ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Extracting Adverse Drug Events from Clinical Notes.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A Large Language Model for Extracting Post-marketing Adverse Drug Events from Clinical Notes in the Electronic Health Record.Drug safety · 2026Article
- 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
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Enhanced Adverse-Event Detection and Drug-Event Relation Extraction from Clinical Notes.medRxiv : the preprint server for health sciences · 2026Article
- Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.JMIR formative research · 2026Article
- Portability of a text mining algorithm for detecting adverse drug reactions in electronic health records across diverse patient groups in two Dutch hospitals.PLOS digital health · 2026Article
- Article
- Efficient extraction of medication information from clinical notes: an evaluation in 2 languages.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach.JMIR medical informatics · 2025Article
- Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.Scientific reports · 2025Article
- Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.Scientific reports · 2025Observational
- Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records.NPJ digital medicine · 2024Article
- Lightweight transformers for clinical natural language processing.Natural language engineering · 2024Article
- End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.BMC medical informatics and decision making · 2024Article
- Research on named entity recognition of adverse drug reactions based on NLP and deep learning.Frontiers in pharmacology · 2023Article
- A large language model for electronic health records.NPJ digital medicine · 2022Article
- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, dosage), the reason why the drug was initially prescribed, and any adverse reaction to the drug. This paper explores the relationship between a drug and its associated attributes using relation extraction techniques. We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our experimental results demonstrate that the contextualized language model-based approach outperformed other models overall and obtain the state-of-the-art performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an F
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34457157PMC8378605What OpenQuestion holds
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.