Classificação de transtorno bipolar, esquizofrenia e depressão utilizando redes neurais artificiais

• Ano de defesa: 2019
• Autor(a) principal: Fonseca, Mateus Beck
• Orientador(a): Oses, Jean Pierre
• Banca de defesa: Wiener, Carolina David , Moreira , Fernanda Pedrotti , Almeida, Sergio José Melo de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Catolica de Pelotas
• Programa de Pós-Graduação: Programa de Pos-Graduacao em Saude Comportamento
• Departamento: Centro de Ciencias da Saude
• País: Brasil
• Palavras-chave em Português: biomarcadores; redes neurais artificiais; transtorno bipolar; esquizofrenia; depressão; citocinas inflamatórias; neurotrofinas
• Palavras-chave em Inglês: biomarkers; artificial neural networks; bipolar disorder; schizophrenia; cytokines, neurotrophins
• Área do conhecimento CNPq: MEDICINA::PSIQUIATRIA
• Link de acesso: http://tede.ucpel.edu.br:8080/jspui/handle/jspui/782

Bipolar disorder and schizophrenia are disorders of difficult diagnosis and differentiation, and studies point to the alteration of levels of inflammatory biomarkers with the diagnosis of diseases. Artificial neural networks (ANNs) are computational tools of artificial intelligence for modeling based on biological neural systems, which use mathematical formulas mimicking neural behavior. The objective of this work is to propose a model of ANN to aid in the diagnosis of bipolar disorder and schizophrenia using biomarkers and simple characteristics of the sampled population. The method of analysis is ANN training applied to a free distribution database of the Stanley Neuropathology Consortium, which consists of inflammatory biomarkers and characteristics of the population with diagnoses of schizophrenia, bipolar disorder and a control (without mental disorders) group. The RNA training program used is OpenNN, and is also freely distributed. As a result, it is expected to train a ANN with more than 80% accuracy in the classification of bipolar disorder diagnoses, schizophrenia and control group. Bipolar disorder, major depression and schizophrenia are disorders of difficult diagnosis and differentiation. Studies indicate that altered levels of inflammatory and neurotrophic biomarkers may be associated with the diagnosis of these diseases. Artificial neural networks (ANN) are computational tools of artificial intelligence for modeling based on biological neural systems, which use mathematical formulas mimicking neural behavior. The objective of this work is to propose an ANN model to aid in the diagnosis of bipolar disorder, major depression and schizophrenia, using biomarkers and simple characteristics of the population sampled. The method of analysis for the first article is ANN training applied to a free distribution database of the Stanley Neuropathology Consortium, which consists of inflammatory biomarkers and characteristics of the population with diagnoses of schizophrenia, bipolar disorder and one control group (without disorders); for the second article, another database was used, with biochemical variables, population characteristics and questionnaire responses with diagnoses of major depression, bipolar disorder and a control group (without disorders). The RNA training program used is OpenNN, and it is also freely distributed. As a result, trained RNAs with more than 80 % accuracy in diagnostic classifications