ArticleScientific data2018
A dataset of 200 structured product labels annotated for adverse drug reactions.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels.Drug safety · 2026Article
- Potential Drug Dose-Specific Adverse Three-Drug Combinations: A US Insurance Claims Data-Based Study.Pharmacoepidemiology and drug safety · 2025Article
- Article
- OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models.Med (New York, N.Y.) · 2025Article
- DrugCombo: an informatics bridge for anticancer drug combination Phase I trial design.Database : the journal of biological databases and curation · 2025Article
- Establishing a Pharmacoinformatics Repository of Approved Medicines: A Database to Support Drug Product Development.Molecular pharmaceutics · 2025Article
- PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs.Nature communications · 2023Article
- An Early Adverse Drug Event Detection Approach with False Discovery Rate Control.medRxiv : the preprint server for health sciences · 2023Article
- Edge Weight Updating Neural Network for Named Entity Normalization.Neural processing letters · 2022Article
- Hierarchy-aware Adverse Reaction Embeddings for Signal Detection.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Mapping Compound Databases to Disease Maps-A MINERVA Plugin for CandActBase.Journal of personalized medicine · 2021Article
- Drug Safety Data Curation and Modeling in ChEMBL: Boxed Warnings and Withdrawn Drugs.Chemical research in toxicology · 2021Article
- BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk.Frontiers in artificial intelligence · 2021Article
- Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels.BMC bioinformatics · 2019Article
- MINERVA API and plugins: opening molecular network analysis and visualization to the community.Bioinformatics (Oxford, England) · 2019Article
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA.BMC bioinformatics · 2019Article
- A reference set of curated biomedical data and metadata from clinical case reports.Scientific data · 2018Article
- Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.Journal of biomedical semantics · 2018Article
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Authors and funding
8 authors.
Funding
Abstract
Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars.
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