[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117960-en":3,"doc-seo-117960-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117960,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Techniques in Diagnostics and Prediction of the Clinical Features of Schizophrenia: A Narrative Review","Schizophrenia is a severe psychiatric disorder with substantial negative consequences for patients. Early diagnosis and timely treatment improve clinical outcomes and quality of life, motivating development of more precise decision-support approaches. This narrative literature review analyzes how machine learning methods can enhance diagnosis and forecast clinical features of schizophrenia. Searches in major databases cover studies published between January 1, 2010 and March 31, 2023. Results summarize applications across imaging, neurophysiology, behavior, speech, and risk prediction for key clinical manifestations.","REVIEW  \nMachine Learning Techniques in Diagnostics and Prediction of the Clinical Features of Schizophrenia:  \nA Narrative Review  \nИспользование методов машинного обучения в диагностике и прогнозированииклинических особенностей шизофрении: нарративный обзор литературы  \ndoi: 10.17816/CP11030  \nReview  \nVadim Gashkarimov1, Renata Sultanova2, Ilya Efremov3,4, Azat Asadullin3,4,5  \n1 Republican Clinical Psychiatric Hospital, Ufa, Russia  \n2 Moscow Research and Clinical Center for Neuropsychiatry of Moscow Healthcare Department, Moscow, Russia  \n3 Bashkir State Medical University, Ufa, Russia  \n4 V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology, Saint Petersburg, Russia  \n5 Republican Clinical Psychotherapeutic Center, Ufa, Russia  \nВадим Гашкаримов1, Рената Султанова2,Илья Ефремов3,4, Азат Асадуллин3,4,5  \n1 ГБУЗ «Республиканская клиническая психиатрическаябольница» Минздрава Республики Башкортостан,Уфа, Россия  \n2 ГБУЗ «Научно-практический психоневрологическийцентр имени З.П. Соловьева» Департаментаздравоохранения города Москвы, Москва, Россия  \n3 ФГБОУ ВО «Башкирский Государственный МедицинскийУниверситет» Минздрава России, Уфа, Россия  \n4 ФГБУ «Национальный медицинский исследовательскийцентр психиатрии и неврологии им. В.М. Бехтерева»Минздрава России, Санкт-Петербург, Россия  \n5 ГБУЗ «Республиканский клиническийпсихотерапевтический центр» Минздрава РеспубликиБашкортостан, Уфа, Россия  \nABSTRACT  \nBACKGROUND: 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.  \nAIM: 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.  \nMETHODS: The Google Scholar, PubMed, and [eLIBRARY. ru](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.  \nConsortium Psychiatricum | 2023 | Volume 4 | Issue 3 CC BY-NC-ND 4.0 license © Authors, 2023 CP11030  \nRESULTS: 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.  \nCONCLUSION: 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 th","cbCainjT3C5zYDAS","https://ap.wps.com/l/cbCainjT3C5zYDAS","pdf",246425,1,11,"English","en",105,"# Abstract\n## Background\n## Aim\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What is the purpose of this narrative review?\",\"answer\":\"To collect and summarize information on using machine learning techniques for prediction and diagnosis of schizophrenia and for determining its clinical features.\"},{\"question\":\"Which databases and time range were used for the literature search?\",\"answer\":\"Searches were conducted in Google Scholar, PubMed, and eLIBRARY.ru, including articles published from January 1, 2010 to March 31, 2023.\"},{\"question\":\"How are machine learning methods used in schizophrenia diagnostics according to the review?\",\"answer\":\"They are applied to interpret MRI, EEG, and actigraphy findings and to analyze speech, behavior, and creativity data for psychiatric diagnosis and prediction.\"}]","Machine Learning Techniques in Diagnostics and Prediction of the Clinical Features of Schizophrenia: A Narrative Review | 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