ArticleJournal of the American Medical Informatics Association : JAMIA2020
Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- The Evaluation of Transformer Models for the Detection of Adverse Drug Events: A Benchmark Study Using Dutch Free-Text Documents of Hospitalized Patients.Drug safety · 2026Article
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Safety profile of faricimab: a multi-source pharmacovigilance analysis using FAERS and JADER.BMC pharmacology & toxicology · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- LEVERAGING UNSTRUCTURED DATA IN ELECTRONIC HEALTH RECORDS TO DETECT ADVERSE EVENTS FROM PEDIATRIC DRUG USE - A SCOPING REVIEW.medRxiv : the preprint server for health sciences · 2025Article
- Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.JCO clinical cancer informatics · 2024Article
- Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
- Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.Drug safety · 2022Article
- Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.Interventional cardiology (London, England) · 2021Review
- Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data.JAMIA open · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveReal-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claims data, among children treated with drugs for pulmonary hypertension. MATERIALS AND
methodsTextual mentions of medications and signs/symptoms that may represent ADEs were identified in clinical notes using natural language processing. Diagnostic codes for the same signs/symptoms were identified in our electronic data warehouse for the patients with textual evidence of taking pulmonary hypertension-targeted drugs. We compared rates of ADEs identified in clinical notes to those identified from diagnostic code data. In addition, we compared putative ADE rates from clinical notes to those from a healthcare claims dataset from a large, national insurer.
resultsAnalysis of clinical notes identified up to 7-fold higher ADE rates than those ascertained from diagnostic codes. However, certain ADEs (eg, hearing loss) were more often identified in diagnostic code data. Similar results were found when ADE rates ascertained from clinical notes and national claims data were compared. DISCUSSION: While administrative claims and clinical notes are both increasingly used for RWD-based pharmacovigilance, ADE rates substantially differ depending on data source.
conclusionPharmacovigilance based on RWD may lead to discrepant results depending on the data source analyzed. Further work is needed to confirm the validity of identified ADEs, to distinguish them from disease effects, and to understand tradeoffs in sensitivity and specificity between data sources.
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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.