ArticleThe journal of allergy and clinical immunology. In practice
Natural Language Processing for Asthma Ascertainment in Different Practice Settings.
Article in The journal of allergy and clinical immunology. In practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Effect of an electronic health record-integrated machine learning asthma risk marker on pediatrician prognostic accuracy during preschool age: a pilot randomized clinical trial.Scientific reports · 2026Trial
- 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
- 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
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- External validation and update of the pediatric asthma risk score as a passive digital marker for childhood asthma using integrated electronic health records.EClinicalMedicine · 2025Article
- Article
- 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
- Empowering Precision Medicine for Rare Diseases through Cloud Infrastructure Refactoring.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.JMIR research protocols · 2024Article
- Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics.Journal of asthma and allergy · 2024Article
- Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.The journal of allergy and clinical immunology. In practice · 2022Article
- Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Analyzing the Treatment of Patients with Acute Exacerbation of COPD with the Aid of Intelligent Diagnosis Method.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundWe developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic.
objectiveTo adapt NLP-PAC in a different health care setting, Sanford Children Hospital, by assessing its external validity.
methodsThe study was designed as a retrospective cohort study that used a random sample of 2011-2012 Sanford Birth cohort (n = 595). Manual chart review was performed on the cohort for asthma ascertainment on the basis of the PAC. We then used half of the cohort as a training cohort (n = 298) and the other half as a blind test cohort to evaluate the adapted NLP-PAC algorithm. Association of known asthma-related risk factors with the Sanford-NLP algorithm-driven asthma ascertainment was tested.
resultsAmong the eligible test cohort (n = 297), 160 (53%) were males, 268 (90%) white, and the median age was 2.3 years (range, 1.5-3.1 years). NLP-PAC, after adaptation, and the human abstractor identified 74 (25%) and 72 (24%) subjects, respectively, with 66 subjects identified by both approaches. Sensitivity, specificity, positive predictive value, and negative predictive value for the NLP algorithm in predicting asthma status were 92%, 96%, 89%, and 97%, respectively. The known risk factors for asthma identified by NLP (eg, smoking history) were similar to the ones identified by manual chart review.
conclusionsSuccessful implementation of NLP-PAC for asthma ascertainment in 2 different practice settings demonstrates the feasibility of automated asthma ascertainment leveraging electronic health record data with a potential to enable large-scale, multisite asthma studies to improve asthma care and research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.