ReviewThe Journal of allergy and clinical immunology2020
Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.
Review in The Journal of allergy and clinical immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 6 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
109 citing papers in PubMed, 6 syntheses or guidelines pooled it, 279 citations in OpenAlex.
- Synergistic review of automation impact of big data, AI, and ML in current data transformative era.F1000Research · 2025Pooled it
- Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.Journal of medical Internet research · 2024Pooled it
- Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.Expert review of pharmacoeconomics & outcomes research · 2024Pooled it
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis.PloS one · 2022Pooled it
- Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews.International journal of medical informatics · 2021Pooled it
- Can Artificial Intelligence Substitute Dental Public Health Expertise? A Competency-Based Analysis Across Core DPH Domains.Journal of public health dentistry · 2026Article
- Development and external validation of a text-matching algorithm to identify patients with lower extremity deep venous thrombosis from radiology reports at five medical centers.Thrombosis research · 2026Article
- Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management.Current allergy and asthma reports · 2026Review
- Identifying anaphylaxis using weakly-supervised prediction models and natural language processing.medRxiv : the preprint server for health sciences · 2026Article
- Using Natural Language Processing to Improve Fall Documentation in VA Nursing Home Residents.Journal of the American Medical Directors Association · 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
- NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing.BMJ open ophthalmology · 2026Observational
- 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 in cardiovascular medicine: prevention, diagnosis, and intervention.Frontiers in artificial intelligence · 2026Review
- Identification and validation of respiratory virus immunization using natural language processing.Frontiers in digital health · 2026Article
- Multiple Large Language Models' Performance on the Chinese Medical Licensing Examination: Quantitative Comparative Study.JMIR human factors · 2025Article
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.The Journal of allergy and clinical immunology · 2025Article
- Reframing EHR Policy in China: Towards Balanced Implementation and Ethical Foundations.Journal of evaluation in clinical practice · 2025Article
- A case study on barriers to the research implementation of a novel technology in an academic medical center.PLOS digital health · 2025Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. As a large amount of valuable clinical information is locked in clinical narratives, natural language processing techniques as an artificial intelligence approach have been leveraged to extract information from clinical narratives in electronic health records. This capability of natural language processing potentially enables automated chart review for identifying patients with distinctive clinical characteristics in clinical care and reduces methodological heterogeneity in defining phenotype, obscuring biological heterogeneity in research concerning allergy, asthma, and immunology. This brief review discusses the current literature on the secondary use of electronic health record data for clinical research concerning allergy, asthma, and immunology and highlights the potential, challenges, and implications of natural language processing techniques.
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.