ArticleDrug safety2016
Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.
Article in Drug safety, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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
24 citing papers in PubMed.
- Drug-drug interactions between sex hormones and analgesics linked to thromboembolic events: an FDA adverse event reporting system analysis.Frontiers in pharmacology · 2026Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.Journal of cheminformatics · 2022Article
- Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.Frontiers in endocrinology · 2022Article
- Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.PLoS computational biology · 2021Article
- Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.Journal of biomedical informatics · 2021Article
- Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.BMC medical informatics and decision making · 2021Article
- High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels.BMC bioinformatics · 2019Article
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- Review of Statistical Methodologies for Detecting Drug-Drug Interactions Using Spontaneous Reporting Systems.Frontiers in pharmacology · 2019Review
- Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases.BMC bioinformatics · 2018Article
- Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Briefings in bioinformatics · 2018Review
- Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Characteristics of drugs safety signals that predict safety related product information update.Pharmacoepidemiology and drug safety · 2018Article
- Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions.P & T : a peer-reviewed journal for formulary management · 2018Article
- Review
- Drug-drug interaction discovery and demystification using Semantic Web technologies.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
objectiveSeveral studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs).
methodsWe prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.
resultsWe collected information for 5983 putative EHR-derived drug-drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug-drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.
conclusionsOur proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.
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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.