ArticleBMC pulmonary medicine2018
Automated chart review utilizing natural language processing algorithm for asthma predictive index.
Article in BMC pulmonary medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled 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
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 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
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.The Journal of allergy and clinical immunology · 2025Article
- OphthoACR (Ophthalmology Automated Chart Review): An AI-Powered Tool for Complete Automation of Ophthalmology Chart Reviews and Cohort Data Analysis.Translational vision science & technology · 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
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Artificial intelligence and wheezing in children: where are we now?Frontiers in medicine · 2024Review
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Ascertaining asthma status in epidemiologic studies: a comparison between administrative health data and self-report.BMC medical research methodology · 2023Article
- Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.Clinical and translational science · 2023Article
- Gross motor function prediction using natural language processing in cerebral palsy.Developmental medicine and child neurology · 2023Article
- Pneumonia and Pulmonary Thromboembolism Classification Using Electronic Health Records.Diagnostics (Basel, Switzerland) · 2022Article
- An ensemble approach for healthcare application and diagnosis using natural language processing.Cognitive neurodynamics · 2022Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundThus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.
methodsThis is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma.
resultsAmong the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6-6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8-10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively.
conclusionNLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.
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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.