ArticleViruses2024
Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.
Article in Viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Development and validation of an interpretable machine learning model for early hospital-based differentiation of chikungunya and dengue fever using routine clinical data.BMC infectious diseases · 2026Observational
- Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.Tropical medicine and health · 2026Article
- AI-Assisted Differentiation of Dengue and Chikungunya Using Big, Imbalanced Epidemiological Data.Tropical medicine and infectious disease · 2026Article
- Article
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3 authors.
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Abstract
Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodological framework that transforms qualitative information into quantitative information, establishing differential weights in relation to symptoms according to the medical evidence and the GRADE scale based on recommendation 1 of the said guide. To achieve this, common variables from the dataset were identified using the PAHO guide, and quality rules were established. A linear interpolation function was then parameterised to assign weights to the symptoms according to the evidence. Machine learning was used to compare the different models, achieving 99% accuracy compared with 79% without the methodology. This proposal represents a significant advancement, allowing the direct application of the PAHO recommendations to the dataset and improving the differential classification of arboviruses.
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