ArticleThe Journal of allergy and clinical immunology2018
Ascertainment of asthma prognosis using natural language processing from electronic medical records.
Article in The Journal of allergy and clinical immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- 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
- Article
- 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
- Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.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
- Identification of asthma control factor in clinical notes using a hybrid deep learning model.BMC medical informatics and decision making · 2021Article
- Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique.BMC medical informatics and decision making · 2021Article
- A diagnostic codes-based algorithm improves accuracy for identification of childhood asthma in archival data sets.The Journal of asthma : official journal of the Association for the Care of Asthma · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.The Journal of allergy and clinical immunology · 2020Review
- Expert artificial intelligence-based natural language processing characterises childhood asthma.BMJ open respiratory research · 2020Article
- Deep Learning Identification of Asthma Inhaler Techniques in Clinical Notes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2020Article
- Early Identification of Childhood Asthma: The Role of Informatics in an Era of Electronic Health Records.Frontiers in pediatrics · 2019Review
- Pragmatic considerations for fostering reproducible research in artificial intelligence.NPJ digital medicine · 2019Review
- Modeling asynchronous event sequences with RNNs.Journal of biomedical informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
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.