«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.

Relevance:

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.

Expected results

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.

Results achieved

Tasks 1.1, 1.2, 1.3, 1.4 of the project was completed.

Structure of the system

Publications

  1. 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.
  2. «Reflection of the COVID-19 pandemic in mass media» 13th International Conference on Intelligent Decision Technologies. Accepted for publication. Yakunin K.O. 1,2, Murzakhmetov S.B. 1, Musabayev R.R. 1, Mukhamediyev R.I.1,2 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 ➔ Kirill Yakunin Lead software engineer Scopus ➔  ORCID ➔ Publications ➔ Yan Kuchin Senior research scientist Scopus ➔  ORCID ➔ Publications ➔ Elena Mukhamedyeva Research scientist Scopus ➔ ORCID ➔ Publications ➔  
  Renat Mustakayev Software engineer Scopus ORCID Publications Marina Yelis Junior research scientist Scopus ➔ ORCID ➔ Publications ➔   Adilkhan Symagulov Engineer Scopus ➔ ORCID ➔ Publications ➔