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UFMG_cbce76329f0f06645a761c42d138260c
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oai:repositorio.ufmg.br:1843/60519
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UFMG
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Repositório Institucional da UFMG
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Repositório Institucional da UFMG
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UFMG
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Universidade Federal de Minas Gerais (UFMG)
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Universidade Federal de Minas Gerais (UFMG)
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2023-11-06T20:40:26Z2023-11-06T20:40:26Z2023-04-19452127131http://doi.org/10.47626/1516-4446-2022-28111809-452Xhttp://hdl.handle.net/1843/60519https://orcid.org/0000-0001-7516-5473https://orcid.org/0000-0003-1947-9675https://orcid.org/0000-0003-1876-2022https://orcid.org/0000-0003-4125-3736CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoOBJECTIVE: Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. METHODS: Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. RESULTS: Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). CONCLUSION: Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.engUniversidade Federal de Minas GeraisUFMGBrasilMED - DEPARTAMENTO DE SAÚDE MENTALMEDICINA - FACULDADE DE MEDICINABrazilian Journal of PsychiatryInfânciaAprendizado do computadorTomografia por emissão de pósitronsTentativa de SuicídioTranstorno BipolarBipolar disorderChildhood maltreatmentSuicide attempt18F-FDGPositron emission tomographyMachine learningExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Studyinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.bjp.org.br/details/2335/en-US/examining-differences-in-brain-metabolism-associated-with-childhood-maltreatment-and-suicidal-attempt-in-euthymic-patients-with-bipolar-disorder--a-peDante DuarteManuel SchützeMazen ElkhayatMaila de Castro Lourenço Das NevesMarco Aurélio Romano SilvaHumberto Correaapplication/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/60519/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder_ A PET and Machine Learning Study.pdfExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder_ A PET and Machine Learning Study.pdfapplication/pdf155077https://repositorio.ufmg.br/bitstream/1843/60519/2/Examining%20differences%20in%20brain%20metabolism%20associated%20with%20childhood%20maltreatment%20and%20suicidal%20attempt%20in%20euthymic%20patients%20with%20bipolar%20disorder_%20A%20PET%20and%20Machine%20Learning%20Study.pdf383ebeb9b54e17ac1bd5f5642005d0a1MD521843/605192023-11-06 17:40:26.664oai:repositorio.ufmg.br:1843/60519Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-11-06T20:40:26Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
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