kpcczКомплексные проблемы сердечно-сосудистых заболеванийComplex Issues of Cardiovascular Diseases2306-12782587-9537Federal State Budgetary Institution “Research Institute for Complex Issues of Cardiovascular Diseases”10.17802/2306-1278-2023-12-3-109-125kpccz-1351Research ArticleАНАЛИТИЧЕСКИЙ ОБЗОР. Организация здравоохранения и общественное здоровьеВОЗМОЖНОСТИ ПРИМЕНЕНИЯ ТЕХНОЛОГИЙ МАШИННОГО ОБУЧЕНИЯ В СФЕРЕ ПЕРВИЧНОЙ ПРОФИЛАКТИКИ СЕРДЕЧНО-СОСУДИСТЫХ ЗАБОЛЕВАНИЙPOSSIBILITIES OF APPLYING MACHINE LEARNING TECHNOLOGIES IN THE SPHERE OF PRIMARY PREVENTION OF CARDIOVASCULAR DISEASEShttps://orcid.org/0000-0002-0211-4525КавешниковВладимир СергеевичKaveshnikovVladimir S.

кандидат медицинских наук ведущий научный сотрудник лаборатории регистров сердечно-сосудистых заболеваний, высокотехнологичных вмешательств и телемедицины Научно-исследовательского института кардиологии – филиала Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук», Томск, Российская Федерация

PhD, Leading Researcher, Laboratory of Cardiovascular Diseases Registries, High-Tech Interventions and Telemedicine, Research Institute of Cardiology - Branch of the Federal State Budgetary Scientific Institution "Tomsk National Research Medical Center of the Russian Academy of Sciences", Tomsk, Russian Federation

kave@ngs.ruhttps://orcid.org/0000-0002-0875-3301БрагинДмитрий СергеевичBraginDmitry S.

младший научный сотрудник лаборатории регистров сердечно-сосудистых заболеваний, высокотехнологичных вмешательств и телемедицины Научно-исследовательского института кардиологии – филиала Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук», Томск, Российская Федерация

Junior Researcher, Laboratory of Cardiovascular Diseases Registries, High-Tech Interventions and Telemedicine, Research Institute of Cardiology - Branch of the Federal State Budgetary Scientific Institution "Tomsk National Research Medical Center of the Russian Academy of Sciences", Tomsk, Russian Federation

braginds@mail.ruhttps://orcid.org/0000-0003-0004-7717ВаизовВалерий ХарисовичVaizovValery Kh.

кандидат медицинских наук младший научный сотрудник лаборатории регистров сердечно-сосудистых заболеваний, высокотехнологичных вмешательств и телемедицины Научно-исследовательского института кардиологии – филиала Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук», Томск, Российская Федерация

PhD, Junior Researcher, Laboratory of Cardiovascular Diseases Registries, High-Tech Interventions and Telemedicine, Research Institute of Cardiology - Branch of the Federal State Budgetary Scientific Institution "Tomsk National Research Medical Center of the Russian Academy of Sciences", Tomsk, Russian Federation

vaizov@mail.ruhttps://orcid.org/0000-0002-4743-1989КавешниковАртём ВладимировичKaveshnikovArtyom V.

младший научный сотрудник лаборатории регистров сердечно-сосудистых заболеваний, высокотехнологичных вмешательств и телемедицины Научно-исследовательского института кардиологии – филиала Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук», Томск, Российская Федерация

Junior Researcher, Laboratory of Cardiovascular Diseases Registries, High-Tech Interventions and Telemedicine, Research Institute of Cardiology - Branch of the Federal State Budgetary Scientific Institution "Tomsk National Research Medical Center of the Russian Academy of Sciences", Tomsk, Russian Federation

artemkave@mail.ruhttps://orcid.org/0000-0002-5587-3947КузьмичкинаМария АнатольевнаKuzmichkinaMaria A.

кандидат медицинских наук научный сотрудник лаборатории регистров сердечно-сосудистых заболеваний, высокотехнологичных вмешательств и телемедицины Научно-исследовательского института кардиологии – филиала Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук», Томск, Российская Федерация

PhD, Research Associate, Laboratory of Cardiovascular Diseases Registries, High-Tech Interventions and Telemedicine, Research Institute of Cardiology - Branch of Federal State Budgetary Scientific Institution "Tomsk National Research Medical Center of the Russian Academy of Sciences", Tomsk, Russian Federation

kuzmariakuz@gmail.comhttps://orcid.org/0000-0003-1063-7382ТрубачеваИрина АнатольевнаTrubachevaIrina A.

