ArticleJournal of the American Medical Informatics Association : JAMIA2021
DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. 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.
- Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation.Journal of medical Internet research · 2026Observational
- Detecting Adverse Drug Events in Social Media: A Brief Literature Review.SN computer science · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study.JMIR medical informatics · 2025Article
- Automatic genetic phenotype normalization from dysmorphology physical examinations: an overview of the BioCreative VIII-Task 3 competition.Database : the journal of biological databases and curation · 2025Review
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.The journal of prevention of Alzheimer's disease · 2025Article
- Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.JMIR medical informatics · 2024Article
- Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.Journal of medical Internet research · 2024Article
- Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.Journal of medical Internet research · 2024Article
- Embedded monitoring system and teaching of artificial intelligence online drug component recognition.Open life sciences · 2024Article
- Overview of the 8medRxiv : the preprint server for health sciences · 2023Article
- Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- Edinburgh_UCL_Health@SMM4H'22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination.Proceedings of COLING. International Conference on Computational Linguistics · 2022Article
- Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.Database : the journal of biological databases and curation · 2022Article
- COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.Briefings in bioinformatics · 2022Article
- SEED: Symptom Extraction from English Social Media Posts using Deep Learning and Transfer Learning.medRxiv : the preprint server for health sciences · 2022Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveResearch on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs. MATERIALS AND
methodsWe present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average 'natural balance' with ADEs present in about 7% of the tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all 3 tasks.
resultsThe system presented achieved state-of-the-art performance on comparable datasets and scored a classification performance of F1 = 0.63, span extraction performance of F1 = 0.44 and an end-to-end entity resolution performance of F1 = 0.34 on the presented dataset. DISCUSSION: The performance of the models continues to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements.
conclusionMining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task.
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