Author | Lopez, Diego Montenegro | |
Author | Mello, Flávio Luis de | |
Author | Dias, Cristina Maria Giordano | |
Author | Almeida, Paula | |
Author | Araújo, Milton | |
Author | Magalhaes, Monica Avelar | |
Author | Gazeta, Gilberto Salles | |
Author | Brasil, Reginaldo Peçanha | |
Access date | 2018-02-12T16:12:35Z | |
Available date | 2018-02-12T16:12:35Z | |
Document date | 2017 | |
Citation | LOPEZ, Diego Montenegro; et al. Evaluating the surveillance system for spotted fever in Brazil using machine-learning techniques. Frontiers in Public Health, v.5, Article 323, 9p, Nov. 2017. | pt_BR |
ISSN | 2296-2565 | pt_BR |
URI | https://www.arca.fiocruz.br/handle/icict/24809 | |
Language | eng | pt_BR |
Publisher | Frontiers Media | pt_BR |
Rights | open access | pt_BR |
Subject in Portuguese | Saúde pública | pt_BR |
Subject in Portuguese | Epidemiologia | pt_BR |
Subject in Portuguese | Febre manchada | pt_BR |
Subject in Portuguese | aprendizagem mecânica | pt_BR |
Subject in Portuguese | redes neurais probabilísticas | pt_BR |
Subject in Portuguese | Decisão | pt_BR |
Title | Evaluating the surveillance system for spotted fever in Brazil using machine-learning techniques | pt_BR |
Type | Article | |
Abstract | This work analyses the performance of the Brazilian spotted fever (SF) surveillance system in diagnosing and confirming suspected cases in the state of Rio de Janeiro (RJ), from 2007 to 2016 (July) using machine-learning techniques. Of the 890 cases reported to the Disease Notification Information System (SINAN), 11.7% were confirmed as SF, 2.9% as dengue, 1.6% as leptospirosis, and 0.7% as tick bite allergy, with the remainder being diagnosed as other categories (10.5%) or unspecified (72.7%). This study confirms the existence of obstacles in the diagnostic classification of suspected cases of SF by clinical signs and symptoms. Unlike man–capybara contact (1.7% of cases), man–tick contact (71.2%) represents an important risk indicator for SF. The analysis of decision trees highlights some clinical symptoms related to SF patient death or cure, such as: respiratory distress, convulsion, shock, petechiae, coma, icterus, and diarrhea. Moreover, cartographic techniques document patient transit between RJ and bordering states and within RJ itself. This work recommends some changes to SINAN that would provide a greater understanding of the dynamics of SF and serve as a model for other endemic areas in Brazil. | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Doenças Parasitárias. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Referência Nacional em vetores das Riquetsioses. Rio de Janeiro, RJ, Brasil. | pt_BR |
Affilliation | Universidade Federal do Rio de Janeiro. Departamento de Engenharia Eletrônica e de Computadores. Rio de Janeiro, RJ, Brasil. | pt_BR |
Affilliation | Secretaria de Estado de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil | pt_BR |
Affilliation | Secretaria de Estado de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil | pt_BR |
Affilliation | Secretaria de Estado de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnologia emSaúde. Rio de Janeiro, RJ, Brasil. | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Referência Nacional em vetores das Riquetsioses. Rio de Janeiro, RJ, Brasil. | pt_BR |
Affilliation | Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Doenças Parasitárias. Rio de Janeiro, RJ, Brasil. | pt_BR |
Subject | public health | pt_BR |
Subject | epidemiology | pt_BR |
Subject | spotted fever | pt_BR |
Subject | machine-learning | pt_BR |
Subject | probabilistic neural networks | pt_BR |
Subject | decision trees | pt_BR |