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Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques

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 S...

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Autores principales: Lopez, Diego Montenegro, de Mello, Flávio Luis, Giordano Dias, Cristina Maria, Almeida, Paula, Araújo, Milton, Magalhães, Monica Avelar, Gazeta, Gilberto Salles, Brasil, Reginaldo Peçanha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714864/
https://www.ncbi.nlm.nih.gov/pubmed/29250519
http://dx.doi.org/10.3389/fpubh.2017.00323
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author Lopez, Diego Montenegro
de Mello, Flávio Luis
Giordano Dias, Cristina Maria
Almeida, Paula
Araújo, Milton
Magalhães, Monica Avelar
Gazeta, Gilberto Salles
Brasil, Reginaldo Peçanha
author_facet Lopez, Diego Montenegro
de Mello, Flávio Luis
Giordano Dias, Cristina Maria
Almeida, Paula
Araújo, Milton
Magalhães, Monica Avelar
Gazeta, Gilberto Salles
Brasil, Reginaldo Peçanha
author_sort Lopez, Diego Montenegro
collection PubMed
description 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.
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spelling pubmed-57148642017-12-15 Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques Lopez, Diego Montenegro de Mello, Flávio Luis Giordano Dias, Cristina Maria Almeida, Paula Araújo, Milton Magalhães, Monica Avelar Gazeta, Gilberto Salles Brasil, Reginaldo Peçanha Front Public Health Public Health 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. Frontiers Media S.A. 2017-11-30 /pmc/articles/PMC5714864/ /pubmed/29250519 http://dx.doi.org/10.3389/fpubh.2017.00323 Text en Copyright © 2017 Lopez, de Mello, Giordano Dias, Almeida, Araújo, Magalhães, Gazeta and Brasil. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Lopez, Diego Montenegro
de Mello, Flávio Luis
Giordano Dias, Cristina Maria
Almeida, Paula
Araújo, Milton
Magalhães, Monica Avelar
Gazeta, Gilberto Salles
Brasil, Reginaldo Peçanha
Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques
title Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques
title_full Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques
title_fullStr Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques
title_full_unstemmed Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques
title_short Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques
title_sort evaluating the surveillance system for spotted fever in brazil using machine-learning techniques
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714864/
https://www.ncbi.nlm.nih.gov/pubmed/29250519
http://dx.doi.org/10.3389/fpubh.2017.00323
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