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Title: ILITIA: telehealth architecture for high-risk gestation classification
Authors: 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
Keywords: High-risk pregnancy;Telehealth;Referral protocol;Expert systems
Issue Date: 2017
Publisher: Research on Biomedical Engineering
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: Acesso em: 18 Jun. 2020.
Portuguese Abstract: 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
ISSN: 2446-4740
Appears in Collections:CT - DEB - Artigos publicados em periódicos

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