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Updated: Jun 3, 2025
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Noninvasive Sampling of Mucosal Lining Fluid for the Quantification of In Vivo Upper Airway Immune-mediator Levels
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Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis
Daniil Lisik1, Saliha Selin Özuygur Ermis2, Gregorio Paolo Milani3,4
1Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden daniil.lisik@gu.se.
Summary
Childhood asthma and eczema follow consistent patterns, with prenatal smoke exposure being a key risk factor. Further research is needed to understand allergic multimorbidity and other allergy trajectories.
Area of Science:
- Pediatric Allergy and Immunology
- Computational Epidemiology
- Machine Learning in Healthcare
Background:
- Numerous studies have used machine learning to characterize childhood asthma and allergy trajectories.
- Existing research presents mixed findings due to diverse methodologies.
- This study aims to synthesize and critically evaluate the evidence on these trajectories.
Conclusions:
- Childhood asthma/wheezing and eczema exhibit consistent trajectories with identifiable risk factors like smoke exposure.
- Enhanced computational methodologies are required for better generalizability and validation of intermediate/transient trajectories.
- Further research should focus on allergic multimorbidity and trajectories of allergic rhinitis and food allergy.
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