ILITIA: telehealth architecture for high-risk gestation classification
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/29315 |
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eng |
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eng |
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Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ |
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openAccess |
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Research on Biomedical Engineering |
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Research on Biomedical Engineering |
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