medinfoАктуальные проблемы теоретической и клинической медициныActual Problems of Theoretical and Clinical Medicine2790-12892790-1297Казахстанско-Российский медицинский университет10.64854/2790-1289-2026-52-2-09medinfo-1083Research ArticleОБЗОР ЛИТЕРАТУРЫREVEWSМОДЕЛИ И МЕТОДЫ ПРОГНОЗИРОВАНИЯ РЕСУРСОВ ЗДРАВООХРАНЕНИЯ В ГОРОДСКИХ АГЛОМЕРАЦИЯХ: ЛИТЕРАТУРНЫЙ ОБЗОРMODELS AND METHODS FOR HEALTHCARE RESOURCE FORECASTING IN URBAN AGGLOMERATIONS: A LITERARY REVIEWhttps://orcid.org/0009-0001-7018-4154АгмановаЖ. Б.AgmanovaZh. B.

Докторант 2-курса образовательной программы: "Общественное здравоохранение"

2nd-year PhD student of the Educational Program «Public Health»,  Asfendiyarov Kazakh National Medical University, Almaty, Republic of Kazakhstan

jan-janetta@mail.ruhttps://orcid.org/0000-0002-5562-1969КалматаеваЖ. А.KalmatayevaZh. A.

Vice-Rector for Academic Affairs, Doctor of Medical Sciences, Professor, Asfendiyarov Kazakh National Medical University, Almaty, Republic of Kazakhstan

kalmatayeva.z@kaznmu.kzhttps://orcid.org/0009-0000-2063-8114ТогузбаеваК. К.ToguzbayevaK. K.

Professor of the Department of Public Health, Doctor of Medical Sciences, Professor, Asfendiyarov Kazakh National Medical University, Almaty, Republic of Kazakhstan

toguzbaeva.k@kaznmu.kzhttps://orcid.org/0000-0003-4861-0601ИскаковаГ. Д.IskakovaG. D.

Head of the Almaty Regional Branch of the Salidat Kairbekova National Research Center for Health Development, Ministry of Healthcare of the Republic of Kazakhstan, Almaty, Republic of Kazakhstan

omo_gkb7@mail.ruhttps://orcid.org/0009-0003-7169-1750СултангазиеваС. Е.SultangaziyevaS. E.

Vice-Rector for Clinical Affairs, Candidate of Medical Sciences, Asfendiyarov Kazakh National Medical University, Almaty, Republic of Kazakhstan

Sultangaziyeva.s@kaznmu.kzКазахский Национальный Медицинский университет имени С.Д. АсфендияроваКазахстанAsfendiyarov Kazakh National Medical University, Almaty, Republic of KazakhstanKazakhstanAsfendiyarov Kazakh National Medical University, Almaty, Republic of KazakhstanKazakhstanAlmaty Regional Branch of the Salidat Kairbekova National Research Center for Health Development, Ministry of Healthcare of the Republic of Kazakhstan, Almaty, Republic of KazakhstanKazakhstan202603072026021083Copyright © Агманова Ж., Калматаева Ж., Тогузбаева К., Искакова Г., Султангазиева С., 20262026Агманова Ж., Калматаева Ж., Тогузбаева К., Искакова Г., Султангазиева С.Agmanova Z., Kalmatayeva Z., Toguzbayeva K., Iskakova G., Sultangaziyeva S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://kazrosmedjournal.krmu.edu.kz/jour/article/view/1083Актуальность

Актуальность. Рост населения в городских агломерациях создаёт количественно измеримый разрыв между спросом на медицинскую помощь и обеспеченностью кадровыми, лекарственными, финансовыми и материально-техническими ресурсами. Алматинская агломерация концентрирует около 3,5 миллиона жителей с прогнозируемым ростом до 4,5 миллиона человек к 2030 году. По данным Министерства здравоохранения Республики Казахстан, в 2023 году дефицит врачей составил 4 864 ставки, а коэффициент совместительства достиг 1,4, что отражает системную перегрузку кадров.

