All articles published in electronic form in the "Online First" section have passed a full-format review, selection, and editorial processing procedure, and after the corresponding issue is formed, they are published in the printed and electronic versions of the journal "Cardiovascular Therapy and Prevention". The date of publication of the article should be considered the publication of its electronic version in the "Online First" section. Thus, the version of the article published in the "Online First" section should be considered the final version of the article and it can be referred to as a published article.
An article published in the "Online First" section should be cited using the unique article number doi before the publication of the printed version of the article.
Профессиональное образование и аккредитация
The exponential growth of biomedical data, combined with advancements in machine learning (ML), has created opportunities for more precise diagnostics, enhanced treatment planning, and improved patient management. However, the successful implementation of ML in clinical settings depends on healthcare professionals’ understanding and competency in these technologies. This study examines the effectiveness of integrating ML methodologies into the curricula of Astana Medical University and S. D. Asfendiyarov Kazakh National Medical University. Focusing on childhood allergic conditions such as asthma, rhinitis, and skin diseases, a supervised ML approach (linear regression) was employed to analyze both clinical and educational data. Results demonstrated that the experimental group of students, who received ML-integrated training, exhibited significant improvements in analytical proficiency and data processing accuracy compared to a control group. The ML model achieved a coefficient of determination (R²) of 0.85, with low prediction errors (MAE = 0.45, MSE = 0.30, RMSE = 0.55). Statistical tests supported the hypothesis that structured ML education enhances medical students’ competencies, suggesting that future healthcare professionals trained in ML can better leverage data-driven decision-making for improved patient care. This study contributes to the growing body of literature advocating for ML integration in medical education and underscores the need for further research into advanced ML algorithms and long-term clinical outcomes.
INFORMATION FOR AUTHORS
ISSN 2619-0125 (Online)