ArticleChina CDC weekly2025
Developing Machine Learning Models Based on Clinical Manifestations to Predict Influenza - Chongqing Municipality, China, 2022-2023.
Article in China CDC weekly, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
What is already known about this topic?: Current evidence regarding which clinical manifestations best predict influenza requires refinement, particularly considering regional variations in disease presentation and their importance for early diagnosis and surveillance. What is added by this report?: The optimal machine learning model identified key influenza predictors, including epidemiological characteristics, critical symptoms and signs, and age. Based on this model, we introduced a new influenza-like illness (ILI) definition characterized by fever (≥37.9 °C) with either cough or rhinorrhea. What are the implications for public health practice?: These findings provide evidence-based clinical manifestations for influenza prediction and offer an optimized definition of ILI for improved surveillance and early detection.
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Registered trials
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