT). – IEEE, 2021. – С. 260-263. https://ieeexplore.ieee.org/abstract/document/9497572
- M Yelis, Y Kuchin, A Symagulov, E Muhamedieva Explainable machine learning for healthcare decision-making tasks //The 19th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2021 April 22-23, 2021, ISMA University of Applied Science, Riga.- c. 56-58. https://www.ismaitm.lv/images/Files/Theses/2021/01_NC/23_ITM2021_Yelis_Kuchin_Symagulov_Muhamedieva.pdf
- «Reflection of the COVID-19 pandemic in mass media» 13th International Conference on Intelligent Decision Technologies. Accepted for publication. Yakunin K.O. Murzakhmetov S.B. , Musabayev R.R. , Mukhamediyev R.I. 2021 IEEE International Conference on Smart Information Systems and Technologies (2021 IEEE SIST)
Working group of the project
Ravil Mukhamedyev Principal invistegator Scopus ➔ ORCID ➔ Publications ➔
mukhamediev.ravil <Ω> gmail.com
|Kirill Yakunin Lead software engineer Scopus ➔ ORCID ➔ Publications ➔||Yan Kuchin Senior research scientist Scopus ➔ ORCID ➔ Publications ➔||Elena Mukhamedyeva Research scientist Scopus ➔ ORCID ➔ Publications ➔|
|Marina Yelis Junior research scientist Scopus ➔ ORCID ➔ Publications ➔||Adilkhan Symagulov Engineer Scopus ➔ ORCID ➔ Publications ➔|