Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/267032 |
Resumo: | Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics. |
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Veiga, Rafael ValenteFaccini, Lavinia SchulerFrança, Giovanny Vinícius Araújo deAndrade, Roberto Fernandes SilvaTeixeira, Maria GloriaCosta, Larissa CatharinaPaixão, Enny Santos daCosta, Maria da Conceição NascimentoBarreto, Mauricio LimaOliveira, Juliane Fonseca deOliveira, Wanderson Kleber deCardim, Luciana LobatoRodrigues, Moreno Magalhães de Souza2023-11-11T03:25:29Z20212045-2322http://hdl.handle.net/10183/267032001153745Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics.application/pdfengScientific reports. London. Vol. 11 (2021), e6770, 7 p.Zika virusMicrocefaliaCiência da computaçãoInfecção viralAprendizado de máquinaClassification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validationEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001153745.pdf.txt001153745.pdf.txtExtracted Texttext/plain28269http://www.lume.ufrgs.br/bitstream/10183/267032/2/001153745.pdf.txt33b0f0ebd50ff249639740f6507c61eaMD52ORIGINAL001153745.pdfTexto completo (inglês)application/pdf1103696http://www.lume.ufrgs.br/bitstream/10183/267032/1/001153745.pdfd2bc1a2076f0861242164a53d44a244aMD5110183/2670322023-11-12 04:24:22.396252oai:www.lume.ufrgs.br:10183/267032Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-11-12T06:24:22Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
title |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
spellingShingle |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation Veiga, Rafael Valente Zika virus Microcefalia Ciência da computação Infecção viral Aprendizado de máquina |
title_short |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
title_full |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
title_fullStr |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
title_full_unstemmed |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
title_sort |
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation |
author |
Veiga, Rafael Valente |
author_facet |
Veiga, Rafael Valente Faccini, Lavinia Schuler França, Giovanny Vinícius Araújo de Andrade, Roberto Fernandes Silva Teixeira, Maria Gloria Costa, Larissa Catharina Paixão, Enny Santos da Costa, Maria da Conceição Nascimento Barreto, Mauricio Lima Oliveira, Juliane Fonseca de Oliveira, Wanderson Kleber de Cardim, Luciana Lobato Rodrigues, Moreno Magalhães de Souza |
author_role |
author |
author2 |
Faccini, Lavinia Schuler França, Giovanny Vinícius Araújo de Andrade, Roberto Fernandes Silva Teixeira, Maria Gloria Costa, Larissa Catharina Paixão, Enny Santos da Costa, Maria da Conceição Nascimento Barreto, Mauricio Lima Oliveira, Juliane Fonseca de Oliveira, Wanderson Kleber de Cardim, Luciana Lobato Rodrigues, Moreno Magalhães de Souza |
author2_role |
author author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Veiga, Rafael Valente Faccini, Lavinia Schuler França, Giovanny Vinícius Araújo de Andrade, Roberto Fernandes Silva Teixeira, Maria Gloria Costa, Larissa Catharina Paixão, Enny Santos da Costa, Maria da Conceição Nascimento Barreto, Mauricio Lima Oliveira, Juliane Fonseca de Oliveira, Wanderson Kleber de Cardim, Luciana Lobato Rodrigues, Moreno Magalhães de Souza |
dc.subject.por.fl_str_mv |
Zika virus Microcefalia Ciência da computação Infecção viral Aprendizado de máquina |
topic |
Zika virus Microcefalia Ciência da computação Infecção viral Aprendizado de máquina |
description |
Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021 |
dc.date.accessioned.fl_str_mv |
2023-11-11T03:25:29Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/267032 |
dc.identifier.issn.pt_BR.fl_str_mv |
2045-2322 |
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001153745 |
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2045-2322 001153745 |
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http://hdl.handle.net/10183/267032 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Scientific reports. London. Vol. 11 (2021), e6770, 7 p. |
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info:eu-repo/semantics/openAccess |
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openAccess |
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