ArticleJournal of the American Medical Informatics Association : JAMIA
Using information mining of the medical literature to improve drug safety.
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 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of literature reports on drug safety signals.Wiener klinische Wochenschrift · 2021Pooled it
- Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach.Bioinformatics and biology insights · 2020Article
- Inferring new relations between medical entities using literature curated term co-occurrences.JAMIA open · 2019Article
- Article
- Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese.JMIR medical informatics · 2018Article
- Markov Logic Networks for Adverse Drug Event Extraction from Text.Knowledge and information systems · 2017Article
- Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing.Applied clinical informatics · 2017Article
- Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature.BMC bioinformatics · 2016Article
- A curated and standardized adverse drug event resource to accelerate drug safety research.Scientific data · 2016Article
- Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data.Journal of biomedical informatics · 2016Article
- Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.Drug safety · 2016Article
- Automated Summarization of Publications Associated with Adverse Drug Reactions from PubMed.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- A crowdsourcing workflow for extracting chemical-induced disease relations from free text.Database : the journal of biological databases and curation · 2016Article
- Leveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies.Journal of biomedical informatics · 2015Article
- Computational approaches for pharmacovigilance signal detection: toward integrated and semantically-enriched frameworks.Drug safety · 2015Article
- Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles.Journal of biomedical informatics · 2015Article
- Portable automatic text classification for adverse drug reaction detection via multi-corpus training.Journal of biomedical informatics · 2015Article
- Analyzing search behavior of healthcare professionals for drug safety surveillance.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Automated Determination of Publications Related to Adverse Drug Reactions in PubMed.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePrescription drugs can be associated with adverse effects (AEs) that are unrecognized despite evidence in the medical literature, as shown by rofecoxib's late recall in 2004. We assessed whether applying information mining to PubMed could reveal major drug-AE associations if articles testing whether drugs cause AEs are over-represented in the literature.
designMEDLINE citations published between 1949 and September 2009 were retrieved if they mentioned one of 38 drugs and one of 55 AEs. A statistical document classifier (using MeSH index terms) was constructed to remove irrelevant articles unlikely to test whether a drug caused an AE. The remaining relevant articles were analyzed using a disproportionality analysis that identified drug-AE associations (signals of disproportionate reporting) using step-up procedures developed to control the familywise type I error rate. MEASUREMENTS: Sensitivity and positive predictive value (PPV) for empirical drug-AE associations as judged against drug-AE associations subject to FDA warnings.
resultsIn testing, the statistical document classifier identified relevant articles with 81% sensitivity and 87% PPV. Using data filtered by the statistical document classifier, base-case models showed 64.9% sensitivity and 42.4% PPV for detecting FDA warnings. Base-case models discovered 54% of all detected FDA warnings using literature published before warnings. For example, the rofecoxib-heart disease association was evident using literature published before 2002. Analyses incorporating literature mentioning AEs common to the drug class of interest yielded 71.4% sensitivity and 40.7% PPV.
conclusionsResults from large-scale literature retrieval and analysis (literature mining) compared favorably with and could complement current drug safety methods.
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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.