ILITIA: telehealth architecture for high-risk gestation classification

Detalhes bibliográficos
Autor(a) principal: Fernandes, Yáskara Ygara Menescal Pinto
Data de Publicação: 2017
Outros Autores: Araújo, Giseuda Teixeira de, Araújo, Bruno Gomes de, Dantas, Marcel da Câmara Ribeiro, Carvalho, Diego Rodrigues de, Valentim, Ricardo Alexsandro de Medeiros
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/29315
Resumo: Introduction: According to the World Health Organization, about 9.2% of the 28 million newborns worldwide are stillborn. Besides, about 358,000 women died due to complications related to pregnancy in 2015. Part of these deaths could have been avoided with improving prenatal care agility to recognize problems during pregnancy. Based on that, many efforts have been made to provide technologies that can contribute to offer better access to information and assist in decision-making. In this context, this work presents an architecture to automate the classification and referral process of pregnant women between the basic health units and the referral hospital through a Telehealth platform. Methods: The Telehealth architecture was developed in three components: The data acquisition component, responsible for collecting and inserting data; the data processing component, which is the core of the architecture implemented using expert systems to classify gestational risk; and the post-processing component, in charge of the delivery and analysis of cases. Results: Acceptance test, system accuracy test based on rules and performance test were realized. For the tests, 1,380 referral forms of real situations were used. Conclusion: On the results obtained with the analysis of real data, ILITIA, the developed architecture has met the requirements to assist medical specialists on gestational risk classification, which decreases the inconvenience of pregnant women displacement and the resulting costs
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spelling Fernandes, Yáskara Ygara Menescal PintoAraújo, Giseuda Teixeira deAraújo, Bruno Gomes deDantas, Marcel da Câmara RibeiroCarvalho, Diego Rodrigues deValentim, Ricardo Alexsandro de Medeiros2020-06-22T15:11:09Z2020-06-22T15:11:09Z2017FERNANDES, Y. Y. M. P.; VALENTIM, R. A. M.; CARVALHO, D. R.; DANTAS, M. C. R.. ILITIA: telehealth architecture for high-risk gestation classification. Research on biomedical engineering, v. 33, p. 237-246, 2017. Disponível em: https://rbejournal.org/article/doi/10.1590/2446-4740.09416. Acesso em: 18 Jun. 2020. http://dx.doi.org/10.1590/2446-4740.09416.2446-4740https://repositorio.ufrn.br/jspui/handle/123456789/2931510.1590/2446-4740.09416Research on Biomedical EngineeringAttribution 3.0 Brazilhttp://creativecommons.org/licenses/by/3.0/br/info:eu-repo/semantics/openAccessHigh-risk pregnancyTelehealthReferral protocolExpert systemsILITIA: telehealth architecture for high-risk gestation classificationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleIntroduction: According to the World Health Organization, about 9.2% of the 28 million newborns worldwide are stillborn. Besides, about 358,000 women died due to complications related to pregnancy in 2015. Part of these deaths could have been avoided with improving prenatal care agility to recognize problems during pregnancy. Based on that, many efforts have been made to provide technologies that can contribute to offer better access to information and assist in decision-making. In this context, this work presents an architecture to automate the classification and referral process of pregnant women between the basic health units and the referral hospital through a Telehealth platform. Methods: The Telehealth architecture was developed in three components: The data acquisition component, responsible for collecting and inserting data; the data processing component, which is the core of the architecture implemented using expert systems to classify gestational risk; and the post-processing component, in charge of the delivery and analysis of cases. Results: Acceptance test, system accuracy test based on rules and performance test were realized. For the tests, 1,380 referral forms of real situations were used. Conclusion: On the results obtained with the analysis of real data, ILITIA, the developed architecture has met the requirements to assist medical specialists on gestational risk classification, which decreases the inconvenience of pregnant women displacement and the resulting costsengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALILITIA-TelehealthArchitecture_Valentim_2017.pdfILITIA-TelehealthArchitecture_Valentim_2017.pdfapplication/pdf1517222https://repositorio.ufrn.br/bitstream/123456789/29315/1/ILITIA-TelehealthArchitecture_Valentim_2017.pdf763bf190e6180c5fe92364db9cc8776eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.ufrn.br/bitstream/123456789/29315/2/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/29315/3/license.txte9597aa2854d128fd968be5edc8a28d9MD53TEXTILITIA-TelehealthArchitecture_Valentim_2017.pdf.txtILITIA-TelehealthArchitecture_Valentim_2017.pdf.txtExtracted texttext/plain38935https://repositorio.ufrn.br/bitstream/123456789/29315/4/ILITIA-TelehealthArchitecture_Valentim_2017.pdf.txta699f0f7a8e80793c6161d2396589bf5MD54THUMBNAILILITIA-TelehealthArchitecture_Valentim_2017.pdf.jpgILITIA-TelehealthArchitecture_Valentim_2017.pdf.jpgGenerated Thumbnailimage/jpeg1538https://repositorio.ufrn.br/bitstream/123456789/29315/5/ILITIA-TelehealthArchitecture_Valentim_2017.pdf.jpgb6ba8ca5a953f8722497e224cfcb9651MD55123456789/293152020-06-28 04:40:56.545oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2020-06-28T07:40:56Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv ILITIA: telehealth architecture for high-risk gestation classification
