В обзоре представлен анализ научной литературы по результатам использования методов машинного обучения (МО) для оценки предтестовой вероятности (ПТВ) обструктивных (ОПКА) и необструктивных (НПКА) поражений коронарных артерий (КА) у больных с различными клиническими вариантами ишемической болезни сердца. Приведены данные о высокой распространенности НПКА среди лиц, направляемых на инвазивную коронарографию (КАГ), что послужило поводом для разработки моделей и алгоритмов на основе методов МО для использования в качестве дополнительных инструментов ПТВ, позволяющих прогнозировать анатомический статус КА до проведения КАГ. Применение современных технологий моделирования обладает большим потенциалом в верификации НПКА и ОПКА. Подчеркивается, что совершенствование прогностических моделей и их внедрение в клиническую практику является важным элементом поддержки принятия врачебных решений и должно осуществляться на основе междисциплинарной научной кооперации клиницистов и специалистов в области информационных технологий.Ключевые слова: предтестовая вероятность, машинное обучение, поражение коронарных артерий.
Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. The review presents an analysis of literature on the use of ML for diagnosing and predicting the clinical course of coronary artery disease. We provided information on reference databases, the use of which allows to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of ML-based models and their introduction into clinical practice will help support medical decision-making, increase the effectiveness of treatment and optimize health care costs.
Machine learning (ML) is among the main tools of artificial intelligence and are increasingly used in population and clinical cardiology to stratify cardiovascular risk. The systematic review presents an analysis of literature on using various ML methods (artificial neural networks, random forest, stochastic gradient boosting, support vector machines, etc.) to develop predictive models determining the immediate and long-term risk of adverse events after coronary artery bypass grafting and percutaneous coronary intervention. Most of the research on this issue is focused on creation of novel forecast models with a higher predictive value. It is emphasized that the improvement of modeling technologies and the development of clinical decision support systems is one of the most promising areas of digitalizing healthcare that are in demand in everyday professional activities.
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