Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , , , , , , , , , , , , |
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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Brazilian Journal of Health Review |
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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 |
language |
por |
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) instacron:BJRH |
instname_str |
Federação das Indústrias do Estado do Paraná (FIEP) |
instacron_str |
BJRH |
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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1797240045399179264 |