title ILITIA: telehealth architecture for high-risk gestation classification
spellingShingle ILITIA: telehealth architecture for high-risk gestation classification
Fernandes, Yáskara Ygara Menescal Pinto
High-risk pregnancy
Telehealth
Referral protocol
Expert systems
title_short ILITIA: telehealth architecture for high-risk gestation classification
title_full ILITIA: telehealth architecture for high-risk gestation classification
title_fullStr ILITIA: telehealth architecture for high-risk gestation classification
title_full_unstemmed ILITIA: telehealth architecture for high-risk gestation classification
title_sort ILITIA: telehealth architecture for high-risk gestation classification
author Fernandes, Yáskara Ygara Menescal Pinto
author_facet Fernandes, Yáskara Ygara Menescal Pinto
Araújo, Giseuda Teixeira de
Araújo, Bruno Gomes de
Dantas, Marcel da Câmara Ribeiro
Carvalho, Diego Rodrigues de
Valentim, Ricardo Alexsandro de Medeiros
author_role author
author2 Araújo, Giseuda Teixeira de
Araújo, Bruno Gomes de
Dantas, Marcel da Câmara Ribeiro
Carvalho, Diego Rodrigues de
Valentim, Ricardo Alexsandro de Medeiros
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Fernandes, Yáskara Ygara Menescal Pinto
Araújo, Giseuda Teixeira de
Araújo, Bruno Gomes de
Dantas, Marcel da Câmara Ribeiro
Carvalho, Diego Rodrigues de
Valentim, Ricardo Alexsandro de Medeiros
dc.subject.por.fl_str_mv High-risk pregnancy
Telehealth
Referral protocol
Expert systems
topic High-risk pregnancy
Telehealth
Referral protocol
Expert systems
description Introduction: According to the World Health Organization, about 9.2% of the 28 million newborns worldwide are stillborn. Besides, about 358,000 women died due to complications related to pregnancy in 2015. Part of these deaths could have been avoided with improving prenatal care agility to recognize problems during pregnancy. Based on that, many efforts have been made to provide technologies that can contribute to offer better access to information and assist in decision-making. In this context, this work presents an architecture to automate the classification and referral process of pregnant women between the basic health units and the referral hospital through a Telehealth platform. Methods: The Telehealth architecture was developed in three components: The data acquisition component, responsible for collecting and inserting data; the data processing component, which is the core of the architecture implemented using expert systems to classify gestational risk; and the post-processing component, in charge of the delivery and analysis of cases. Results: Acceptance test, system accuracy test based on rules and performance test were realized. For the tests, 1,380 referral forms of real situations were used. Conclusion: On the results obtained with the analysis of real data, ILITIA, the developed architecture has met the requirements to assist medical specialists on gestational risk classification, which decreases the inconvenience of pregnant women displacement and the resulting costs
publishDate 2017
dc.date.issued.fl_str_mv 2017
dc.date.accessioned.fl_str_mv 2020-06-22T15:11:09Z
dc.date.available.fl_str_mv 2020-06-22T15:11:09Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.fl_str_mv FERNANDES, Y. Y. M. P.; VALENTIM, R. A. M.; CARVALHO, D. R.; DANTAS, M. C. R.. ILITIA: telehealth architecture for high-risk gestation classification. Research on biomedical engineering, v. 33, p. 237-246, 2017. Disponível em: https://rbejournal.org/article/doi/10.1590/2446-4740.09416. Acesso em: 18 Jun. 2020. http://dx.doi.org/10.1590/2446-4740.09416.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/jspui/handle/123456789/29315
dc.identifier.issn.none.fl_str_mv 2446-4740
dc.identifier.doi.none.fl_str_mv 10.1590/2446-4740.09416
identifier_str_mv FERNANDES, Y. Y. M. P.; VALENTIM, R. A. M.; CARVALHO, D. R.; DANTAS, M. C. R.. ILITIA: telehealth architecture for high-risk gestation classification. Research on biomedical engineering, v. 33, p. 237-246, 2017. Disponível em: https://rbejournal.org/article/doi/10.1590/2446-4740.09416. Acesso em: 18 Jun. 2020. http://dx.doi.org/10.1590/2446-4740.09416.
2446-4740
10.1590/2446-4740.09416
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dc.language.iso.fl_str_mv eng
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dc.rights.driver.fl_str_mv Attribution 3.0 Brazil
http://creativecommons.org/licenses/by/3.0/br/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 3.0 Brazil
http://creativecommons.org/licenses/by/3.0/br/
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dc.publisher.none.fl_str_mv Research on Biomedical Engineering
publisher.none.fl_str_mv Research on Biomedical Engineering
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