Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties

Detalhes bibliográficos
Autor(a) principal: Marques, Paulo César F.
Data de Publicação: 2024
Outros Autores: Soares, Lucas Rafael Ferreira, Araujo, André Victor de Albuquerque, Monteiro, Arthur Ribeiro, Batista, Arthur Almeida Leitão, Pimentel, Túlio Farias, Calheiros, Lis de Lima, Padilla, Maria Helena N. S., Pacheco, André Luiz de Góes, Silva , Fabio Queda Bueno da, Oliveira, João Ricardo M., Lima Filho, José Luiz de, Bocanegra, Silvana, Albuquerque, Jones
Tipo de documento: Artigo
Idioma: por
Título da fonte: Brazilian Journal of Health Review
Texto Completo: https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/68803
Resumo: Schizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia.
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spelling Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric propertiesschizophreniaclassification modelsclassification models geometricsSchizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia.Brazilian Journals Publicações de Periódicos e Editora Ltda.2024-04-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/6880310.34119/bjhrv7n2-337Brazilian Journal of Health Review; Vol. 7 No. 2 (2024); e68803Brazilian Journal of Health Review; Vol. 7 Núm. 2 (2024); e68803Brazilian Journal of Health Review; v. 7 n. 2 (2024); e688032595-6825reponame:Brazilian Journal of Health Reviewinstname:Federação das Indústrias do Estado do Paraná (FIEP)instacron:BJRHporhttps://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/68803/48770Marques, Paulo César F.Soares, Lucas Rafael FerreiraAraujo, André Victor de AlbuquerqueMonteiro, Arthur RibeiroBatista, Arthur Almeida LeitãoPimentel, Túlio FariasCalheiros, Lis de LimaPadilla, Maria Helena N. S.Pacheco, André Luiz de GóesSilva , Fabio Queda Bueno daOliveira, João Ricardo M.Lima Filho, José Luiz deBocanegra, SilvanaAlbuquerque, Jonesinfo:eu-repo/semantics/openAccess2024-04-10T19:10:29Zoai:ojs2.ojs.brazilianjournals.com.br:article/68803Revistahttp://www.brazilianjournals.com/index.php/BJHR/indexPRIhttps://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/oai|| brazilianjhr@gmail.com2595-68252595-6825opendoar:2024-04-10T19:10:29Brazilian Journal of Health Review - Federação das Indústrias do Estado do Paraná (FIEP)false
dc.title.none.fl_str_mv Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
title Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
spellingShingle Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
Marques, Paulo César F.
schizophrenia
classification models
classification models geometrics
title_short Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
title_full Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
title_fullStr Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
title_full_unstemmed Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
title_sort Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
author Marques, Paulo César F.
author_facet Marques, Paulo César F.
Soares, Lucas Rafael Ferreira
Araujo, André Victor de Albuquerque
Monteiro, Arthur Ribeiro
Batista, Arthur Almeida Leitão
Pimentel, Túlio Farias
Calheiros, Lis de Lima
Padilla, Maria Helena N. S.
Pacheco, André Luiz de Góes
Silva , Fabio Queda Bueno da
Oliveira, João Ricardo M.
Lima Filho, José Luiz de
Bocanegra, Silvana
Albuquerque, Jones
author_role author
author2 Soares, Lucas Rafael Ferreira
Araujo, André Victor de Albuquerque
Monteiro, Arthur Ribeiro
Batista, Arthur Almeida Leitão
Pimentel, Túlio Farias
Calheiros, Lis de Lima
Padilla, Maria Helena N. S.
Pacheco, André Luiz de Góes
Silva , Fabio Queda Bueno da
Oliveira, João Ricardo M.
Lima Filho, José Luiz de
Bocanegra, Silvana
Albuquerque, Jones
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Marques, Paulo César F.
Soares, Lucas Rafael Ferreira
Araujo, André Victor de Albuquerque
Monteiro, Arthur Ribeiro
Batista, Arthur Almeida Leitão
Pimentel, Túlio Farias
Calheiros, Lis de Lima
Padilla, Maria Helena N. S.
Pacheco, André Luiz de Góes
Silva , Fabio Queda Bueno da
Oliveira, João Ricardo M.
Lima Filho, José Luiz de
Bocanegra, Silvana
Albuquerque, Jones
dc.subject.por.fl_str_mv schizophrenia
classification models
classification models geometrics
topic schizophrenia
classification models
classification models geometrics
description Schizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia.
publishDate 2024
dc.date.none.fl_str_mv 2024-04-10
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/68803
10.34119/bjhrv7n2-337
url https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/68803
identifier_str_mv 10.34119/bjhrv7n2-337
dc.language.iso.fl_str_mv por
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dc.relation.none.fl_str_mv https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/68803/48770
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Brazilian Journals Publicações de Periódicos e Editora Ltda.
publisher.none.fl_str_mv Brazilian Journals Publicações de Periódicos e Editora Ltda.
dc.source.none.fl_str_mv Brazilian Journal of Health Review; Vol. 7 No. 2 (2024); e68803
Brazilian Journal of Health Review; Vol. 7 Núm. 2 (2024); e68803
Brazilian Journal of Health Review; v. 7 n. 2 (2024); e68803
2595-6825
reponame:Brazilian Journal of Health Review
instname:Federação das Indústrias do Estado do Paraná (FIEP)
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instname_str Federação das Indústrias do Estado do Paraná (FIEP)
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institution BJRH
reponame_str Brazilian Journal of Health Review
collection Brazilian Journal of Health Review
repository.name.fl_str_mv Brazilian Journal of Health Review - Federação das Indústrias do Estado do Paraná (FIEP)
repository.mail.fl_str_mv || brazilianjhr@gmail.com
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