ArticleJournal of the American Medical Informatics Association : JAMIA
Mining clinical text for signals of adverse drug-drug interactions.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
84 citing papers in PubMed, 2 syntheses or guidelines pooled it, 172 citations in OpenAlex.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence.Current oncology reports · 2026Review
- Causal knowledge graph analysis identifies adverse drug effects.Bioinformatics (Oxford, England) · 2026Article
- Cross-national pharmacovigilance of drug-induced intestinal obstruction: disproportionality signals in FAERS and external validation in JADER.Frontiers in pharmacology · 2026Article
- Post-Marketing Safety Concerns with Efgartigimod alfa: A Pharmacovigilance Analysis Based on the Food and Drug Administration Adverse Event Reporting System Database.Clinical epidemiology · 2025Article
- Computational and Experimental Investigation of Antidiabetic Drugs on Tofacitinib Metabolism: Molecular Docking, in vitro, and in vivo Studies.Drug design, development and therapy · 2025Article
- Drug-induced kidney stones: a real-world pharmacovigilance study using the FDA adverse event reporting system database.Frontiers in pharmacology · 2025Article
- Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.BMC medical research methodology · 2024Article
- Drug-drug interaction assessment based on a large-scale spontaneous reporting system for hepato- and renal-toxicity, and thrombocytopenia with concomitant low-dose methotrexate and analgesics use.BMC pharmacology & toxicology · 2024Article
- Detection of muscular system adverse reaction signals in sacubitril/valsartan treatment combined with statins.Frontiers in pharmacology · 2024Article
- Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review.Frontiers in drug safety and regulation · 2024Review
- Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.PloS one · 2024Article
- Progressive multifocal leukoencephalopathy reports in rheumatoid arthritis concerning different treatment patterns-an exploratory assessment using the food and drug administration adverse event reporting system.Frontiers in drug safety and regulation · 2024Article
- Safety assessment of cenobamate: real-world adverse event analysis from the FAERS database.Frontiers in pharmacology · 2024Article
- Article
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.Pharmacoepidemiology and drug safety · 2023Article
- An Early Adverse Drug Event Detection Approach with False Discovery Rate Control.medRxiv : the preprint server for health sciences · 2023Article
- Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature.Drug safety · 2023Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
BACKGROUND AND
objectiveElectronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.
methodsWe recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.
resultsOur method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus.
conclusionsIt is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.
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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.