دورية أكاديمية
أعرض في EDS

Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers.

التفاصيل البيبلوغرافية
العنوان: Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers.
المؤلفون: Kozyrev EA; Budker Institute of Nuclear Physics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia., Ermakov EA; Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia., Boiko AS; Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia., Mednova IA; Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia., Kornetova EG; Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia.; University Hospital, Siberian State Medical University, 634050 Tomsk, Russia., Bokhan NA; Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia.; Psychiatry, Addiction Psychiatry and Psychotherapy Department, Siberian State Medical University, 634050 Tomsk, Russia., Ivanova SA; Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia.; Psychiatry, Addiction Psychiatry and Psychotherapy Department, Siberian State Medical University, 634050 Tomsk, Russia.
المصدر: Biomedicines [Biomedicines] 2023 Jul 14; Vol. 11 (7). Date of Electronic Publication: 2023 Jul 14.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101691304 Publication Model: Electronic Cited Medium: Print ISSN: 2227-9059 (Print) Linking ISSN: 22279059 NLM ISO Abbreviation: Biomedicines Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI AG, [2013]-
مستخلص: Machine learning and artificial intelligence technologies are known to be a convenient tool for analyzing multi-domain data in precision psychiatry. In the case of schizophrenia, the most commonly used data sources for such purposes are neuroimaging, voice and language patterns, and mobile phone data. Data on peripheral markers can also be useful for building predictive models. Here, we have developed five predictive models for the binary classification of schizophrenia patients and healthy individuals. Data on serum concentrations of cytokines, chemokines, growth factors, and age were among 38 parameters used to build these models. The sample consisted of 217 schizophrenia patients and 90 healthy individuals. The models architecture was involved logistic regression, deep neural networks, decision trees, support vector machine, and k-nearest neighbors algorithms. It was shown that the algorithm based on a deep neural network (consisting of five layers) showed a slightly higher sensitivity (0.87 ± 0.04) and specificity (0.52 ± 0.06) than other algorithms. Combining all variables into a single classifier showed a cumulative effect that exceeded the effectiveness of individual variables, indicating the need to use multiple biomarkers to diagnose schizophrenia. Thus, the data obtained showed the promise of using data on peripheral biomarkers and machine learning methods for diagnosing schizophrenia.
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معلومات مُعتمدة: 22-25-00633 Russian Science Foundation
فهرسة مساهمة: Keywords: artificial intelligence; biomarkers; decision trees; deep neural network; k-nearest neighbors; logistic regression; machine learning; predictive model; schizophrenia; support vector machine
تواريخ الأحداث: Date Created: 20230729 Latest Revision: 20230731
رمز التحديث: 20230731
مُعرف محوري في PubMed: PMC10377576
DOI: 10.3390/biomedicines11071990
PMID: 37509629
قاعدة البيانات: MEDLINE
الوصف
تدمد:2227-9059
DOI:10.3390/biomedicines11071990