vitjВрач и информационные технологииMedical Doctor and Information Technologies1811-01932413-5208Pirogov National Medical and Surgical Center10.25881/18110193_2026_3_164vitj-415Research ArticleОРИГИНАЛЬНЫЕ ИССЛЕДОВАНИЯORIGINAL RESEARCHСитуационный алгоритм прогнозирования временных послеоперационных параметров госпитализации кардиохирургических пациентов с использованием нейронных сетейSituational algorithm for predicting postoperative hospitalization duration for cardiac surgery patients using neural networkshttps://orcid.org/0000-0003-0359-0897АндриковД. А.AndrikovD. A.

к.т.н.

г. Москва

PhD

Moscow

andrikov@bmstu.ruhttps://orcid.org/0000-0002-9415-710XАсташевП. Е.AstashevP. E.

к.м.н.

г. Москва

PhD

Moscow

astashevpe@pirogov-center.ruhttps://orcid.org/0000-0001-7149-6072БерезкинД. В.BerezkinD. V.

к.т.н., доцент

г. Москва

PhD, Associate Professor

Moscow

berezkind@bmstu.ruhttps://orcid.org/0000-0002-1540-9437ИвинаА. А.IvinaA. A.

г. Москва

Moscow

ivinaaa@pirogov-center.ruhttps://orcid.org/0000-0001-9220-5588КозловИ. А.KozlovI. A.

г. Москва

Moscow

kozlov.ia@bmstu.ruhttps://orcid.org/0000-0002-2429-1805МартынюкП. А.MartynyukP. A.

г. Москва

Moscow

martynyuk.pa@bmstu.ruhttps://orcid.org/0000-0001-8648-8294ПензинО. В.PenzinO. V.

к.м.н.

г. Москва

PhD

Moscow

penzinov@pirogov-center.ruhttps://orcid.org/0000-0001-8060-3431ПролетарскийА. В.ProletarskyA. V.

д.т.н., профессор

г. Москва

DSc, Professor

Moscow

pav@bmstu.ruhttps://orcid.org/0000-0002-3640-4897СубботинС. А.SubbotinS. A.

г. Москва

Moscow

subbotinsa@pirogov-center.ruМГТУ им. Н.Э. БауманаРоссияBauman Moscow State Technical UniversityRussian FederationФГБУ «НМХЦ им. Н.И. Пирогова» Минздрава РоссииРоссияPirogov National Medical and Surgical CenterRussian Federation20261710202603164180Copyright © Андриков Д.А., Асташев П.Е., Березкин Д.В., Ивина А.А., Козлов И.А., Мартынюк П.А., Пензин О.В., Пролетарский А.В., Субботин С.А., 20262026Андриков Д.А., Асташев П.Е., Березкин Д.В., Ивина А.А., Козлов И.А., Мартынюк П.А., Пензин О.В., Пролетарский А.В., Субботин С.А.Andrikov D.A., Astashev P.E., Berezkin D.V., Ivina A.A., Kozlov I.A., Martynyuk P.A., Penzin O.V., Proletarsky A.V., Subbotin 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://www.vit-j.ru/jour/article/view/415

В статье представлен ситуационный алгоритм анализа цепочек событий с целью прогнозирования временных послеоперационных параметров хирургических операций, таких как длительность пребывания в стационаре и в отделении реанимации после операции. Описано построение модели события с использованием специализированных языковых нейросетевых моделей. На основе обезличенных данных архива электронных медицинских карт и заключений выписных эпикризов пациентов с выполненными кардиохирургическими операциями проведена алгоритмическая обработка, которая позволяет выделять кластеры операций и прогнозировать значения временных послеоперационных параметров. В статье представлен алгоритм формирования цепочек событий, описание метода их анализа и результаты экспериментальной проверки метода.

The article presents a situational algorithm for analyzing event sequences to predict postoperative time parameters – such as the duration of hospital and intensive care unit stays. It describes the construction of an event model using specialized neural language models. Algorithmic processing was performed using anonymized data from an electronic medical record archive and patient discharge summaries following cardiac surgery. Such approach enables the identification of operation clusters and the prediction of postoperative temporal parameters. The article presents an algorithm for constructing event sequences, describes the analysis method, and reports the results of the method's experimental validation.

Цепочки событийнейронные сетирасстояние Левенштейнасобытия послеоперационного периодаChains of eventsneural networksLevenshtein distancepostoperative eventsИсследование выполнено при поддержке гранта Российского научного фонда № 25-11-90002, https://rscf.ru/project/25-11-90002/ReferencesHramov A, Ghosh D, Pisarchik A, et al. Artificial intelligence and complex networks meet natural sciences. The European Physical Journal Special Topics. 2025. doi: 10.1140/epjs/s11734-025-01844-0.Hramov A, Ghosh D, Pisarchik A, et al. Artificial intelligence and complex networks meet natural sciences. The European Physical Journal Special Topics. 2025. doi: 10.1140/epjs/s11734-025-01844-0.Andrikov D.A. Open source ML framework algorithms for biophysical models. Eur. Phys. J. Spec. Top. 2025. doi: 10.1140/epjs/s11734-025-01621-z.Andrikov D.A. Open source ML framework algorithms for biophysical models. Eur. Phys. J. Spec. 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The authors declare that there are no conflicts of interest present.