Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach

Detalhes bibliográficos
Autor(a) principal: Hair, Gleicy Macedo
Data de Publicação: 2019
Outros Autores: Nobre, Flávio Fonseca, Brasil, Patrícia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/34689
Resumo: Universidade Federal do Rio de Janeiro. Centro de Tecnologia. COPPE. Programa de Engenharia Biomédica. Laboratório de Engenharia em Sistemas de Saúde. Rio de Janeiro, RJ, Brasil.
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spelling Hair, Gleicy MacedoNobre, Flávio FonsecaBrasil, Patrícia2019-08-09T13:58:15Z2019-08-09T13:58:15Z2019HAIR, Gleicy Macedo; NOBRE, Flávio Fonseca; BRASIL, Patrícia. Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach. BMC Infectious Diseases, v. 19, p. 1-11, 2019.1471-2334https://www.arca.fiocruz.br/handle/icict/3468910.1186/s12879-019-4282-y1471-2334engBMCCharacterization of clinical patterns of dengue patients using an unsupervised machine learning approachinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleUniversidade Federal do Rio de Janeiro. Centro de Tecnologia. COPPE. Programa de Engenharia Biomédica. Laboratório de Engenharia em Sistemas de Saúde. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Centro de Tecnologia. COPPE. Programa de Engenharia Biomédica. Laboratório de Engenharia em Sistemas de Saúde. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Doenças Febris Agudas. Rio de Janeiro, RJ, Brasil.Background: Despite the greater sensitivity of the new dengue clinical classification proposed by the World Health Organization (WHO) in 2009, there is a need for a better definition of warning signs and clinical progression of dengue cases. Classic statistical methods have been used to evaluate risk criteria in dengue patients, however they usually cannot access the complexity of dengue clinical profiles. We propose the use of machine learning as an alternative tool to identify the possible characteristics that could be used to develop a risk criterion for severity in dengue patients. Method: In this study, we analyzed the clinical profiles of 523 confirmed dengue cases using self-organizing maps (SOM) and random forest algorithms to identify clusters of patients with similar patterns. Results: We identified four natural clusters, two with features of dengue without warning signs or mild disease, one that comprises the severe dengue cases and high frequency of warning signs, and another with intermediate characteristics. Age appeared as the key variable for splitting the data into these four clusters although warning signs such as abdominal pain or tenderness, clinical fluid accumulation, mucosal bleeding, lethargy, restlessness, liver enlargement and increased hematocrit associated with a decrease in platelet counts should also be considered to evaluate severity in dengue patients. Conclusions: These findings suggest that age must be the first characteristic to be considered in places where dengue is hyperendemic. Our results show that warning signs should be closely monitored, mainly in children. Further studies exploring these results in a longitudinal approach may help to understand the full spectrum of dengue clinical manifestations.DengueAgeClinical classificationWarning signsMachine learninginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-83104https://www.arca.fiocruz.br/bitstream/icict/34689/1/license.txt79178e5f2a0eb066867a274556814938MD51ORIGINALve_Hair_Gleicy_etal_INI_2019.pdfve_Hair_Gleicy_etal_INI_2019.pdfapplication/pdf777766https://www.arca.fiocruz.br/bitstream/icict/34689/2/ve_Hair_Gleicy_etal_INI_2019.pdf4f3808372108d551233c01464a74ccacMD52TEXTve_Hair_Gleicy_etal_INI_2019.pdf.txtve_Hair_Gleicy_etal_INI_2019.pdf.txtExtracted texttext/plain48318https://www.arca.fiocruz.br/bitstream/icict/34689/3/ve_Hair_Gleicy_etal_INI_2019.pdf.txtd871cab0c25b721a2307961802c66c75MD53icict/346892019-08-10 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dc.title.pt_BR.fl_str_mv Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
title Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
spellingShingle Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
Hair, Gleicy Macedo
Dengue
Age
Clinical classification
Warning signs
Machine learning
title_short Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
title_full Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
title_fullStr Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
title_full_unstemmed Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
title_sort Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach
author Hair, Gleicy Macedo
author_facet Hair, Gleicy Macedo
Nobre, Flávio Fonseca
Brasil, Patrícia
author_role author
author2 Nobre, Flávio Fonseca
Brasil, Patrícia
author2_role author
author
dc.contributor.author.fl_str_mv Hair, Gleicy Macedo
Nobre, Flávio Fonseca
Brasil, Patrícia
dc.subject.en.pt_BR.fl_str_mv Dengue
Age
Clinical classification
Warning signs
Machine learning
topic Dengue
Age
Clinical classification
Warning signs
Machine learning
description Universidade Federal do Rio de Janeiro. Centro de Tecnologia. COPPE. Programa de Engenharia Biomédica. Laboratório de Engenharia em Sistemas de Saúde. Rio de Janeiro, RJ, Brasil.
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-08-09T13:58:15Z
dc.date.available.fl_str_mv 2019-08-09T13:58:15Z
dc.date.issued.fl_str_mv 2019
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 HAIR, Gleicy Macedo; NOBRE, Flávio Fonseca; BRASIL, Patrícia. Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach. BMC Infectious Diseases, v. 19, p. 1-11, 2019.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/34689
dc.identifier.issn.pt_BR.fl_str_mv 1471-2334
dc.identifier.doi.none.fl_str_mv 10.1186/s12879-019-4282-y
dc.identifier.eissn.none.fl_str_mv 1471-2334
identifier_str_mv HAIR, Gleicy Macedo; NOBRE, Flávio Fonseca; BRASIL, Patrícia. Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach. BMC Infectious Diseases, v. 19, p. 1-11, 2019.
1471-2334
10.1186/s12879-019-4282-y
url https://www.arca.fiocruz.br/handle/icict/34689
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/34689/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/34689/2/ve_Hair_Gleicy_etal_INI_2019.pdf
https://www.arca.fiocruz.br/bitstream/icict/34689/3/ve_Hair_Gleicy_etal_INI_2019.pdf.txt
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