ArticleAmerican journal of epidemiology2023
Improving Methods of Identifying Anaphylaxis for Medical Product Safety Surveillance Using Natural Language Processing and Machine Learning.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Identifying anaphylaxis using weakly-supervised prediction models and natural language processing.medRxiv : the preprint server for health sciences · 2026Article
- Predicting small molecule-RNA interactions without RNA tertiary structures.Nature biotechnology · 2026Article
- Strengthening inferential studies in the FDA Sentinel initiative: results from a methodological demonstration project.NPJ digital medicine · 2025Article
- Artificial intelligence for detecting anaphylaxis in electronic medical records.Asia Pacific allergy · 2025Article
- Practical Considerations for Variable Screening in the Super Learner.The New England Journal of Statistics in Data Science · 2025Article
- A future of data-rich pharmacoepidemiology studies: transitioning to large-scale linked electronic health record + claims data.American journal of epidemiology · 2025Article
- Scalable incident detection via natural language processing and probabilistic language models.Scientific reports · 2024Article
- Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.The Permanente journal · 2024Article
- A general framework for developing computable clinical phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.Current allergy and asthma reports · 2024Review
- An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data.Journal of clinical and translational science · 2023Article
- Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.Drug safety · 2022Review
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We sought to determine whether machine learning and natural language processing (NLP) applied to electronic medical records could improve performance of automated health-care claims-based algorithms to identify anaphylaxis events using data on 516 patients with outpatient, emergency department, or inpatient anaphylaxis diagnosis codes during 2015-2019 in 2 integrated health-care institutions in the Northwest United States. We used one site's manually reviewed gold-standard outcomes data for model development and the other's for external validation based on cross-validated area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and sensitivity. In the development site 154 (64%) of 239 potential events met adjudication criteria for anaphylaxis compared with 180 (65%) of 277 in the validation site. Logistic regression models using only structured claims data achieved a cross-validated AUC of 0.58 (95% CI: 0.54, 0.63). Machine learning improved cross-validated AUC to 0.62 (0.58, 0.66); incorporating NLP-derived covariates further increased cross-validated AUCs to 0.70 (0.66, 0.75) in development and 0.67 (0.63, 0.71) in external validation data. A classification threshold with cross-validated PPV of 79% and cross-validated sensitivity of 66% in development data had cross-validated PPV of 78% and cross-validated sensitivity of 56% in external data. Machine learning and NLP-derived data improved identification of validated anaphylaxis events.
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