ArticleAmerican journal of respiratory and critical care medicine2017
Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.
Article in American journal of respiratory and critical care medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.PloS one · 2021Trial
- From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution.ESMO real world data and digital oncology · 2026Article
- A Novel Electronic Medical Record Search Method to Identify Patients With Ketosis-Prone Diabetes: Implications for Discovery of Atypical Diabetes.Diabetes care · 2026Article
- 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
- Evolution, hotspots, and future directions of artificial intelligence in asthma research: a Web of Science-based bibliometric analysis [2016-2026].Journal of thoracic disease · 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
- Utilizing large language models and natural language processing to classify ischemia status from cardiac stress tests in a large multicenter healthcare system.BMC research notes · 2025Article
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.The Journal of allergy and clinical immunology · 2025Article
- Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Association of delayed asthma diagnosis with asthma exacerbations 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
- Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics.Journal of asthma and allergy · 2024Article
- Ascertaining asthma status in epidemiologic studies: a comparison between administrative health data and self-report.BMC medical research methodology · 2023Article
- Multicenter Validation of Natural Language Processing Algorithms for the Detection of Common Data Elements in Operative Notes for Total Hip Arthroplasty: Algorithm Development and Validation.JMIR medical informatics · 2022Article
- 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
- 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
- Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.The journal of allergy and clinical immunology. In practice · 2022Article
Corrections and comments
- Commented on by
Authors and funding
12 authors.
Funding
Abstract
rationaleDifficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.
objectivesWe evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).
methodsThe study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester. Performance of NLP-based asthma ascertainment using predetermined asthma criteria was assessed by determining both criterion validity (chart review of EMRs by abstractor as a gold standard) and construct validity (association with known risk factors for asthma, such as allergic rhinitis). MEASUREMENTS AND MAIN
resultsAfter excluding three subjects whose respiratory symptoms could be attributed to other conditions (e.g., tracheomalacia), among the remaining eligible 497 subjects, 51% were male, 77% white persons, and the median age at last follow-up date was 11.5 years. The asthma prevalence was 31% in the study cohort. Sensitivity, specificity, positive predictive value, and negative predictive value for NLP algorithm in predicting asthma status were 97%, 95%, 90%, and 98%, respectively. The risk factors for asthma (e.g., allergic rhinitis) that were identified either by NLP or the abstractor were the same.
conclusionsAsthma ascertainment through NLP should be considered in the era of EMRs because it can enable large-scale clinical studies in a more time-efficient manner and improve the recognition and care of childhood asthma in practice.
Indexed as
Identifiers
What OpenQuestion holds
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.