Consortium PSYCHIATRICUMConsortium PSYCHIATRICUMConsortium PSYCHIATRICUM2712-76722713-2919Eco-Vector1103010.17816/CP11030REVIEWОБЗОРReview ArticleMachine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative reviewИспользование методов машинного обучения в диагностике и прогнозировании клинических особенностей шизофрении: нарративный обзор литературыhttps://orcid.org/0000-0001-9944-141X3828-4634GashkarimovVadim R.ГашкаримовВадим Римович
Russian Federation

clinical resident-psychiatrist, Republican Clinical Psychiatric Hospital

Врач-психиатр, ГБУЗ «Республиканская клиническая психиатрическая больница» Минздрава Республики Башкортостан

gashkarimov@yandex.ruhttps://orcid.org/0000-0001-6679-44548284-8451SultanovaRenata I.СултановаРената Ильдаровна
Russian Federation

psychiatrist, Moscow Research and Clinical Center for Neuropsychiatry of Moscow Healthcare Department

Врач-психиатр, ГБУЗ г. Москвы «Научно-практический психоневрологический центр имени З.П. Соловьева» Департамента здравоохранения города Москвы

renatasu@mail.ruhttps://orcid.org/0000-0002-9994-86569983-8464EfremovIlya S.ЕфремовИлья Сергеевич
Russian Federation

Cand. Sci. (Med.), Assistant, Department of Psychiatry, Narcology and Psychotherapy Bashkir State Medical University; Junior Researcher, V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology

к.м.н., Ассистент кафедры психиатрии, наркологии и психотерапии с курсами ИДПО, ФГБОУ ВО «Башкирский Государственный Медицинский Университет» Минздрава России; Младший научный сотрудник, ФГБУ «Национальный медицинский исследовательский центр психиатрии и неврологии им. В.М. Бехтерева» Минздрава России

efremovilya102@gmail.comhttps://orcid.org/0000-0001-7148-44853740-7843AsadullinAzat R.АсадуллинАзат Раилевич
Russian Federation

Dr. Sci. (Med.), Professor, Department of Psychiatry, Narcology and Psychotherapy Bashkir State Medical University; Leading Researcher, V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology; Deputy Medical Director, Republican Clinical Psychotherapeutic Center

д.м.н., Профессор кафедры психиатрии, наркологии и психотерапии с курсами ИДПО, ФГБОУ ВО «Башкирский Государственный Медицинский Университет» Минздрава России; Ведущий научный сотрудник ФГБУ «Национальный медицинский исследовательский центр психиатрии и неврологии им. В.М. Бехтерева» Минздрава России; Заместитель главного врача по медицинской части, ГБУЗ «Республиканский клинический психотерапевтический центр» Минздрава Республики Башкортостан

droar@yandex.ruRepublican Clinical Psychiatric HospitalГБУЗ «Республиканская клиническая психиатрическая больница» Минздрава Республики БашкортостанMoscow Research and Clinical Center for Neuropsychiatry of Moscow Healthcare DepartmentГБУЗ «Научно-практический психоневрологический центр имени З.П. Соловьева» Департамента здравоохранения города МосквыBashkir State Medical UniversityФГБОУ ВО «Башкирский Государственный Медицинский Университет» Минздрава РоссииV.M. Bekhterev National Medical Research Centre for Psychiatry and NeurologyФГБУ «Национальный медицинский исследовательский центр психиатрии и неврологии им. В.М. Бехтерева» Минздрава РоссииRepublican Clinical Psychotherapeutic CenterГБУЗ «Республиканский клинический психотерапевтический центр» Минздрава Республики Башкортостан04092023290920234343530706202307082023Copyright ©; 2023, Gashkarimov V.R., Sultanova R.I., Efremov I.S., Asadullin A.R.Copyright ©; 2023, Гашкаримов В.Р., Султанова Р.И., Ефремов И.С., Асадуллин А.Р.2023Gashkarimov V.R., Sultanova R.I., Efremov I.S., Asadullin A.R.Гашкаримов В.Р., Султанова Р.И., Ефремов И.С., Асадуллин А.Р.https://creativecommons.org/licenses/by-nc-nd/4.0https://consortium-psy.com/jour/article/view/11030

BACKGROUND: Schizophrenia is a severe psychiatric disorder associated with a significant negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on the clinical outcome and patient’s quality of life. In this context, machine learning techniques open up new opportunities for a more accurate diagnosis and prediction of the clinical features of this illness.

