ILITIA: telehealth architecture for high-risk gestation classification

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

ILITIA: telehealth architecture for high-risk gestation classification

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dc.contributor.author	Fernandes, Yáskara Ygara Menescal Pinto
dc.contributor.author	Araújo, Giseuda Teixeira de
dc.contributor.author	Araújo, Bruno Gomes de
dc.contributor.author	Dantas, Marcel da Câmara Ribeiro
dc.contributor.author	Carvalho, Diego Rodrigues de
dc.contributor.author	Valentim, Ricardo Alexsandro de Medeiros
dc.date.accessioned	2020-06-22T15:11:09Z
dc.date.available	2020-06-22T15:11:09Z
dc.date.issued	2017
dc.description.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	pt_BR
dc.identifier.citation	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.	pt_BR
dc.identifier.doi	10.1590/2446-4740.09416
dc.identifier.issn	2446-4740
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/29315
dc.language	en	pt_BR
dc.publisher	Research on Biomedical Engineering	pt_BR
dc.rights	Attribution 3.0 Brazil	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/br/	*
dc.subject	High-risk pregnancy	pt_BR
dc.subject	Telehealth	pt_BR
dc.subject	Referral protocol	pt_BR
dc.subject	Expert systems	pt_BR
dc.title	ILITIA: telehealth architecture for high-risk gestation classification	pt_BR
dc.type	article	pt_BR