ArticleThe journal of allergy and clinical immunology. Global2024
Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment.
Article in The journal of allergy and clinical immunology. Global, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 10 citations in OpenAlex.
- Temporal windowing of recurrent sinusitis improves EHR-based immunodeficiency classification.Journal of human immunity · 2026Article
- Implementing calculated globulin screening for immunodeficiencies: The benefits and challenges.Journal of human immunity · 2026Review
- Retrieval-Augmented Language Models for Clinical Decision Support in the Classification of Inborn Errors of Immunity.Journal of clinical immunology · 2026Article
- Large language models for structured cardiovascular data extraction: a foundation for scalable research and clinical applications.European heart journal. Digital health · 2026Article
- Proceedings of the second Artificial Intelligence in Primary Immunodeficiency (AIPI) meeting.The Journal of allergy and clinical immunology · 2026Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Identifying long-term conditions in New Zealand general practice using structured and unstructured data: a cross-sectional study.BMJ health & care informatics · 2025Article
- New tools for diagnosis of primary immunodeficiencies: from awareness to artificial intelligence.Frontiers in immunology · 2025Review
- Advancing equity in allergy and immunology: progress, pitfalls, and the path forward.Frontiers in allergy · 2025Review
- Navigating disruption in the PID landscape: embracing opportunities and anticipating threats in the next ten years.Frontiers in immunology · 2025Review
- Building alliances for early detection of immunodeficiencies: from primary care to hematology.Frontiers in immunology · 2025Review
- Piloting an automated query and scoring system to facilitate APDS patient identification from health systems.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 1 country.
Funding
Abstract
Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center's electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve > 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection.
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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.