DOI: https://doi.org/10.22263/2312-4156.2026.2.93
A.I. Razuvanau, I.T. Doroshenko, E.A. Mikhedenko
Typology of persons undergoing medical and social assessments based on the analysis of their written appeals
National Science and Practice Centre of Medical Assessment and Rehabilitation, village Yukhnovka, Minsk region, Republic of Belarus
Vestnik VGMU. 2026;25(2):93-103.
Abstract.
Objectives. To create groups of patients based on the analysis of written requests from citizens.
Material and Methods. To analyze the main reasons for written requests (from 2018 to 2024 (excluding 2020 and 2021)) from citizens aged 18 to 64, a patient record was developed. Patient recruitment was performed using a continuous sampling method. The following methods were used: descriptive statistics; nonparametric statistical tests; the Python programming language in the Visual Studio Code development environment; data processing in the Pandas library; and statistical tests using the Pingouin library.
Results and discussion. Analysis of the cluster distribution of the study group (n=943) revealed two clearly distinct clusters:
Cluster 0 is predominantly represented by older individuals with long professional experience, a short history of disability, and, most often, retirees employed in the economy with an emotionally intense psychophysiological profile of their profession. These individuals are more likely to have limited self-care and mobility, have progressive cardiac diseases, and are more likely to refuse vocational and occupational rehabilitation.
Cluster 1, in contrast, includes younger patients with less work experience but a longer history of disability. They often lack qualifications, have an undefined occupation, are not employed in the economy, and have significant limitations in orientation, communication, behavioral control, and learning. Their clinical profile is predominantly psychiatric and sensory; adjustments to work schedules are often required, and social and vocational rehabilitation are prescribed.
Conclusions. The resulting clusters can be used as a basis for prospective group recruitment, the development of differentiated individual rehabilitation programs for individuals with disabilities, and the prediction of outcomes of medical and social assessments and rehabilitation.
Keywords: cluster analysis, disability, appeals of citizens, medical and social assessments, rehabilitation.
Funding sources. Task 3.78 “Establish a relationship between performance and working capacity indicators and limitations of patients’ life activity categories” of subprogram 11.2 “Fundamentals of Medicine” of State Program for Scientific Research 11 “Experimental Medicine and Medical Biotechnology”.
References
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2. Mckinney W. Pandas: a foundational Python library for data analysis and statistics. URL: https://www.researchgate.net/publication/265194455 [Accessed 17th March 2026].
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4. Vallat R. Pingouin: statistics in Python. Journal of Open Source Software. 2018;3(3):1026. doi: http://dx.doi.org/10.21105/joss.01026
Submitted 05.03.2026
Accepted 14.04.2026
Information about authors:
Aliaksei I. Razuvanau – Candidate of Medical Sciences, associate professor, scientific secretary of National Science and Practice Centre of Medical Assessment and Rehabilitation, https://orcid.org/0000-0001-5033-2933, e-mail: Этот адрес электронной почты защищён от спам-ботов. У вас должен быть включен JavaScript для просмотра.;
I.T. Doroshenko – Candidate of Medical Sciences, associate professor, head of the Laboratory of Medical Assessment and Rehabilitation of Children, National Science and Practice Centre of Medical Assessment and Rehabilitation, https://orcid.org/0000-0003-1223-7497
E.A. Mikhedenko – hygienist at the Department of Professional Capacity Assessment, National Science and Practice Centre of Medical Assessment and Rehabilitation.
