V.V. Zayats1, V.V. Danshin2, R.V. Kolesnikov3

1,3 Universal design and assistive technology resource center,
Ministry of Industry and Trade of the Russian Federation (Moscow, Russia)

2 National Research Nuclear University MEPHI of Moscow Engineering Physics Institute (Moscow, Russia)

The direction of accurate diagnostics based on the analysis of medical data, predicting the dynamics of patients' diseases on the basis of laboratory tests is an urgent task of clinical medicine. Various deterministic and probabilistic methods and algorithms are used in this process. Among the probabilistic approaches, tools based on artificial intelligence (AI) technologies, as well as combinatorial methods, occupy an important place. Medical AI tools have advantages such as speed of processing "big" data and speed of obtaining results, but they also have disadvantages, which include weak interpretability of results and system error up to 10%. An alternative approach devoid of these disadvantages could be the combined use of time-series prediction and combinatorics methods for disease diagnosis based on patient analyses. Therefore, the issue of substantiating the primary technical requirements for the service of collecting and processing data on the dynamics of patient diseases is important.

The purpose of this work is to substantiate the primary minimum technical requirements for a set of tools for disease prediction based on time series analysis and the use of classical and modern combinatorial methods. The formed set of requirements can be used for the development and approval of the terms of reference for R&D on the topic of development, creation and implementation of a promising domestic complex for predicting the course of individual diseases on the basis of blood tests, computer and magnetic resonance imaging.

Zayats V.V., Danshin V.V., Kolesnikov R.V. The use of combinatorial methods and time series analysis to predict the dynamics of diseases based on data from clinical analyzes. Biomedicine Radioengineering. 2023. V. 26. № 1. Р. 18-26. DOI: https://doi.org/10.18127/ j15604136-202301-02 (In Russian).

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