доктор медицинских наук заместитель директора по научно-организационной работе, руководитель отдела популяционной кардиологии Научно-исследовательского института кардиологии – филиал Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук», Томск, Российская Федерация

PhD, Deputy Director for Scientific and Organizational Work, Head of Population Cardiology Department, Research Institute of Cardiology - Branch of the Federal State Budgetary Scientific Institution "Tomsk National Research Medical Center of the Russian Academy of Sciences", Tomsk, Russian Federation

tia@cardio-tomsk.ruНаучно-исследовательский институт кардиологии – филиал Федерального государственного бюджетного научного учреждения «Томский национальный исследовательский медицинский центр Российской академии наук»РоссияFederal State Budgetary Scientific Institution “Tomsk National Research Medical Center of the Russian Academy of Sciences”Russian Federation202325092023123109125Copyright © Кавешников В.С., Брагин Д.С., Ваизов В.Х., Кавешников А.В., Кузьмичкина М.А., Трубачева И.А., 20232023Кавешников В.С., Брагин Д.С., Ваизов В.Х., Кавешников А.В., Кузьмичкина М.А., Трубачева И.А.Kaveshnikov V.S., Bragin D.S., Vaizov V.K., Kaveshnikov A.V., Kuzmichkina M.A., Trubacheva I.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.nii-kpssz.com/jour/article/view/1351Основные положения

Основные положения

В обзоре проанализированы исследования, посвященные возможности использования методов машинного обучения для прогнозирования возникновения фибрилляции предсердий, кардиоваскулярных факторов риска, каротидного атеросклероза, суммарного сердечно-сосудистого риска. Значительные перспективы имеет сочетание методов машинного обучения с мобильными, облачными и телемедицинскими технологиями. В ближайшем будущем ожидается использование таких технологий для скрининга фибрилляции предсердий, а также стратификации риска с использованием данных кардиовизуализации. На основе методов машинного обучения развиваются мобильные профилактические технологии, направленные в частности на управление пищевым поведением.

 

 

Резюме

Резюме

В статье рассмотрены основные направления применения технологий машинного обучения (МО) в сфере первичной профилактики сердечно-сосудистых заболеваний (ССЗ), показаны примеры решения с их помощью научных и практических задач. В настоящее время изучается возможность использования МО для прогнозирования суммарного сердечно-сосудистого риска, риска возникновения фибрилляции предсердий, факторов риска (ФР) ССЗ, каротидного атеросклероза и др. Кроме традиционных ФР в моделях МО применяются данные опросников, врачебного осмотра, лабораторных показателей, электрокардиографии, кардиовизуализации, сведения о принимаемом лечении, геномные и протеомные признаки. Обращает внимание разнообразие методов, применяемых при МО. Наиболее часто прибегают к таким классификаторам, как Random Forest, метод опорных векторов, искусственные нейронные сети. Многие алгоритмы МО демонстрируют прирост точности прогноза по отношению к действующим шкалам риска, но на текущий момент ни одна из методик однозначно не признана. На ранних стадиях развития находятся технологии глубокого МО. Мобильные, облачные и телемедицинские технологи открывают новые возможности для сбора, хранения и полезного применения медицинских данных и могут вывести профилактику ССЗ на новый уровень. В ближайшем будущем ожидается использование таких технологий для скрининга фибрилляции предсердий, а также стратификации сердечно-сосудистого риска с использованием данных кардиовизуализации, добавление которых к традиционным ФР позволяет получить наиболее стабильные оценки риска. Есть примеры использования мобильных технологий МО для управления ФР, в частности пищевым поведением. Авторы обращают внимание на такие аспекты, как недопустимость переоценки роли искусственного интеллекта в технологиях здравоохранения, предвзятость алгоритмов, кибербезопасность, этические вопросы сбора и использования медицинских данных. Практическая применимость моделей МО и их влияние на конечные точки на текущий момент изучены недостаточно. Значительным препятствием к внедрению технологий МО в сфере здравоохранения являются недостаточный опыт и отсутствие законодательной базы.

Highlights

Highlights

The review analyzes the studies devoted to the possibility of using machine learning methods to predict the occurrence of atrial fibrillation, cardiovascular risk factors, carotid atherosclerosis, and total cardiovascular risk. The combinations of machine learning methods with mobile, cloud and telemedicine technologies have significant prospects. In the near future, such technologies are expected to be used for atrial fibrillation screening and risk stratification using cardiac imaging data. Based on machine learning methods, mobile preventive technologies are being developed, particularly for nutritional behavior management.

 

 

Abstract

Abstract

The article reviews the main directions of machine learning (ML) application in the primary prevention of cardiovascular diseases (CVD) and highlights examples of scientific and practical problems solved with its help. Currently, the possibility of using ML to predict cardiovascular risk, occurrence of atrial fibrillation (AF), cardiovascular risk factors, carotid atherosclerosis, etc. has been studied. The data of questionnaires, medical examination, laboratory indices, electrocardiography, cardio visualization, medications, genomics and proteomics are used in ML models. The most common classifiers are Random Forest, Support Vector, Neural Networks. As compared to traditional risk calculators many ML algorithms show improvement in prediction accuracy, but no evident leader has been defined yet. Deep ML technologies are at the very early stages of development. Mobile, cloud and telemedicine technologies open new possibilities for collection, storage and the use of medical data and can improve CVD prevention. In the near future, such technologies are expected to be used for atrial fibrillation screening as well as cardiovascular risk stratification using cardiac imaging data. Moreover, the addition of them to traditional risk factors provides the most stable risk estimates. There are examples of mobile ML technologies use to manage risk factors, particularly eating behavior. Attention is paid to such problems, as need to avoid overestimating the role of artificial intelligence in healthcare, algorithms’ bias, cybersecurity, ethical issues of medical data collection and use. Practical applicability of ML models and their impact on endpoints are currently understudied. A significant obstacle to implementation of ML technologies in healthcare is the lack of experience and regulation.

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The authors declare that there are no conflicts of interest present.