ArticleJournal of the American Medical Informatics Association : JAMIA2017
Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses that pooled it.
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Who cites it
70 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective.Journal of medical Internet research · 2026Article
- Review
- KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge-Graph Reinforced Deep Q-Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- The application of machine learning in the evaluation of urinary tract infections incidence in patients in a Nursing and Treatment Facility.Pharmacological reports : PR · 2026Article
- Assessing patient perceptions of off-label cannabidiol use for insomnia through sentiment analysis.Journal of cannabis research · 2025Article
- Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.Drug safety · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media.Scientific reports · 2025Article
- Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.Therapeutic advances in drug safety · 2025Review
- Transformer-based models for ADR detection: cross-drug validation and benchmarking against large language models.Therapeutic advances in drug safety · 2025Article
- Bidirectional Long Short-Term Memory-Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches.JMIR medical informatics · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Intelligent health in the IS area: A literature review and research agenda.Fundamental research · 2024Article
- Predicting Drugs Suspected of Causing Adverse Drug Reactions Using Graph Features and Attention Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2024Article
- BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.Molecules (Basel, Switzerland) · 2024Article
- Review
- A weakly supervised method for named entity recognition of Chinese electronic medical records.Medical & biological engineering & computing · 2023Article
- Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSocial media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. MATERIALS AND
methodsWe developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training.
resultsOur best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. DISCUSSION: Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models.
conclusionADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.
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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.