AP26194209 «Mathematical and algorithmic support for an intelligent clinical decision support system in haematology and diabetology based on blood gasometry technology»

Name of the project: AP26194209 «Mathematical and algorithmic support for an intelligent clinical decision support system in haematology and diabetology based on blood gasometry technology»

Competition name: Competition for grant financing of scientific and (or) scientific and technical projects for 2025-2027.

Project Manager: Rakhmetullina Zhenisgul Tuleukhanovna, candidate of physical and Mathematical Sciences, Associate Professor

 Identifiers:

• ResearcherID Web of Science: GYG-2630-2022
• ORCID: https://orcid.org/0000-0002-0554-7684
• Scopus Author ID: 57220809777

Project Research Team

Project abstract:

The development of intelligent clinical decision support systems based on artificial intelligence, machine learning, and mathematical modeling technologies can significantly improve the quality of medical care. Creation of algorithmic and mathematical software capable of: - integrating gasometry data with other laboratory and clinical indicators, to identify hidden patterns and relationships between parameters, predict the development of pathological conditions. Offering optimal clinical solutions is an urgent scientific task in the field of medical informatics and biomedical engineering.

The aim of the projectis to developscientific and methodologicalfoundations for mathematicalandalgorithmicsupport of an intelligentclinicaldecisionsupportsystembased on bloodgasometrytechnology, which willimprove the effectiveness of medicalrecommendationsinhematologyanddiabetology.

To achieve the goal, the following logically interrelated tasks are planned:

1. research and analysis of theoretical and practical aspects of clinical decision support processes based on blood gasometry technology;
2. designing a conceptual model of a clinical decision support system in hematology and diabetology based on blood gasometry technology;
3. development of mathematical models and algorithms to support clinical decisions in hematology and diabetology based on blood gasometry technology and machine learning methods;
4. development of a hybrid model for analyzing blood gasometry indicators based on the technology of ensemble machine learning methods to improve the accuracy of diagnosis and the effectiveness of medical solutions in hematology and diabetology;
5. software implementation of modules of the intelligent clinical decision support system for the tasks of hematology and diabetology;
6. experimental study of an intelligent clinical decision support system for hematology and diabetology and development of necessary technical documentation.

Expected and achieved results of the project: