Evidence map›Paper›PMID 41857633›Full record

ArticleTropical medicine and health2026

Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.

Gabriel Masson, Kaio Viana, Sebastião Rogerio, Kayo Henrique de C Monteiro, Jamile Taniele-Silva, Gabriela Cavalcanti Lima Albuquerque, Moacyr Jesus Barreto de Melo Rêgo, André Machado de Siqueira, Raphael Dourado, Patricia Takako Endo

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Article in Tropical medicine and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Gabriel MassonPrograma de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, PE, Brazil.
Kaio VianaPrograma de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, PE, Brazil.
Sebastião RogerioCentro Universitário Favip Wyden, Caruaru, PE, Brazil.
Kayo Henrique de C MonteiroPrograma de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, PE, Brazil.
Jamile Taniele-SilvaPrograma de Pós-Graduação em Inovação Terapêutica, Universidade Federal de Pernambuco, Recife, PE, Brazil.
Gabriela Cavalcanti Lima AlbuquerquePrograma de Pós-Graduação em Inovação Terapêutica, Universidade Federal de Pernambuco, Recife, PE, Brazil.
Moacyr Jesus Barreto de Melo RêgoPrograma de Pós-Graduação em Inovação Terapêutica, Universidade Federal de Pernambuco, Recife, PE, Brazil.
André Machado de SiqueiraPrograma de Pós-Graduação em Inovação Terapêutica, Universidade Federal de Pernambuco, Recife, PE, Brazil.
Raphael DouradoPrograma de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, PE, Brazil.
Patricia Takako EndoPrograma de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, PE, Brazil. patricia.endo@upe.br.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to progress from the acute to the chronic phase, marked by the prevalence of symptoms of arthralgia. Joint pain compromises the performance of daily activities, including psychological, economic, and physical functioning.

methodsThrough the use of data science techniques, such as data analysis, evaluation, and visualization, the aim is to understand the influence of pain points on disease progression. Furthermore, we also evaluate artificial intelligence models to calculate the likelihood of patients progressing to a chronic phase.

resultsThe data analysis showed that arthralgia was reported by 97.70% of the sample (339 cases), followed by 74.06% edema (257 cases), 36.31% low back pain (126 cases) and 34.58% myalgia (120 cases), being factors that are related to chronicity. The artificial intelligence models have achieved metrics above 60%, demonstrating potential for estimating the likelihood of a patient's progression to the chronic phase.

conclusionsBased on these estimates, healthcare professionals can adopt preventive measures capable of mitigating the disease's impacts. Implementing these models in the decision-making process becomes an important ally in the fight against Chikungunya in Brazil, helping to mitigate the social and economic impacts caused by the chronic phase.

Indexed as

ArbovirusChikungunyaChronic phaseJoint painMachine learning

Identifiers

PMID41857633
PMCPMC13045035

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LicenceCC BY-NC-ND
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