Возможности информационных систем в прогнозировании исходов новой коронавирусной инфекции COVID-19

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СтатьяМЕД СОВЕТ. 2022; № 4: 42–50. DOI:10.21518/2079-701X-2022-16-4-42-50

Возможности информационных систем в прогнозировании исходов новой коронавирусной инфекции COVID-19

Демко И.В.^[1,2]

Корчагин Е.Е.^[1]

Черкашин О.А.^[1]

Гордеева Н.В.^[1,2]

Аникин Д.А.^[1,2]

Аникина Д.А.^[2]

^[1]Краевая клиническая больница

^[2]Красноярский государственный медицинский университет им. В. Ф. Войно-Ясенецкого

Пандемия коронавирусной инфекции COVID-19 (Coronavirus Disease 2019), вызванная новым штаммом коронавируса SARSCoV-2 (severe acute respiratory syndrome coronavirus 2), стала причиной быстрого роста количества заболевших и высокой смертности во всем мире. Клинические проявления COVID-19 неспецифичны, их диагностика основывается на комплексной оценке клинических, лабораторных и рентгенологических данных. Развитие пандемии поставило перед специалистами здравоохранения новые задачи: разработать диагностические и лечебные алгоритмы, а также меры и средства профилактики. На современном этапе большое внимание уделяется внедрению информационных систем в медицинскую практику, а также применению технологий интеллектуального анализа данных с целью повышения качества оказываемой медицинской помощи. Отмечено, что применение искусственного интеллекта (ИИ) в медицине было связано с разработкой программ ИИ, предназначенных для усовершенствования методик постановки диагноза, подбора терапевтических решений и прогнозирования исхода болезни. Такие системы включают в себя искусственные нейронные сети (ИНС), нечеткие экспертные системы, эволюционные вычисления и гибридные интеллектуальные системы. В статье проанализированы данные ряда исследований по использованию искусственного интеллекта для неспецифической диагностики COVID-19, прогнозирования риска смертности и изучения факторов риска тяжелого течения и смерти в различных группах. С использованием вычислительных алгоритмов были построены кластеры предикторов смертности на основе медицинских данных для пациентов с COVID-19. Используя кластеры, были разработаны модели для прогнозирования смертности и понимания взаимосвязи различных демографических характеристик и заболеваний со смертностью от COVID-19. Также в статье обобщены ключевые факторы, ухудшающие прогноз при COVID-19. Отдельным пунктом отмечены шкалы для выявления или прогноза развития COVID-19-индуцированного «цитокинового шторма».

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Язык текста: Русский

ISSN: 2079-701X

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URI:4e432d4d4544534f5645542d41525449434c452d323032322d302d342d302d34322d3530

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