ArticleThe journal of allergy and clinical immunology. In practice2020
Natural Language Processing Combined with ICD-9-CM Codes as a Novel Method to Study the Epidemiology of Allergic Drug Reactions.
Article in The journal of allergy and clinical immunology. In practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- The Use of Electronic Health Records to Study Drug-Induced Hypersensitivity Reactions from 2000 to 2021: A Systematic Review.Immunology and allergy clinics of North America · 2022Pooled it
- Assessing real-world natural history of indolent systemic mastocytosis: A retrospective matched cohort study.The journal of allergy and clinical immunology. Global · 2026Article
- Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study.Journal of medical Internet research · 2025Article
- Scalable incident detection via natural language processing and probabilistic language models.Scientific reports · 2024Article
- Examining cefazolin utilization and perioperative anaphylaxis in patients with and without a penicillin allergy label: A cross-sectional study.Journal of clinical anesthesia · 2024Article
- Applying Deep Learning Model to Predict Diagnosis Code of Medical Records.Diagnostics (Basel, Switzerland) · 2023Article
- Deep-ADCA: Development and Validation of Deep Learning Model for Automated Diagnosis Code Assignment Using Clinical Notes in Electronic Medical Records.Journal of personalized medicine · 2022Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
- The applications of eHealth technologies in the management of asthma and allergic diseases.Clinical and translational allergy · 2021Review
- Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science.Yearbook of medical informatics · 2020Review
- Challenges and opportunities for public health made possible by advances in natural language processing.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAllergic drug reaction epidemiologic data are sparse because it remains difficult to identify true cases in large data sets using manual chart review.
objectiveTo develop and validate a novel informatics method based on natural language processing (NLP) in combination with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes that identifies allergic drug reactions in the electronic health record.
methodsPreviously studied and high-yield ICD-9-CM codes were used to screen for possible allergic drug reactions among all inpatients admitted in 2007 and 2008. A random sample was selected for manual chart review to identify true cases of allergic drug reactions. A rule-based NLP algorithm was then developed to identify allergic drug reactions using free-text clinical notes and discharge summaries from the filtered cases. The performance of using manual chart review of ICD-9-CM codes alone was compared with ICD-9-CM codes in combination with NLP.
resultsOf 3907 cases identified by ICD-9-CM codes, 725 (19%) were randomly selected for manual chart review; 335 were confirmed as allergic drug reactions, resulting in a positive predictive value (PPV) of 46% (range: 18%-79%) when using ICD-9-CM codes alone. Our NLP algorithm in combination with ICD-9-CM codes achieved a PPV of 86% (range: 69%-100%). Among the 335 confirmed positive cases, NLP identified 259 true cases, resulting in a recall/sensitivity of 77% (range: 26%-100%). Among the 390 negative cases, NLP achieved a specificity of 89% (range: 69%-100%).
conclusionUsing NLP with ICD-9-CM codes improved identification of allergic drug reactions. The resulting decrease in manual chart review effort will facilitate large epidemiology studies of this understudied area.
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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.