AIM: This literature review is aimed to search for information on the use of machine learning techniques in the prediction and diagnosis of schizophrenia and the determination of its clinical features.

METHODS: The Google Scholar, PubMed, and eLIBRARY.ru databases were used to search for relevant data. The review included articles that had been published not earlier than January 1, 2010, and not later than March 31, 2023. Combinations of the following keywords were applied for search queries: “machine learning”, “deep learning”, “schizophrenia”, “neural network”, “predictors”, “artificial intelligence”, “diagnostics”, “suicide”, “depressive”, “insomnia”, and “cognitive”. Original articles regardless of their design were included in the review. Descriptive analysis was used to summarize the retrieved data.

RESULTS: Machine learning techniques are widely used in the functional assessment of patients with schizophrenia. They are used for interpretation of MRI, EEG, and actigraphy findings. Also, models created using machine learning algorithms can analyze speech, behavior, and the creativity of people and these data can be used for the diagnosis of psychiatric disorders. It has been found that different machine learning-based models can help specialists predict and diagnose schizophrenia based on medical history and genetic data, as well as epigenetic information. Machine learning techniques can also be used to build effective models that can help specialists diagnose and predict clinical manifestations and complications of schizophrenia, such as insomnia, depressive symptoms, suicide risk, aggressive behavior, and changes in cognitive functions over time.

CONCLUSION: Machine learning techniques play an important role in psychiatry, as they have been used in models that help specialists in the diagnosis of schizophrenia and determination of its clinical features. The use of machine learning algorithms is one of the most promising direction in psychiatry, and it can significantly improve the effectiveness of the diagnosis and treatment of schizophrenia.

ВВЕДЕНИЕ: Шизофрения является тяжелым психическим расстройством, которое влечет за собой значительные негативные последствия. Раннее выявление шизофрении и ее лечение благоприятно влияют на клинический прогноз и качество жизни пациента. В этом контексте методы машинного обучения открывают новые возможности для более точной диагностики и прогнозирования клинических особенностей данного расстройства.

ЦЕЛЬ: Данный обзор литературы направлен на поиск информации о применении методов машинного обучения в прогнозировании и диагностике шизофрении и ее клинических особенностей.

МЕТОДЫ: Поиск материала был осуществлен в базах данных Google Scholar, PubMed, eLIBRARY.ru. В обзор включались работы, опубликованные не раньше 1 января 2010 г. и не позже 31 марта 2023 г. Поисковые запросы формировались путем комбинации ключевых слов: “machine learning”, “deep learning”, “schizophrenia”, “neural network”, “predictors”, “artificial intelligence”, “diagnostics”, “suicide”, “depressive”, “insomnia”, “cognitive”. В обзор включались оригинальные исследования независимо от их дизайна. Для обобщения полученных данных использовался описательный анализ.

РЕЗУЛЬТАТЫ: Методы машинного обучения широко применяются в функциональной диагностике шизофрении. Их используют в распознавании данных от МРТ, ЭЭГ, актиграфии. Также модели, созданные с помощью алгоритмов машинного обучения, могут анализировать речь, поведение, творчество людей для диагностики психических расстройств. Было установлено, что различные модели, построенные на основе машинного обучения, способны помогать специалистам прогнозировать и диагностировать шизофрению, основываясь на анамнестической, генетической, эпигенетической информации. Методы машинного обучения также успешно применяются для построения моделей, которые способны помогать специалистам диагностировать и прогнозировать клинические проявления и осложнения шизофрении, такие как бессонница, депрессивные проявления, риск суицида, агрессивное поведение, динамика когнитивных функций.

ЗАКЛЮЧЕНИЕ: Применение методов машинного обучения играет важную роль в психиатрии, с их помощью разработаны модели, помогающие специалистам в диагностике шизофрении и ее клинических особенностей. Применение алгоритмов машинного обучения является одним из наиболее перспективных направлений в психиатрии, это может значительно повысить эффективность диагностики и лечения шизофрении.

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