References

russjcardiol

Российский кардиологический журнал

Russian Journal of Cardiology

1560-40712618-7620

«SILICEA-POLIGRAF» LLC

10.15829/1560-4071-2021-4618

russjcardiol-4618

Research Article

ОБЗОРЫ ЛИТЕРАТУРЫ

REVIEW

Совершенствование возможностей оценки сердечно-сосудистого риска при помощи методов машинного обучения

Improvement of cardiovascular risk assessment using machine learning methods

https://orcid.org/0000-0002-7380-8460

Гусев

А. В.

Gusev

A. V.

Гусев Александр Владимирович — кандидат технических наук, директор по развитию

Петрозаводск

Petrozavodsk

agusev@webiomed.ai

https://orcid.org/0000-0002-8745-857X

Гаврилов

Д. В.

Gavrilov

D. V.

Гаврилов Денис Владимирович — эксперт по медицине

Петрозаводск

Petrozavodsk

dgavrilov@webiomed.ai

https://orcid.org/0000-0002-2350-977X

Новицкий

Р. Э.

Novitsky

R. E.

Новицкий Роман Эдвардович — генеральный директор

Петрозаводск

Petrozavodsk

roman@webiomed.ai

https://orcid.org/0000-0002-6654-1382

Кузнецова

Т. Ю.

Kuznetsova

T. Yu.

Кузнецова Татьяна Юрьевна — доктор медицинских наук, доцент, заведующий кафедрой факультетской терапии, фтизиатрии, инфекционных болезней и эпидемиологии

Петрозаводск

Petrozavodsk

eme@sampo.ru

https://orcid.org/0000-0001-6998-8406

Бойцов

С. А.

Boytsov

S. A.

Бойцов Сергей Анатольевич — доктор медицинских наук, профессор, академик РАН, генеральный директор

Москва

Moscow

prof-boytsov@mail.ru

К-СкайРоссияK-SkaiRussian Federation

ФГБОУ ВО ПетрГУРоссияPetrozavodsk State UniversityRussian Federation

ФГБУ НМИЦ Кардиологии Минздрава РоссииРоссияNational Medical Research Center of CardiologyRussian Federation

2021

25102021

261246184618

2022

Гусев А.В., Гаврилов Д.В., Новицкий Р.Э., Кузнецова Т.Ю., Бойцов С.А.

Gusev A.V., Gavrilov D.V., Novitsky R.E., Kuznetsova T.Y., Boytsov S.A.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://russjcardiol.elpub.ru/jour/article/view/4618

Рост распространенности сердечно-сосудистых заболеваний (ССЗ) определяет важность их прогноза, необходимость точной стратификации рисков, профилактических и лечебных воздействий. Большие базы медицинских данных и технологии их обработки в виде алгоритмов машинного обучения, появившиеся в последние годы, потенциально позволяют улучшить предсказательную точность и персонализировать терапевтические подходы к ССЗ. В обзоре исследуется применение машинного обучения в предсказании и определении клинических событий кардиологического профиля. Обсуждается роль данной технологии как в расчете общего сердечно-сосудистого риска, так и предсказании отдельных заболеваний и событий. Сравнивается предсказательная точность с принятыми шкалами расчета рисков и действие различных алгоритмов машинного обучения. Анализируются условия для применения машинного обучения и возможности разработки персонализированной тактики ведения больных с ССЗ.

The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.

сердечно-сосудистые заболеванияоценка рискапредсказание сердечно-сосудистых событиймашинное обучениеискусственный интеллект

cardiovascular diseasesrisk assessmentprediction of cardiovascular eventsmachine learningartificial intelligence

Исследование выполнено при финансовой поддержке Министерства науки и высшего образования Российской Федерации в рамках Соглашения № 075-15-2021-665

This research was financially supported by the Ministry of Science and Higher Education of the Russian Federation Theme No. 075-15- 2021-665

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