Цель

Цель. Анализ современных моделей прогнозирования потребности в ресурсах здравоохранения в городских агломерациях с фокусом на инструментах машинного обучения, их оценки применимости к условиям Алматинской агломерации.

Материал и методы

Материал и методы: Поиск литературы был проведен в базах данных PubMed, Scopus, Web of Science Core Collection, WHO IRIS за период с января 2010 года по март 2026 года. В обзор включались оригинальные исследования, систематические обзоры и методологические отчёты ВОЗ/ОЭСР, содержащие количественные прогностические модели с оценкой точности (MAE, RMSE, MAPE, AUROC, R²) на уровне городских агломераций или регионов с уровнем урбанизации ≥ 50 %. Из 1 286 идентифицированных записей в обзор включены 57 публикаций.

Результаты

Результаты. Статистические модели были наиболее распространены (40,4 %), затем методы машинного обучения с учителем (34,0 %), гибридные модели (17,0 %), методы машинного обучения без учителя - лишь в 4 публикациях (8,5 %). Только 3 модели (6,4 %) валидированы на данных Казахстана и охватывают исключительно категорию кадровых ресурсов. Ни одна модель не охватывает одновременно все четыре категории ресурсов.

Выводы

Выводы. Методологические данные обзора обосновывают разработку интегрированной модели прогнозирования на основе машинного обучения без учителя для Алматинской агломерации с охватом не менее трёх категорий ресурсов и горизонтом прогноза 5-10 лет в соответствии с национальными плановыми документами.

Relevance

Relevance. Population growth in urban agglomerations creates a quantifiable gap between the demand for medical care and the availability of personnel, medicines, financial resources, and logistical support. The Almaty agglomeration is home to approximately 3.5 million residents, with a projected population of 4.5 million by 2030. According to the Ministry of Health of the Republic of Kazakhstan, in 2023, the shortage of physicians amounted to 4,864 positions, and the concurrent employment rate reached 1.4, reflecting systemic staff overload. 

Objective

Objective. To analyze current models for forecasting healthcare resource needs in urban agglomerations, with a focus on machine learning tools, and to assess their applicability to the conditions of the Almaty agglomeration.

Materials and methods

Materials and methods: A literature search was conducted in the PubMed, Scopus, Web of Science Core Collection, and WHO IRIS databases for the period from January, 2010 to March, 2026. The review included original studies, systematic reviews, and WHO/OECD methodological reports containing quantitative predictive models with accuracy metrics (MAE, RMSE, MAPE, AUROC, R²) at the level of urban agglomerations or regions with an urbanization rate ≥ 50 %. Of the 1,286 records identified, 57 publications were included in the review. 

Results

Results. Statistical models were the most common (40.4 %), followed by supervised machine learning methods (34.0 %), hybrid models (17.0 %), and unsupervised machine learning methods (4 publications, or 8.5 %). Only 3 validated models (6.4 %) used data from Kazakhstan and cover the human resources category exclusively. No model covered all four resource categories simultaneously. 

Conclusions

Conclusions. The methodological findings of this review justify the development of an integrated forecasting model based on unsupervised machine learning for the Almaty metropolitan area, covering at least three resource categories and a 5-10-year forecast horizon in accordance with national planning documents.

ресурсы здравоохраненияпрогностическая модельмашинное обучениеобучение без учителягородская агломерациясистематический обзорКазахстанмедицинские кадры.healthcare resourcesforecasting modelmachine learningunsupervised learningurban agglomerationsystematic reviewKazakhstanhealth workforce-ReferencesUnited Nations, Department of Economic and Social Affairs, Population Division. (2019). World urbanization prospects: The 2018 revision (ST/ESA/SER.A/420). United Nations. Retrieved May 15, 2026, from https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf.United Nations, Department of Economic and Social Affairs, Population Division. 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The authors declare that there are no conflicts of interest present.