ArticleBMJ open respiratory research2020
Expert artificial intelligence-based natural language processing characterises childhood asthma.
Article in BMJ open respiratory research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.PloS one · 2021Trial
- 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
- Feasibility and acceptability of a passive digital marker for childhood asthma risk prognosis: a pilot cross-sectional study.Pilot and feasibility studies · 2026Article
- The evolution of scientific knowledge in childhood asthma over time.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Application of a natural language processing algorithm to early asthma ascertainment for adults in the era of electronic health records.The journal of allergy and clinical immunology. Global · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.The Journal of allergy and clinical immunology · 2025Article
- Hyperspectral Imaging for Foreign Matter Detection in Foods: Advances, Challenges, and Future Directions.Foods (Basel, Switzerland) · 2025Review
- Association of delayed asthma diagnosis with asthma exacerbations in children.The journal of allergy and clinical immunology. Global · 2025Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Sex differences in sociodemographic, clinical, and laboratory variables in childhood asthma: A birth cohort study.Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology · 2024Article
- Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review.JMIR AI · 2024Article
- The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.Current allergy and asthma reports · 2024Review
- Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment.The journal of allergy and clinical immunology. Global · 2024Article
- Rural Health Disparities in Allergy, Asthma, and Immunologic Diseases: The Current State and Future Direction for Clinical Care and Research.The journal of allergy and clinical immunology. In practice · 2024Review
- Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.JMIR formative research · 2024Article
- Gross motor function prediction using natural language processing in cerebral palsy.Developmental medicine and child neurology · 2023Article
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.The journal of allergy and clinical immunology. In practice · 2022Article
- Risk of pneumonia in asthmatic children using inhaled corticosteroids: a nested case-control study in a birth cohort.BMJ open · 2022Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 3 countries.
Funding
Abstract
introductionThe lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics.
methodsUsing the 1997-2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC
resultsOf the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC
conclusionExpert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.
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