ArticleThe Journal of allergy and clinical immunology2025
Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.
Article in The Journal of allergy and clinical immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Epidemiological characteristics of influenza A virus infection across age groups: a longitudinal analysis based on gender and clinical diagnosis.Journal of translational medicine · 2025Article
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Authors and funding
23 authors.
Funding
Abstract
backgroundAsthma is associated with an increased risk of acute respiratory infections (ARI). Little is known about whether natural language processing (NLP)-powered digital biomarkers can identify a high-risk asthma subgroup for ARI during early childhood.
objectiveWe assessed whether a digital biomarker could identify a high-risk subgroup of childhood asthma for ARI.
methodsWe applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) and Asthma Predictive Index (NLP-API) to electronic health records of the 1997-2016 Mayo Clinic Birth Cohort. We categorized the cohort into 4 subgroups: both criteria positive (NLP
resultsThere were 22,370 eligible subjects (51% male and 81% White). The NLP
conclusionNLP
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