Input data for training the neuromodel of the decision support system for optimising clinical-expert technologies: sample formation

Authors:

Poliakov Dmitrii Aleksandrovich – PhD in Medical sciences (Cand. Med. Sci.), Head of the Department of Medical and social expertise and medical rehabilitation of disabled persons of the Institute for Rehabilitation and Abilitation of Disabled Persons of the Albrecht Federal Scientific and Scientific and Educational Center of Medical and Social Expertise and Rehabilitation, 50 Bestuzhevskaya Street, 195067 St. Petersburg, Russian Federation; e-mail: poliakov_dm@mail.ru; https://orcid.org/0009-0000-0251-5356.

Ishutina Inna Sergeevna – PhD in Medical sciences (Cand. Med. Sci.), Director of the Institute for Rehabilitation and Abilitation of Disabled Persons of the Albrecht Federal Scientific and Scientific and Educational Center of Medical and Social Expertise and Rehabilitation, 50 Bestuzhevskaya Street, 195067 St. Petersburg, Russian Federation; e-mail: in.ishutina@yandex.ru; https://orcid.org/0000-0002-6640-904X.

Khodakovskii Matvei Dmitrievich – junior researcher at the Department of Medical and social expertise and medical rehabilitation of disabled persons of the Institute for Rehabilitation and Abilitation of Disabled Persons of the Albrecht Federal Scientific and Scientific and Educational Center of Medical and Social Expertise and Rehabilitation, 50 Bestuzhevskaya Street, 195067 St. Petersburg, Russian Federation; e-mail: hodak200@yandex.ru; https://orcid.org/0000-0001-6953-5191.

Kiseleva Olga Nikolaevna – researcher at the Department of Medical and social expertise and medical rehabilitation of disabled persons of the Institute for Rehabilitation and Abilitation of Disabled Persons of the AlbrechtFederalScientificandScientificandEducationalCenterofMedicalandSocialExpertiseandRehabilitation, 50 Bestuzhevskaya Street, 195067 St. Petersburg, Russian Federation; e-mail: kiselevaon@gmail.com; https://orcid.org/0009-0000-3059-4477.

Kulinich Tatiana Sergeevna – Head – Expert of Medical and Social Expertise, General Office of Medical and Social Expertise for Leningrad Region, 43 Prosveshchenia Ave, 194291 St. Petersburg, Russian Federation; e-mail: mail@47.gbmse.ru.

Annotation:

Introduction. The relevance of the topic is due to the need to adapt to rapidly changing conditions, where digital innovations are becoming the basis for improving efficiency, competitiveness and sustainable development, which is especially important in expert practices. The use of neural models in medical and social expertise is a promising area. The development of information technologies will improve the quality of doctors’ work on medical and social expertise.

Aim. The aim of the study is to identify approaches to sampling for neuromodel training to optimize clinical and expert technologies based on a single nosological unit (consequences of acute cerebrovascular accident.

Materials and methods. The study was based on the principles of continuous sampling: 3,130 protocols for conducting medical and social examinations of citizens with the consequences of acute cerebral circulatory disorders who were examined by the Main Bureau of Medical and Social Expertise for St. Petersburg and the Leningrad Region in 2021 were selected to form the sample. The average age of the examined persons was 61.87±10.3 years, ranging from 25 to 98 years. To solve the tasks set, an analysis of the documentation was carried out, including anonymized inspection protocols. Methods of descriptive statistics and correlation analysis were also used.

Results. For reasons of disability, the “general disease” prevails, which is found in 3002 (95.91 %) disabled people. The most common disability in both men and women with an established disability was a limitation of the ability to work, and the least common was a limitation of the ability to learn. The structure of neurological consequences of acute cerebrovascular accident in the study sample was as follows: paresis and/or paralysis (75.08 %), mental disorders (64.66 %) and ataxia of varying severity (49.84 %).

Discussion. The frequency of occurrence of the consequences of acute cerebrovascular accident in the study sample was analyzed by disability groups. According to the results of the correlation analysis, a moderate direct correlation was found between the disability group and the consequences of STROKE in the form of paresis and/or paralysis (Spearman’s Rh 0.567 at p <0.01) and urinary incontinence (Spearman’s Rh 0.566 at p <0.01).

Summary. For effective training of the neural model, it is necessary to carry out qualitative selection and preparation of the initial dataset, in the structure of which it is proposed to identify groups of features: socio-demographic, limitations of the main categories of vital activity and their degree, the clinical and functional status of the patient.

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